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RFC 4981 - Survey of Research towards Robust Peer-to-Peer Networ


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Network Working Group                                          J. Risson
Request for Comments: 4981                                      T. Moors
Category: Informational                    University of New South Wales
                                                          September 2007

       Survey of Research towards Robust Peer-to-Peer Networks:
                            Search Methods

Status of This Memo

   This memo provides information for the Internet community.  It does
   not specify an Internet standard of any kind.  Distribution of this
   memo is unlimited.

IESG Note

   This RFC is not a candidate for any level of Internet Standard.  The
   IETF disclaims any knowledge of the fitness of this RFC for any
   purpose and notes that the decision to publish is not based on IETF
   review apart from IESG review for conflict with IETF work.  The RFC
   Editor has chosen to publish this document at its discretion.  See
   RFC 3932 for more information.

Abstract

   The pace of research on peer-to-peer (P2P) networking in the last
   five years warrants a critical survey.  P2P has the makings of a
   disruptive technology -- it can aggregate enormous storage and
   processing resources while minimizing entry and scaling costs.

   Failures are common amongst massive numbers of distributed peers,
   though the impact of individual failures may be less than in
   conventional architectures.  Thus, the key to realizing P2P's
   potential in applications other than casual file sharing is
   robustness.

   P2P search methods are first couched within an overall P2P taxonomy.
   P2P indexes for simple key lookup are assessed, including those based
   on Plaxton trees, rings, tori, butterflies, de Bruijn graphs, and
   skip graphs.  Similarly, P2P indexes for keyword lookup, information
   retrieval and data management are explored.  Finally, early efforts
   to optimize range, multi-attribute, join, and aggregation queries
   over P2P indexes are reviewed.  Insofar as they are available in the
   primary literature, robustness mechanisms and metrics are highlighted
   throughout.  However, the low-level mechanisms that most affect
   robustness are not well isolated in the literature.  Recommendations
   are given for future research.

Table of Contents

   1. Introduction ....................................................3
      1.1. Related Disciplines ........................................6
      1.2. Structured and Unstructured Routing ........................7
      1.3. Indexes and Queries ........................................9
   2. Index Types ....................................................10
      2.1. Local Index (Gnutella) ....................................10
      2.2. Central Index (Napster) ...................................12
      2.3. Distributed Index (Freenet) ...............................13
   3. Semantic Free Index ............................................15
      3.1. Origins ...................................................15
           3.1.1. Plaxton, Rajaraman, and Richa (PRR) ................15
           3.1.2. Consistent Hashing .................................16
           3.1.3. Scalable Distributed Data Structures (LH*) .........16
      3.2. Dependability .............................................17
           3.2.1. Static Dependability ...............................17
           3.2.2. Dynamic Dependability ..............................18
           3.2.3. Ephemeral or Stable Nodes -- O(log n) or
                  O(1) Hops ..........................................19
           3.2.4. Simulation and Proof ...............................20
      3.3. Latency ...................................................21
           3.3.1. Hop Count and the O(1)-Hop DHTs ....................21
           3.3.2. Proximity and the O(log n)-Hop DHTs ................22
      3.4. Multicasting ..............................................23
           3.4.1. Multicasting vs. Broadcasting ......................23
           3.4.2. Motivation for DHT-based Multicasting ..............23
           3.4.3. Design Issues ......................................24
      3.5. Routing Geometries ........................................25
           3.5.1. Plaxton Trees (Pastry, Tapestry) ...................25
           3.5.2. Rings (Chord, DKS) .................................27
           3.5.3. Tori (CAN) .........................................28
           3.5.4. Butterflies (Viceroy) ..............................29
           3.5.5. de Bruijn (D2B, Koorde, Distance Halving, ODRI) ....30
           3.5.6. Skip Graphs ........................................32
   4. Semantic Index .................................................33
      4.1. Keyword Lookup ............................................34
           4.1.1. Gnutella Enhancements ..............................36
           4.1.2. Partition-by-Document, Partition-by-Keyword ........38
           4.1.3. Partial Search, Exhaustive Search ..................39
      4.2. Information Retrieval .....................................39
           4.2.1. Vector Model (PlanetP, FASD, eSearch) ..............41
           4.2.2. Latent Semantic Indexing (pSearch) .................43
           4.2.3. Small Worlds .......................................43
   5. Queries ........................................................44
      5.1. Range Queries .............................................45
      5.2. Multi-Attribute Queries ...................................48
      5.3. Join Queries ..............................................50

      5.4. Aggregation Queries .......................................50
   6. Security Considerations ........................................52
   7. Conclusions ....................................................52
   8. Acknowledgments ................................................53
   9. References .....................................................54
      9.1. Informative References ....................................54

1.  Introduction

   Peer-to-peer (P2P) networks are those that exhibit three
   characteristics: self-organization, symmetric communication, and
   distributed control [1].  A self-organizing P2P network
   "automatically adapts to the arrival, departure and failure of nodes"
   [2].  Communication is symmetric in that peers act as both clients
   and servers.  It has no centralized directory or control point.
   USENET servers and BGP peers have these traits [3] but the emphasis
   here is on the flurry of research since 2000.  Leading examples
   include Gnutella [4], Freenet [5], Pastry [2], Tapestry [6], Chord
   [7], the Content Addressable Network (CAN) [8], pSearch [9], and
   Edutella [10].  Some have suggested that peers are inherently
   unreliable [11].  Others have assumed well-connected, stable peers
   [12].

   This critical survey of P2P academic literature is warranted, given
   the intensity of recent research.  At the time of writing, one
   research database lists over 5,800 P2P publications [13].  One vendor
   surveyed P2P products and deployments [14].  There is also a tutorial
   survey of leading P2P systems [15].  DePaoli and Mariani recently
   reviewed the dependability of some early P2P systems at a high level
   [16].  The need for a critical survey was flagged in the peer-to-peer
   research group of the Internet Research Task Force (IRTF) [17].

   P2P is potentially a disruptive technology with numerous
   applications, but this potential will not be realized unless it is
   demonstrated to be robust.  A massively distributed search technique
   may yield numerous practical benefits for applications [18].  A P2P
   system has potential to be more dependable than architectures relying
   on a small number of centralized servers.  It has potential to evolve
   better from small configurations -- the capital outlays for high
   performance servers can be reduced and spread over time if a P2P
   assembly of general purpose nodes is used.  A similar argument
   motivated the deployment of distributed databases -- one thousand,
   off-the-shelf PC processors are more powerful and much less expensive
   than a large mainframe computer [19].  Storage and processing can be
   aggregated to achieve massive scale.  Wasteful partitioning between
   servers or clusters can be avoided.  As Gedik and Liu put it, if P2P
   is to find its way into applications other than casual file sharing,
   then reliability needs to be addressed [20].

   The taxonomy of Figure 1 divides the entire body of P2P research
   literature along four lines: search, storage, security, and
   applications.  This survey concentrates on search aspects.  A P2P
   search network consists of an underlying index (Sections 2 to 4) and
   queries that propagate over that index (Section 5).

   Search [18, 21-29]
      Semantic-Free Indexes [2, 6, 7, 30-52]
         Plaxton Trees
         Rings
         Tori
         Butterflies
         de Bruijn Graphs
         Skip Graphs
      Semantic Indexes [4, 53-71]
         Keyword Lookup
         Peer Information Retrieval
         Peer Data Management
      Queries [20, 22, 23, 25, 32, 38, 41, 56, 72-100]
         Range Queries
         Multi-Attribute Queries
         Join Queries
         Aggregation Queries
         Continuous Queries
         Recursive Queries
         Adaptive Queries

   Storage
      Consistency & Replication [101-112]
         Eventual consistency
         Trade-offs
      Distribution [39, 42, 90, 92, 113-131]
         Epidemics, Bloom Filters
      Fault Tolerance [40, 105, 132-139]
         Erasure Coding
         Byzantine Agreement
      Locality [24, 43, 47, 140-160]
      Load Balancing [37, 86, 100, 107, 151, 161-171]

   Security
      Character [172-182]
         Identity
         Reputation and Trust
         Incentives
      Goals [25, 27, 71, 183-197]
         Availability
         Authenticity
         Anonymity
         Access Control
         Fair Trading

   Applications [1, 198-200]
      Memory [32, 90, 142, 201-222]
         File Systems
         Web
         Content Delivery Networks
         Directories
      Service Discovery
      Publish / Subscribe ...
   Intelligence [223-228]
      GRID
      Security...
   Communication [12, 92, 119, 229-247]
      Multicasting
      Streaming Media
      Mobility
      Sensors...

            Figure 1: Classification of P2P Research Literature

   This survey is concerned with two questions.  The first, "How do P2P
   search networks work?"  This foundation is important given the pace
   and breadth of P2P research in the last five years.  In Section 2, we
   classify indexes as local, centralized and distributed.  Since
   distributed indexes are becoming dominant, they are given closer
   attention in Sections 3 and 4.  Section 3 compares distributed P2P
   indexes for simple key lookup; in particular, their origins (Section
   3.1), dependability (Section 3.2), latency (Section 3.3), and their
   support for multicast (Section 3.4).  It classifies those indexes
   according to their routing geometry (Section 3.5) -- Plaxton trees,
   rings, tori, butterflies, de Bruijn graphs and skip graphs.  Section
   4 reviews distributed P2P indexes supporting keyword lookup (Section
   4.1) and information retrieval (Section 4.2).  Section 5 probes the
   embryonic research on P2P queries; in particular, range queries
   (Section 5.1), multi-attribute queries (Section 5.2), join queries
   (Section 5.3), and aggregation queries (Section 5.4).

   The second question, "How robust are P2P search networks?"  Insofar
   as it is available in the research literature, we tease out the
   robustness mechanisms and metrics throughout Sections 2 to 5.
   Unfortunately, robustness is often more sensitive to low-level design
   choices than it is to the broad P2P index structure, yet these
   underlying design choices are seldom isolated in the primary
   literature [248].  Furthermore, there has been little consensus on
   P2P robustness metrics (Section 3.2).  Section 8 gives
   recommendations to address these important gaps.

1.1.  Related Disciplines

   Peer-to-peer research draws upon numerous distributed systems
   disciplines.  Networking researchers will recognize familiar issues
   of naming, routing, and congestion control.  P2P designs need to
   address routing and security issues across network region boundaries
   [152].  Networking research has traditionally been host-centric.  The
   Web's Universal Resource Identifiers are naturally tied to specific
   hosts, making object mobility a challenge [216].

   P2P work is data-centric [249].  P2P systems for dynamic object
   location and routing have borrowed heavily from the distributed
   systems corpus.  Some have used replication, erasure codes, and
   Byzantine agreement [111].  Others have used epidemics for durable
   peer group communication [39].

   Similarly, P2P research is set to benefit from database research
   [250].  Database researchers will recognize the need to reapply
   Codd's principle of physical data independence, that is, to decouple
   data indexes from the applications that use the data [23].  It was
   the invention of appropriate indexing mechanisms and query
   optimizations that enabled data independence.  Database indexes like
   B+ trees have an analog in P2P's distributed hash tables (DHTs).
   Wide-area, P2P query optimization is a ripe, but challenging, area
   for innovation.

   More flexible distribution of objects comes with increased security
   risks.  There are opportunities for security researchers to deliver
   new methods for availability, file authenticity, anonymity, and
   access control [25].  Proactive and reactive mechanisms are needed to
   deal with large numbers of autonomous, distributed peers.  To build
   robust systems from cooperating but self-interested peers, issues of
   identity, reputation, trust, and incentives need to be tackled.
   Although it is beyond the scope of this paper, robustness against
   malicious attacks also ought to be addressed [195].

   Possibly the largest portion of P2P research has majored on basic
   routing structures [18], where research on algorithms comes to the

   fore.  Should the overlay be "structured" or "unstructured"?  Are the
   two approaches competing or complementary?  Comparisons of the
   "structured" approaches (hypercubes, rings, toroids, butterflies, de
   Bruijn, and skip graphs) have weighed the amount of routing state per
   peer and the number of links per peer against overlay hop counts.
   While "unstructured" overlays initially used blind flooding and
   random walks, overheads usually trigger some structure, for example,
   super-peers and clusters.

   P2P applications rely on cooperation between these disciplines.
   Applications have included file sharing, directories, content
   delivery networks, email, distributed computation, publish-subscribe
   middleware, multicasting, and distributed authentication.  Which
   applications will be suited to which structures?  Are there adaptable
   mechanisms that can decouple applications from the underlying data
   structures?  What are the criteria for selection of applications
   amenable to a P2P design [1]?

   Robustness is emphasized throughout the survey.  We are particularly
   interested in two aspects.  The first, dependability, was a leading
   design goal for the original Internet [251].  It deserves the same
   status in P2P.  The measures of dependability are well established:
   reliability, a measure of the mean-time-to-failure (MTTF);
   availability, a measure of both the MTTF and the mean-time-to-repair
   (MTTR); maintainability; and safety [252].  The second aspect is the
   ability to accommodate variation in outcome, which one could call
   adaptability.  Its measures have yet to be defined.  In the context
   of the Internet, it was only recently acknowledged as a first-class
   requirement [253].  In P2P, it means planning for the tussles over
   resources and identity.  It means handling different kinds of queries
   and accommodating changeable application requirements with minimal
   intervention.  It means "organic scaling" [22], whereby the system
   grows gracefully, without a priori data center costs or architectural
   breakpoints.

   In the following section, we discuss one notable omission from the
   taxonomy of P2P networking in Figure 1 -- routing.

1.2.  Structured and Unstructured Routing

   P2P routing algorithms have been classified as "structured" or
   "unstructured".  Peers in unstructured overlay networks join by
   connecting to any existing peers [254].  In structured overlays, the
   identifier of the joining peer determines the set of peers that it
   connects to [254].  Early instantiations of Gnutella were
   unstructured -- keyword queries were flooded widely [255].  Napster
   [256] had decentralized content and a centralized index, so it only
   partially satisfies the distributed control criteria for P2P systems.

   Early structured algorithms included Plaxton, Rajaraman and Richa
   (PRR) [30], Pastry [2], Tapestry [31], Chord [7], and the Content
   Addressable Network [8].  Mishchke and Stiller recently classified
   P2P systems by the presence or absence of structure in routing tables
   and network topology [257].

   Some have cast unstructured and structured algorithms as competing
   alternatives.  Unstructured approaches have been called "first
   generation", implicitly inferior to the "second generation"
   structured algorithms [2, 31].  When generic key lookups are
   required, these structured, key-based routing schemes can guarantee
   location of a target within a bounded number of hops [23].  The
   broadcasting unstructured approaches, however, may have large routing
   costs, or fail to find available content [22].  Despite the apparent
   advantages of structured P2P, several research groups are still
   pursuing unstructured P2P.

   There have been two main criticisms of structured systems [61].  The
   first relates to peer transience, which in turn, affects robustness.
   Chawathe, et al. opined that highly transient peers are not well
   supported by DHTs [61].  P2P systems often exhibit "churn", with
   peers continually arriving and departing.  One objection to concerns
   about highly transient peers is that many applications use peers in
   well-connected parts of the network.  The Tapestry authors analyzed
   the impact of churn in a network of 1000 nodes [31].  Others opined
   that it is possible to maintain a robust DHT at relatively low cost
   [258].  Very few papers have quantitatively compared the resilience
   of structured systems.  Loguinov, Kumar, et al. claimed that there
   were only two such works [24, 36].

   The second criticism of structured systems is that they do not
   support keyword searches and complex queries as well as unstructured
   systems.  Given the current file-sharing deployments, keyword
   searches seem more important than exact-match key searches in the
   short term.  Paraphrased, "most queries are for hay, not needles"
   [61].

   More recently, some have justifiably seen unstructured and structured
   proposals as complementary, and have devised hybrid models [259].
   Their starting point was the observation that unstructured flooding
   or random walks are inefficient for data that is not highly
   replicated across the P2P network.  Structured graphs can find keys
   efficiently, irrespective of replication.  Castro, et al. proposed
   Structella, a hybrid of Gnutella built on top of Pastry [259].
   Another design used structured search for rare items and unstructured
   search for massively replicated items [54].

   However, the "structured versus unstructured routing" taxonomy is
   becoming less useful, for two reasons, Firstly, most "unstructured"
   proposals have evolved and incorporated structure.  Consider the
   classic "unstructured" system, Gnutella [4].  For scalability, its
   peers are either ultrapeers or leaf nodes.  This hierarchy is
   augmented with a query routing protocol whereby ultrapeers receive a
   hashed summary of the resource names available at leaf nodes.
   Between ultrapeers, simple query broadcast is still used, though
   methods to reduce the query load here have been considered [260].
   Secondly, there are emerging schema-based P2P designs [59], with
   super-node hierarchies and structure within documents.  These are
   quite distinct from the structured DHT proposals.

1.3.  Indexes and Queries

   Given that most, if not all, P2P designs today assume some structure,
   a more instructive taxonomy would describe the structure.  In this
   survey, we use a database taxonomy in lieu of the networking
   taxonomy, as suggested by Hellerstein, Cooper, and Garcia-Molina [23,
   261].  The structure is determined by the type of index (Sections 2 ,
   3, and 4).  Queries feature in lieu of routing (Section 5).  The DHT
   algorithms implement a "semantic-free index" [216].  They are
   oblivious of whether keys represent document titles, meta-data, or
   text.  Gnutella-like and schema-based proposals have a "semantic
   index".

   Index engineering is at the heart of P2P search methods.  It captures
   a broad range of P2P issues, as demonstrated by the Search/Index
   Links model [261].  As Manber put it, "the most important of the
   tools for information retrieval is the index -- a collection of terms
   with pointers to places where information about documents can be
   found" [262].  Sen and Wang noted that a "P2P network" usually
   consists of connections between hosts for application-layer
   signaling, rather than for the data transfer itself [263].
   Similarly, we concentrate on the "signaled" indexes and queries.

   Our focus here is the dependability and adaptability of the search
   network.  Static dependability is a measure of how well queries route
   around failures in a network that is normally fault-free.  Dynamic
   dependability gives an indication of query success when nodes and
   data are continually joining and leaving the P2P system.  An
   adaptable index accommodates change in the data and query
   distribution.  It enables data independence, in that it facilitates
   changes to the data layout without requiring changes to the
   applications that use the data [23].  An adaptable P2P system can
   support rich queries for a wide range of applications.  Some
   applications benefit from simple, semantic-free key lookups [264].
   Others require more complex, Structured Query Language (SQL)-like

   queries to find documents with multiple keywords, or to aggregate or
   join query results from distributed relations [22].

2.  Index Types

   A P2P index can be local, centralized, or distributed.  With a local
   index, a peer only keeps the references to its own data, and does not
   receive references for data at other nodes.  The very early Gnutella
   design epitomized the local index (Section 2.1).  In a centralized
   index, a single server keeps references to data on many peers.  The
   classic example is Napster (Section 2.2).  With distributed indexes,
   pointers towards the target reside at several nodes.  One very early
   example is Freenet (Section 2.3).  Distributed indexes are used in
   most P2P designs nowadays -- they dominate this survey.

   P2P indexes can also be classified as non-forwarding and forwarding.
   When queries are guided by a non-forwarding index, they jump to the
   node containing the target data in a single hop.  There have been
   semantic and semantic-free one-hop schemes [138, 265, 266].  Where
   scalability to a massive number of peers is required, these schemes
   have been extended to two hops [267, 268].  More common are the
   forwarding P2Ps, where the number of hops varies with the total
   number of peers, often logarithmically.  The related trade-offs
   between routing state, lookup latency, update bandwidth, and peer
   churn are critical to total system dependability.

2.1.  Local Index (Gnutella)

   P2Ps with a purely local data index are becoming rare.  In such
   designs, peers flood queries widely and only index their own content.
   They enable rich queries - the search is not limited to a simple key
   lookup.  However, they also generate a large volume of query traffic
   with no guarantee that a match will be found, even if it does exist
   on the network.  For example, to find potential peers on the early
   instantiations of Gnutella, 'ping' messages were broadcast over the
   P2P network and the 'pong' responses were used to build the node
   index.  Then, small 'query' messages, each with a list of keywords,
   are broadcast to peers that respond with matching filenames [4].

   There have been numerous attempts to improve the scalability of
   local-index P2P networks.  Gnutella uses fixed time-to-live (TTL)
   rings, where the query's TTL is set less than 7-10 hops [4].  Small
   TTLs reduce the network traffic and the load on peers, but also
   reduce the chances of a successful query hit.  One paper reported,
   perhaps a little too bluntly, that the fixed "TTL-based mechanism
   does not work" [67].  To address this TTL selection problem, they
   proposed an expanding ring, known elsewhere as iterative deepening
   [29].  It uses successively larger TTL counters until there is a

   match.  The flooding, ring, and expanding ring methods all increase
   network load with duplicated query messages.  A random walk, whereby
   an unduplicated query wanders about the network, does indeed reduce
   the network load but massively increases the search latency.  One
   solution is to replicate the query k times at each peer.  Called
   random k-walkers, this technique can be coupled with TTL limits, or
   periodic checks with the query originator, to cap the query load
   [67].  Adamic, Lukose, et al. suggested that the random walk searches
   be directed to nodes with a higher degree, that is, with larger
   numbers of inter-peer connections [269].  They assumed that higher-
   degree peers are also capable of higher query throughputs.  However,
   without some balancing design rule, such peers would be swamped with
   the entire P2P signaling traffic.  In addition to the above
   approaches, there is the 'directed breadth-first' algorithm [29].  It
   forwards queries within a subset of peers selected according to
   heuristics on previous performance, like the number of successful
   query results.  Another algorithm, called probabilistic flooding, has
   been modeled using percolation theory [270].

   Several measurement studies have investigated locally indexed P2Ps.
   Jovanovic noted Gnutella's power law behaviour [70].  Sen and Wang
   compared the performance of Gnutella, Fasttrack [271], and Direct
   Connect [263, 272, 273].  At the time, only Gnutella used local data
   indexes.  All three schemes now use distributed data indexes, with
   hierarchy in the form of Ultrapeers (Gnutella), Super-Nodes
   FastTrack), and Hubs (Direct Connect).  It was found that a very
   small percentage of peers have a very high degree and that the total
   system dependability is at the mercy of such peers.  While peer up-
   time and bandwidth were heavy-tailed, they did not fit well with the
   Zipf distribution.  Fortunately for Internet Service Providers,
   measures aggregated by IP prefix and Autonomous System (AS) were more
   stable than for individual IP addresses.  A study of University of
   Washington traffic found that Gnutella and Kazaa together contributed
   43% of the university's total TCP traffic [274].  They also reported
   a heavy-tailed distribution, with 600 external peers (out of 281,026)
   delivering 26% of Kazaa bytes to internal peers.  Furthermore,
   objects retrieved from the P2P network were typically three orders of
   magnitude larger than Web objects -- 300 objects contributed to
   almost half the total outbound Kazaa bandwidth.  Others reported
   Gnutella's topology mismatch, whereby only 2-5% of P2P connections
   link peers in the same Autonomous System (AS), despite over 40% of
   peers being in the top 10 ASs [65].  Together these studies
   underscore the significance of multimedia sharing applications.  They
   motivate interesting caching and locality solutions to the topology
   mismatch problem.

   These same studies bear out one main dependability lesson: total
   system dependability may be sensitive to the dependability of high-

   degree peers.  The designers of Scamp translated this observation to
   the design heuristic, "have the degree of each node be of nearly
   equal size" [153].  They analyzed a system of N peers, with mean
   degree c.log(n), where link failures occur independently with
   probability e.  If d>0 is fixed and c>(1+d)/(-log(e)), then the
   probability of graph disconnection goes to zero as N->infinity.
   Otherwise, if c<(1-d)/(-log(e)), then the probability of
   disconnection goes to one as N->infinity.  They presented a
   localizer, which finds approximate minima to a global function of
   peer degree and arbitrary link costs using only local information.
   The Scamp overlay construction algorithms could support any of the
   flooding and walking routing schemes above, or other epidemic and
   multicasting schemes for that matter.  Resilience to high churn rates
   was identified for future study.

2.2.  Central Index (Napster)

   Centralized schemes like Napster [256] are significant because they
   were the first to demonstrate the P2P scalability that comes from
   separating the data index from the data itself.  Ultimately, 36
   million Napster users lost their service not because of technical
   failure, but because the single administration was vulnerable to the
   legal challenges of record companies [275].

   There has since been little research on P2P systems with central data
   indexes.  Such systems have also been called 'hybrid' since the index
   is centralized but the data is distributed.  Yang and Garcia-Molina
   devised a four-way classification of hybrid systems [276]: unchained
   servers, where users whose index is on one server do not see other
   servers' indexes; chained servers, where the server that receives a
   query forwards it to a list of servers if it does not own the index
   itself; full replication, where all centralized servers keep a
   complete index of all available metadata; and hashing, where keywords
   are hashed to the server where the associated inverted list is kept.
   The unchained architecture was used by Napster, but it has the
   disadvantage that users do not see all indexed data in the system.
   Strictly speaking, the other three options illustrate the distributed
   data index, not the central index.  The chained architecture was
   recommended as the optimum for the music-swapping application at the
   time.  The methods by which clients update the central index were
   classified as batch or incremental, with the optimum determined by
   the query-to-login ratio.  Measurements were derived from a clone of
   Napster called OpenNap[277].  Another study of live Napster data
   reported wide variation in the availability of peers, a general
   unwillingness to share files (20-40% of peers share few or no files),
   and a common understatement of available bandwidth so as to
   discourage other peers from sharing one's link [202].

   Influenced by Napster's early demise, the P2P research community may
   have prematurely turned its back on centralized architectures.
   Chawathe, Ratnasamy, et al. opined that Google and Yahoo demonstrate
   the viability of a centralized index.  They argued that "the real
   barriers to Napster-like designs are not technical but legal and
   financial" [61].  Even this view may be a little too harsh on the
   centralized architectures -- it implies that they always have an up-
   front capital hurdle that is steeper than for distributed
   architectures.  The closer one looks at scalable 'centralized'
   architectures, the less the distinction with 'distributed'
   architectures seems to matter.  For example, it is clear that
   Google's designers consider Google a distributed, not centralized,
   file system [278].  Google demonstrates the scale and performance
   possible on commodity hardware, but still has a centralized master
   that is critical to the operation of each Google cluster.  Time may
   prove that the value of emerging P2P networks, regardless of the
   centralized-versus-distributed classification, is that they smooth
   the capital outlays and remove the single points of failure across
   the spectra of scale and geographic distribution.

2.3.  Distributed Index (Freenet)

   An important early P2P proposal for a distributed index was Freenet
   [5, 71, 279].  While its primary emphasis was the anonymity of peers,
   it did introduce a novel indexing scheme.  Files are identified by
   low-level "content-hash" keys and by "secure signed-subspace" keys,
   which ensure that only a file owner can write to a file while anyone
   can read from it.  To find a file, the requesting peer first checks
   its local table for the node with keys closest to the target.  When
   that node receives the query, it too checks for either a match or
   another node with keys close to the target.  Eventually, the query
   either finds the target or exceeds time-to-live (TTL) limits.  The
   query response traverses the successful query path in reverse,
   depositing a new routing table entry (the requested key and the data
   holder) at each peer.  The insert message similarly steps towards the
   target node, updating routing table entries as it goes, and finally
   stores the file there.  Whereas early versions of Gnutella used
   breadth-first flooding, Freenet uses a more economic depth-first
   search [280].

   An initial assessment has been done of Freenet's robustness.  It was
   shown that in a network of 1000 nodes, the median query path length
   stayed under 20 hops for a failure of 30% of nodes.  While the
   Freenet designers considered this as evidence that the system is
   "surprisingly robust against quite large failures" [71], the same
   datapoint may well be outside meaningful operating bounds.  How many
   applications are useful when the first quartile of queries have path
   lengths of several hundred hops in a network of only 1000 nodes, per

   Figure 4 of [71]?  To date, there has been no analysis of Freenet's
   dynamic robustness.  For example, how does it perform when nodes are
   continually arriving and departing?

   There have been both criticisms and extensions of the early Freenet
   work.  Gnutella proponents acknowledged the merit in Freenet's
   avoidance of query broadcasting [281].  However, they are critical on
   two counts: the exact file name is needed to construct a query; and
   exactly one match is returned for each query.  P2P designs using
   DHTs, per Section 3, share similar characteristics -- a precise query
   yields a precise response.  The similarity is not surprising since
   Freenet also uses a hash function to generate keys.  However, the
   query routing used in the DHTs has firmer theoretical foundations.
   Another difference with DHTs is that Freenet will take time, when a
   new node joins the network, to build an index that facilitates
   efficient query routing.  By the inventor's own admission, this is
   damaging for a user's first impressions [282].  It was proposed to
   download a copy of routing tables from seed nodes at startup, even
   though the new node might be far from the seed node.  Freenet's slow
   startup motivated Mache, Gilbert, et al. to amend the overlay after
   failed requests and to place additional index entries on successful
   requests -- they claim almost an order of magnitude reduction in
   average query path length [280].  Clarke also highlighted the lack of
   locality or bandwidth information available for efficient query
   routing decisions [282].  He proposed that each node gather response
   times, connection times, and proportion of successful requests for
   each entry in the query routing table.  When searching for a key that
   is not in its own routing table, it was proposed to estimate response
   times from the routing metrics for the nearest known keys and
   consequently choose the node that can retrieve the data fastest.  The
   response time heuristic assumed that nodes close in the key space
   have similar response times.  This assumption stemmed from early
   deployment observations that Freenet peers seemed to specialize in
   parts of the keyspace -- it has not been justified analytically.
   Kronfol drew attention to Freenet's inability to do keyword searches
   [283].  He suggested that peers cache lists of weighted keywords in
   order to route queries to documents, using Term Frequency Inverse
   Document Frequency (TFIDF) measures and inverted indexes (Section
   4.2.1).  With these methods, a peer can route queries for simple
   keyword lists or more complicated conjunctions and disjunctions of
   keywords.  Robustness analysis and simulation of Kronfol's proposal
   remain open.

   The vast majority of P2P proposals in following sections rely on a
   distributed index.

3.  Semantic Free Index

   Many of today's distributed network indexes are semantic.  The
   semantic index is human-readable.  For example, it might associate
   information with other keywords, a document, a database key, or even
   an administrative domain.  It makes it easy to associate objects with
   particular network providers, companies, or organizations, as
   evidenced in the Domain Name System (DNS).  However, it can also
   trigger legal tussles and frustrate content replication and migration
   [216].

   Distributed Hash Tables (DHTs) have been proposed to provide
   semantic-free, data-centric references.  DHTs enable one to find an
   object's persistent key in a very large, changing set of hosts.  They
   are typically designed for [23]:

   a) low degree.  If each node keeps routing information for only a
      small number of other nodes, the impact of high node arrival and
      departure rates is contained;

   b) low hop count.  The hops and delay introduced by the extra
      indirection are minimized;

   c) greedy routing.  Nodes independently calculate a short path to the
      target.  At each hop, the query moves closer to the target; and

   d) robustness.  A path to the target can be found even when links or
      nodes fail.

3.1.  Origins

   To understand the origins of recent DHTs, one needs to look to three
   contributions from the 1990s.  The first two -- Plaxton, Rajaraman,
   and Richa (PRR) [30] and Consistent Hashing [49] -- were published
   within one month of each other.  The third, the Scalable Distributed
   Data Structure (SDDS) [52], was curiously ignored in significant
   structured P2P designs despite having some similar goals [2, 6, 7].
   It has been briefly referenced in other P2P papers [46, 284-287].

3.1.1.  Plaxton, Rajaraman, and Richa (PRR)

   PRR is the most recent of the three.  It influenced the designs of
   Pastry [2], Tapestry [6], and Chord [7].  The value of PRR is that it
   can locate objects using fixed-length routing tables [6].  Objects
   and nodes are assigned a semantic-free address, for example a 160-bit
   key.  Every node is effectively the root of a spanning tree.  A
   message routes toward an object by matching longer address suffixes,
   until it encounters either the object's root node or another node

   with a 'nearby' copy.  It can route around link and node failure by
   matching nodes with a related suffix.  The scheme has several
   disadvantages [6]: global knowledge is needed to construct the
   overlay; an object's root node is a single point of failure; nodes
   cannot be inserted and deleted; and there is no mechanism for queries
   to avoid congestion hot spots.

3.1.2.  Consistent Hashing

   Consistent Hashing [288] strongly influenced the designs of Chord [7]
   and Koorde [37].  Karger, et al. introduced Consistent Hashing in the
   context of the Web-caching problem [49].  Web servers could
   conceivably use standard hashing to place objects across a network of
   caches.  Clients could use the approach to find the objects.  For
   normal hashing, most object references would be moved when caches are
   added or deleted.  On the other hand, Consistent Hashing is "smooth"
   -- when caches are added or deleted, the minimum number of object
   references move so as to maintain load balancing.  Consistent Hashing
   also ensures that the total number of caches responsible for a
   particular object is limited.  Whereas Litwin's Linear Hashing (LH*)
   scheme requires 'buckets' to be added one at a time in sequence [50],
   Consistent Hashing allows them to be added in any order [49].  There
   is an open Consistent Hashing problem pertaining to the fraction of
   items moved when a node is inserted [165].  Extended Consistent
   Hashing was recently proposed to randomize queries over the spread of
   caches to significantly reduce the load variance [289].
   Interestingly, Karger [49] referred to an older DHT algorithm by
   Devine that used "a novel autonomous location discovery algorithm
   that learns the buckets' locations instead of using a centralized
   directory" [51].

3.1.3.  Scalable Distributed Data Structures (LH*)

   In turn, Devine's primary point of reference was Litwin's work on
   SDDSs and the associated LH* algorithm [52].  An SDDS satisfies three
   design requirements: files grow to new servers only when existing
   servers are well loaded; there is no centralized directory; and the
   basic operations like insert, search, and split never require atomic
   updates to multiple clients.  Honicky and Miller suggested the first
   requirement could be considered a limitation since expansion to new
   servers is not under administrative control [286].  Litwin recently
   noted numerous similarities and differences between LH* and Chord
   [290].  He found that both implement key search.  Although LH* refers
   to clients and servers, nodes can operate as peers in both.  Chord
   'splits' nodes when a new node is inserted, while LH* schedules
   'splits' to avoid overload.  Chord requests travel O(log n) hops,
   while LH* client requests need, at most, two hops to find the target.
   Chord stores a small number of 'fingers' at each node.  LH* servers

   store N/2 to N addresses while LH* clients store 1 to N addresses.
   This trade-off between hop count and the size of the index affects
   system robustness, and bears striking similarity to recent one- and
   two-hop P2P schemes in Section 2.  The arrival and departure of LH*
   clients does not disrupt LH* server metadata at all.  Given the size
   of the index, the arrival and departure of LH* servers are likely to
   cause more churn than that of Chord nodes.  Unlike Chord, LH* has a
   single point of failure, the split coordinator.  It can be
   replicated.  Alternatively, it can be removed in later LH* variants,
   though details have not been progressed for lack of practical need
   [290].

3.2.  Dependability

   We make four overall observations about their dependability.
   Dependability metrics fall into two categories: static dependability,
   a measure of performance before recovery mechanisms take over; and
   dynamic dependability, for the most likely case in massive networks
   where there is continual failure and recovery ("churn").

3.2.1.  Static Dependability

   Observation A: Static dependability comparisons show that no O(log n)
   DHT geometry is significantly more dependable than the other O(log n)
   geometries.

   Gummadi, et al. compared the tree, hypercube, butterfly, ring, XOR,
   and hybrid geometries.  In such geometries, nodes generally know
   about O(log n) neighbors and route to a destination in O(log n) hops,
   where N is the number of nodes in the overlay.  Gummadi, et al. asked
   "Why not the ring?"  They concluded that only the ring and XOR
   geometries permit flexible choice of both neighbors and alternative
   routes [24].  Loguinov, et al. added the de Bruijn graph to their
   comparison [36].  They concluded that the classical analyses, for
   example the probability that a particular node becomes disconnected,
   yield no major differences between the resilience of Chord, CAN, and
   de Bruijn graphs.  Using bisection width (the minimum edge count
   between two equal partitions) and path overlap (the likelihood that
   backup paths will encounter the same failed nodes or links as the
   primary path), they argued for the superior resilience of the de
   Bruijn graph.  In short, ring, XOR, and de Bruijn graphs all permit
   flexible choice of alternative paths, but only in de Bruijn are the
   alternate paths independent of each other [36].

3.2.2.  Dynamic Dependability

   Observation B: Dynamic dependability comparisons show that DHT
   dependability is sensitive to the underlying topology maintenance
   algorithms.

   Li, et al. give the best comparison to date of several leading DHTs
   during churn [291].  They relate the disparate configuration
   parameters of Tapestry, Chord, Kademlia, Kelips, and OneHop to
   fundamental design choices.  For each of these DHTs, they plotted the
   optimal performance in terms of lookup latency (milliseconds) and
   fraction of failed lookups.  The results led to several important
   insights about the underlying algorithms, for example: increasing
   routing table size is more cost-effective than increasing the rate of
   periodic stabilization; learning about new nodes during the lookup
   process sometimes eliminates the need for stabilization; and parallel
   lookups reduce latency due to timeouts more effectively than faster
   stabilization.  Similarly, Zhuang, et al. compared keep-alive
   algorithms for DHT failure detection [292].  Such algorithmic
   comparisons can significantly improve the dependability of DHT
   designs.

   In Figure 2, we propose a taxonomy for the topology maintenance
   algorithms that influence dependability.  The algorithms can be
   classified by how nodes join and leave, how they first detect
   failures, how they share information about topology updates, and how
   they react when they receive information about topology updates.

   Normal Updates
      Joins (passive; active) [293]
      Leaves (passive; active) [293]

   Fault Detection [292]
      Maintenance
         Proactive (periodic or keep-alive probes)
         Reactive (correction-on-use, correction-on-failure) [294]
      Report
         Negative (all dead nodes, nodes recently failed)
         Positive (all live nodes; nodes recently recovered) [292]

   Topology Sharing: yes/ no [292]
         Multicast Tree (explicit, implicit) [267, 295]
         Gossip (timeouts; number of contacts) [39]

   Corrective Action
      Routing
         Rerouting actions
            (reroute once; route in parallel [291]; reject)
         Routing timeouts
            (TCP-style, virtual coordinates) [296]
      Topology
         Update action (evict/ replace/ tag node)
         Update timeliness (immediate, periodic[296], delayed [297])

        Figure 2: Topology Maintenance in Distributed Hash Tables

3.2.3.  Ephemeral or Stable Nodes -- O(log n) or O(1) Hops

   Observation C: Most DHTs use O(log n) geometries to suit ephemeral
   nodes.  The O(1) hop DHTs suit stable nodes and deserve more research
   attention.

   Most of the DHTs in Section 3.5 assume that nodes are ephemeral, with
   expected lifetimes of one to two hours.  Therefore, they mostly use
   an O(log n) geometry.  The common assumption is that maintenance of
   full routing tables in the O(1) hop DHTs will consume excessive
   bandwidth when nodes are continually joining and leaving.  The
   corollary is that, when they run on stable infrastructure servers
   [298], most of the DHTs in Section 3.5 are less than optimal --
   lookups take many more hops than necessary, wasting latency and
   bandwidth budgets.  The O(1) hop DHTs suit stable deployments and
   high lookup rates.  For a churning 1024-node network, Li, et al.
   concluded that OneHop is superior to Chord, Tapestry, Kademlia, and
   Kelips in terms of latency and lookup success rate [291].  For a
   3000-node network, they concluded that "OneHop is only preferable to
   Chord when the deployment scenario allows a communication cost

   greater than 20 bytes per node per second" [291].  This apparent
   limitation needs to be put in context.  They assumed that each node
   issues only one lookup every 10 minutes and has a lifetime of only 60
   minutes.  It seems reasonable to expect that in some deployments,
   nodes will have a lifetime of weeks or more, a maintenance bandwidth
   of tens of kilobits per second, and a load of hundreds of lookups per
   second.  O(1) hop DHTs are superior in such situations.  OneHop can
   scale at least to many tens of thousands of nodes [267].  The recent
   O(1) hop designs [267, 295] are vastly outnumbered by the O(log n)
   DHTs in Section 3.5.  Research on the algorithms of Figure 2 will
   also yield improvements in the dependability of the O(1) hop DHTs.

3.2.4.  Simulation and Proof

   Observation D: Although not yet a mature science, the study of DHT
   dependability is helped by recent simulation and formal development
   tools.

   While there are recent reference architectures [294, 298], much of
   the DHT literature in Section 3.5 does not lend itself to repeatable,
   comparative studies.  The best comparative work to date [291] relies
   on the Peer-to-Peer Simulator (P2PSIM) [299].  At the time of
   writing, it supports more DHT geometries than any other simulator.
   As the study of DHTs matures, we can expect to see the simulation
   emphasis shift from geometric comparison to a comparison of the
   algorithms of Figure 2.

   P2P correctness proofs generally rely on less-than-complete formal
   specifications of system invariants and events [7, 45, 300].  Li and
   Plaxton expressed concern that "when many joins and leaves happen
   concurrently, it is not clear whether the neighbor tables will remain
   in a 'good' state" [47].  While acknowledging that guaranteeing
   consistency in a failure-prone network is impossible, Lynch, Malkhi,
   et al. sketched amendments to the Chord algorithm to guarantee
   atomicity [301].  More recently, Gilbert, Lynch, et al. gave a new
   algorithm for atomic read/write memory in a churning distributed
   network, suggesting it to be a good match for P2P [302].  Lynch and
   Stoica show in an enhancement to Chord that lookups are provably
   correct when there is a limited rate of joins and failures [303].
   Fault Tolerant Active Rings is a protocol for active joins and leaves
   that was formally specified and proven using B-method tools [304].  A
   good starting point for a formal DHT development would be the
   numerous informal API specifications [22, 305, 306].  Such work could
   be informed by other efforts to formally specify routing invariants
   [307, 308].

3.3.  Latency

   The key metrics for DHT latency are:

   1) Shortest-Path Distance and Diameter.  In graph theory, the
      shortest-path distance is the minimum number of edges in any path
      between two vertices of the graph.  Diameter is the largest of all
      shortest-path distances in a graph [309].  Networking synonyms for
      distance on a DHT are "hop count" and "lookup length".

   2) Latency and Latency Stretch.  Two types of latency are relevant
      here -- network-layer latency and overlay latency.  Network-layer
      latency has been referred to as "proximity" or "locality" [24].
      Stretch is the cost of an overlay path between two nodes, divided
      by the cost of the direct network path between those nodes [310].
      Latency stretch is also known as the "relative delay penalty"
      [311].

3.3.1.  Hop Count and the O(1)-Hop DHTs

   Hop count gives an approximate indication of path latency.  O(1)-hop
   DHTs have path latencies lower than the O(log n)-hop DHTs [291].
   This significant advantage is often overlooked on account of concern
   about the messaging costs to maintain large routing tables (Section
   3.2.3).  Such concern is justified when the mean node lifetime is
   only a few hours and the mean lookup interval per node is more than a
   few seconds (the classic profile of a P2P file-sharing node).
   However, for a large, practical operating range (node lifetimes of
   days or more, lookup rates of over tens of lookups per second per
   node, up to ~100,000 nodes), the total messaging cost in O(1) hop
   DHTs is lower than in O(log n) DHTs [312].  Lookups and routing table
   maintenance contribute to the total messaging cost.  If a deployment
   fits this operating range, then O(1)-hop DHTs will give lower path
   latencies and lower total messaging costs.  An additional merit of
   the O(1)-hop DHTs is that they yield lower lookup failure rates than
   their O(log N)-hop counterparts [291].

   Low hop count can be achieved in two ways: each node has a large O(N)
   index of nodes; or the object references can be replicated on many
   nodes.  Beehive [313], Kelips [39], LAND [310], and Tulip [314] are
   examples of the latter category.  Beehive achieves O(1) hops on
   average and O(log n) hops in the worst case, by proactive replication
   of popular objects.  Kelips replicates the 'file index'.  It incurs
   O(sqrt(N)) storage costs for both the node index and the file index.
   LAND uses O(log n) reference pointers for each stored object and an
   O(log n) index to achieve a worst-case 1+e stretch, where 0<e.  The
   Kelips-like Tulip [314] requires 2 hops per lookup.  Each node

   maintains 2sqrt(N)log(N) links to other nodes and objects are
   replicated on O(sqrt(N)) nodes.

   The DHTs with a large O(N) node index can be divided into two groups:
   those for which the index is always O(N); and those for which the
   index opportunistically ranges from O(log n) to O(N).  Linear Hashing
   (LH*) servers [52], OneHop [267], and 1h-Calot [295] fall into the
   former category.  EpiChord [315] and Accordion [316] are examples of
   the latter.

3.3.2.  Proximity and the O(log n)-Hop DHTs

   If one chooses not to use single-hop DHTs, hop count is a weak
   indicator of end-to-end path latency.  Some hops may incur large
   delays because of intercontinental or satellite links.  Consequently,
   numerous DHT designs minimize path latency by considering the
   proximity of nodes.  Gummadi, et al. classified the proximity methods
   as follows [24]:

   1) Proximity Neighbor Selection (PNS).  The nodes in the routing
      table are chosen based on the latency of the direct hop to those
      nodes.  The latency may be explicitly measured [317], or it may be
      estimated using one of several synthetic coordinate systems [150,
      154, 318].  As a lower bound on PNS performance, Dabek, et al.
      showed that lookups on O(log n) DHTs take at least 1.5 times the
      average roundtrip time of the underlying network [154].

   2) Proximity Route Selection (PRS).  At lookup time, the choice of
      the next-hop node relies on the latency of the direct hop to that
      node.  PRS is less effective than PNS, though it may complement it
      [24].  Some of the routing geometries in Section 3.5 do not
      support PNS and/or PRS [24].

   3) Proximity Identifier Selection (PIS).  Node identifiers indicate
      geographic position.  PIS frustrates load balancing, increases the
      risk of correlated failures, and is not often used [24].

   The proximity study by Gummadi, et al. assumed recursive routing,
   though they suggested that PNS would also be superior to PRS with
   iterative routing [24].  Dabek, et al. found that recursive lookups
   take 0.6 times as long as iterative lookups [150].

   Beyond the explicit use of proximity information, redundancy can help
   to avoid slow paths and servers.  One may increase the number of
   replicas [150], use parallel lookups [291, 316], use alternate routes
   on failure [150], or use multiple gateway nodes to enter the DHT
   [317].

3.4.  Multicasting

3.4.1.  Multicasting vs. Broadcasting

   "Multicasting" here means sending a message to a subset of an
   overlay's nodes.  Nodes explicitly join and leave this subset, called
   a "multicast group".  "Broadcasting" here is a special case of
   multicasting in which a message is sent to all nodes in the overlay.
   Broadcasting relies on overlay membership messages -- it does not
   need extra group membership messaging.  Castro, et al. said
   multicasting on structured overlays is either "flooding" (one overlay
   per group) or "tree-based" (one tree per group) [319].  These are
   synonyms for broadcasting and multicasting respectively.

   The first DHT-based designs for multicasting were CAN multicast
   [320], Scribe [241], Bayeux [242], and i3 [231].  They were based on
   CAN [8], Pastry [2], Tapestry [31], and Chord [7] respectively.  El-
   Ansary, et al. devised the first DHT-based broadcasting scheme [321].
   It was based on Chord.

   Multicast trees can be constructed using reverse-path forwarding or
   forward-path forwarding.  Scribe uses reverse-path forwarding [241].
   Bayeux uses forward-path forwarding [242].  Borg, a multicast design
   based on Pastry, uses a combination of forward-path and reverse-path
   forwarding to minimize latency [237].

3.4.2.  Motivation for DHT-based Multicasting

   Multicasting complements DHT search capability.  DHTs naturally
   support exact match queries.  With multicasting, they can support
   more complex queries.  Multicasting also enables the dissemination
   and collection of global information.

   Consider, for example, aggregation queries like minimum, maximum,
   count, sum, and average (Section 5.4).  A node at the root of a
   dissemination tree might multicast such a query [322].  The leaf
   nodes return local results towards the root node.  Successive parents
   aggregate the result so that eventually the root node can compute the
   global result.  Such queries may help to monitor the capacity and
   health of the overlay itself.

   Why bother with structured overlays for multicasting?  In Section
   2.1, we saw that Gnutella can multicast complex queries without them
   [4].  Castro, et al. posed the question, "Should we build Gnutella on
   a structured overlay?" [259].  While acknowledging that their study
   was preliminary, they did conclude that "we see no reason to build
   Gnutella on top of an unstructured overlay" [259].  The supposedly
   high maintenance costs of structured overlays were outweighed by

   query cost savings.  The structured overlay ensured that nodes were
   only visited once during a complex query.  It also helped to
   accurately limit the total number of nodes visited.  Pai, et al.
   acknowledged that multicast trees based on structured overlays
   contribute to simple routing rules, low delay and low delay variation
   [323].  However, they opted for unstructured, gossip-based
   multicasting for reliability reasons: data loss near the tree root
   affects all subtended nodes; interior node failures must be repaired
   quickly; interior nodes are obliged to disseminate more than their
   fair share of traffic, giving leaf nodes a "free ride".  The most
   promising research direction is to improve on the Bimodal
   Multicasting approach [324].  It combines the bandwidth efficiency
   and low latency of structured, best-effort multicasting trees with
   the reliability of unstructured gossip protocols.

3.4.3.  Design Issues

   None of the early structured overlay multicast designs addressed all
   of the following issues [325]:

   1) Heterogeneous Node Capacity.  Nodes differ in their processing,
      memory, and network capacity.  Multicast throughput is largely
      determined by the node with smallest throughput [325].  To limit
      the multicasting load on a node, one might cap its out-degree.  If
      the same node receives further join requests, it refers them to
      its children ("pushdown") [240].  Bharambe, et al. explored
      several pushdown strategies but found them inadequate to deal with
      heterogeneity [326].  They concluded that the heterogeneity issue
      remains open, and should be addressed before deploying DHTs for
      high-bandwidth multicasting applications.  Independently, Zhang et
      al. partially tackled heterogeneity by allowing nodes in their
      CAM-Chord and CAM-Koorde designs to vary out-degree according to
      the node's capacity [325].  However, they made no mention of the
      "pushdown" issue -- they did not describe topology maintenance
      when the out-degree limit is reached.

   2) Reliability (Dynamic Membership).  If a multicast tree is to be
      resilient, it must survive dynamic membership.  There are several
      ways to deal with dynamic membership: ensure that the root node of
      the multicasting tree does not handle all requests to join or
      leave the multicast group [242]; use multiple interior-node-
      disjoint trees to avoid single points of failure in tree
      structures [322]; and split the root node into several replicas
      and partition members across them [241].  For example, Bayeux
      requires the root node to track all group membership changes
      whereas Scribe does not [241].  CAN-multicast uses a single,
      well-known host to bootstrap the join operations [320].  The
      earliest DHT-based broadcasting work by El-Ansary, et al. did not

      address the issue of dynamic membership [321].  Ghodsi, et al.
      addressed it in a subsequent paper, though, giving two broadcast
      algorithms that accommodate routing table inconsistencies [327].
      One algorithm achieves a more optimal multicasting network at the
      expense of greater correction overhead.  Splitstream, based on
      Scribe and Pastry, redundantly striped content across multiple
      interior-node-disjoint multicast trees -- if one interior node
      fails, then only one stripe is lost [240].

   3) Large Any-Source Multicast Groups.  Any group member should be
      allowed to send multicast messages.  The group should scale to a
      very large number of hosts.  CAN-based multicast was the first
      application-level multicast scheme to scale to groups of several
      thousands of nodes without restricting the service model to a
      single source [320].  Bayeux scales to large groups but has a
      single root node for each multicast group.  It supports the any-
      source model only by having the root node operate as a reflector
      for multiple senders [242].

3.5.  Routing Geometries

   In Sections 3.5.1 to 3.5.6, we introduce the main geometries for
   simple key lookup and survey their robustness mechanisms.

3.5.1.  Plaxton Trees (Pastry, Tapestry)

   Work began in March 2000 on a structured, fault-tolerant, wide-area
   Dynamic Object Location and Routing (DOLR) system called Tapestry [6,
   155].  While DHTs fix replica locations, a DOLR API enables
   applications to control object placement [31].  Tapestry's basic
   location and routing scheme follows Plaxton, Rajaraman, and Richa
   (PRR) [30], but it remedies PRR's robustness shortcomings described
   in Section 3.1.  Whereas each object has one root node in PRR,
   Tapestry uses several to avoid a single point of failure.  Unlike
   PRR, it allows nodes to be inserted and deleted.  Whereas PRR
   required a total ordering of nodes, Tapestry uses 'surrogate routing'
   to incrementally choose root nodes.  The PRR algorithm does not
   address congestion, but Tapestry can put object copies close to nodes
   generating high query loads.  PRR nodes only know of the nearest
   replica, whereas Tapestry nodes enable selection from a set of
   replicas (for example, to retrieve the most up to date).  To detect
   routing faults, Tapestry uses TCP timeouts and UDP heartbeats for
   detection, sequential secondary neighbours for rerouting, and a
   'second chance' window so that recovery can occur without the
   overhead of a full node insertion.  Tapestry's dependability has been
   measured on a testbed of about 100 machines and on simulations of

   about 1000 nodes.  Successful routing rates and maintenance
   bandwidths were measured during instantaneous failures and ongoing
   churn [31].

   Pastry, like Tapestry, uses Plaxton-like prefix routing [2].  As in
   Tapestry, Pastry nodes maintain O(log n) neighbours and route to a
   target in O(log n) hops.  Pastry differs from Tapestry only in the
   method by which it handles network locality and replication [2].
   Each Pastry node maintains a 'leaf set' and a 'routing table'.  The
   leaf set contains l/2 node IDs on either side of the local node ID in
   the node ID space.  The routing table, in row r, column c, points to
   the node ID with the same r-digit prefix as the local node, but with
   an r+1 digit of c.  A Pastry node periodically probes leaf set and
   routing table nodes, with periodicity of Tls and Trt and a timeout
   Tout.  Mahajan, Castry, et al. analyzed the reliability versus
   maintenance cost trade-offs in terms of the parameters l, Tls, Trt,
   and Tout [328].  They concluded that earlier concerns about excessive
   maintenance cost in a churning P2P network were unfounded, but
   suggested follow-up work for a wider range of reliability targets,
   maintenance costs, and probe periods.  Rhea Geels, et al. concluded
   that existing DHTs fail at high churn rates [329].  Building on a
   Pastry implementation from Rice University, they found that most
   lookups fail to complete when there is excessive churn.  They
   conjectured that short-lived nodes often leave the network with
   lookups that have not yet timed out, but no evidence was provided to
   confirm the theory.  They identified three design issues that affect
   DHT performance under churn: reactive versus periodic recovery of
   peers; lookup timeouts; and choice of nearby neighbours.  Since
   reactive recovery was found to add traffic to already congested
   links, the authors used periodic recovery in their design.  For
   lookup timeouts, they advocated an exponentially weighted moving
   average of each neighbour's response time, over alternative fixed
   timeout or 'virtual coordinate' schemes.  For selection of nearby
   neighbours, they found that 'global sampling' was more effective than
   simply sampling a 'neighbour's neighbours' or 'inverse neighbours'.
   Castro, Costa, et al. have refuted the suggestion that DHTs cannot
   cope with high churn rates [330].  By implementing methods for
   continuous detection and repair, their MSPastry implementation
   achieved shorter routing paths and a maintenance overhead of less
   than half a message per second per node.

   There have been more recent proposals based on these early Plaxton-
   like schemes.  Kademlia uses a bit-wise exclusive or (XOR) metric for
   the 'distance' between 160-bit node identifiers [45].  Each node
   keeps a list of contact nodes for each section of the node space that
   is between 2^i and 2^(i+1) from itself (0.i<160).  Longer-lived nodes
   are deliberately given preference on this list -- it has been found
   in Gnutella that the longer a node has been active, the more likely

   it is to remain active.  Like Kademlia, Willow uses the XOR metric
   [32].  It implements a Tree Maintenance Protocol to 'zipper' together
   broken segments of a tree.  Where other schemes use DHT routing to
   inefficiently add new peers, Willow can merge disjoint or broken
   trees in O(log n) parallel operations.

3.5.2.  Rings (Chord, DKS)

   Chord is the prototypical DHT ring, so we first sketch its operation.
   Chord maps nodes and keys to an identifier ring [7, 34].  Chord
   supports one main operation: find a node with the given key.  It uses
   Consistent Hashing (Section 3.1) to minimize disruption of keys when
   nodes join and leave the network.  However, Chord peers need only
   track O(log n) other peers, not all peers as in the original
   consistent hashing proposal [49].  It enables concurrent node
   insertions and deletions, improving on PRR.  Compared to Pastry, it
   has a simpler join protocol.  Each Chord peer tracks its predecessor,
   a list of successors, and a finger table.  Using the finger table,
   each hop is at least half the remaining distance around the ring to
   the target node, giving an average lookup hop count of (1/2)log
   n(base 2).  Each Chord node runs a periodic stabilization routine
   that updates predecessor and successor pointers to cater to newly
   added nodes.  All successors of a given node need to fail for the
   ring to fail.  Although a node departure could be treated the same as
   a failure, a departing Chord node first notifies the predecessor and
   successors, so as to improve performance.

   In their definitive paper, Chord's inventors critiqued its
   dependability under churn [34].  They provided proofs on the
   behaviour of the Chord network when nodes in a stable network fail,
   stressing that such proofs are inadequate in the general case of a
   perpetually churning network.  An earlier paper had posed the
   question, "For lookups to be successful during churn, how regularly
   do the Chord stabilization routines need to run?" [331].  Stoica,
   Morris, et al. modeled a range of node join/departure rates and
   stabilization periods for a Chord network of 1000 nodes.  They
   measured the number of timeouts (caused by a finger pointing to a
   departed node) and lookup failures (caused by nodes that temporarily
   point to the wrong successor during churn).  They also modeled the
   'lookup stretch', the ratio of the Chord lookup time to optimal
   lookup time on the underlying network.  They demonstrated the latency
   advantage of recursive lookups over iterative lookups, but there
   remains room for delay reduction.  For further work, the authors
   proposed to improve resilience to network partitions, using a small
   set of known nodes or 'remembered' random nodes.  To reduce the
   number of messages per lookup, they suggested an increase in the size
   of each step around the ring, accomplished via a larger number of
   fingers at each node.  Much of the paper assumed independent, equally

   likely node failures.  Analysis of correlated node failures, caused
   by massive site or backbone failures, will be more important in some
   deployments.  The paper did not attempt to recommend a fixed optimal
   stabilization rate.  Liben-Nowell, Balakrishnan, et al. had suggested
   that optimum stabilization rate might evolve according to
   measurements of peers' behaviour [331] -- such a mechanism has yet to
   be devised.

   Alima, El-Ansary, et al. considered the communication costs of
   Chord's stabilization routines, referred to as 'active correction',
   to be excessive [332].  Two other robustness issues also motivated
   their Distributed K-ary Search (DKS) design, which is similar to
   Chord.  Firstly, the total system should evolve for an optimum
   balance between the number of peers, the lookup hop count, and the
   size of the routing table.  Secondly, lookups should be reliable --
   P2P algorithms should be able to guarantee a successful lookup for
   key/value pairs that have been inserted into the system.  A similar
   lookup-correctness issue was raised elsewhere by one of Chord's
   authors; "Is it possible to augment the data structure to work even
   when nodes (and their associated finger lists) just disappear?" [333]
   Alima, El-Ansary, et al. asserted that P2Ps using active correction,
   like Chord, Pastry, and Tapestry, are unable to give such a
   guarantee.  They propose an alternate 'correction-on-use' scheme,
   whereby expired routing entries are corrected by information
   piggybacking lookups and insertions.  A prerequisite is that lookup
   and insertion rates are significantly higher than node arrival,
   departure, and failure rates.  Correct lookups are guaranteed in the
   presence of simultaneous node arrivals or up to f concurrent node
   departures, where f is configurable.

3.5.3.  Tori (CAN)

   Ratnasamy, Francis, et al. developed the Content-Addressable Network
   (CAN), another early DHT widely referenced alongside Tapestry,
   Pastry, and Chord [8, 334].  It is arranged as a virtual
   d-dimensional Cartesian coordinate space on a d-torus.  Each node is
   responsible for a zone in this coordinate space.  The designers used
   a heuristic thought to be important for large, churning P2P networks:
   keep the number of neighbours independent of system size.
   Consequently, its design differs significantly from Pastry, Tapestry,
   and Chord.  Whereas they have O(log n) neighbours per node and O(log
   n) hops per lookup, CAN has O(d) neighbours and O(dn^(1/d)) hop
   count.  When CAN's system-wide parameter d is set to log(n), CAN
   converges to their profile.  If the number of nodes grows, a major
   rearrangement of the CAN network may be required [151].  The CAN
   designers considered building on PRR, but opted for the simple, low-
   state-per-node CAN algorithm instead.  They had reasoned that a PRR-
   based design would not perform well under churn, given node

   departures and arrivals would affect a logarithmic number of nodes
   [8].

   There have been preliminary assessments of CAN's resilience.  When a
   node leaves the CAN in an orderly fashion, it passes its own Virtual
   ID (VID), its neighbours' VIDs and IP addresses, and its key/value
   pairs to a takeover node.  If a node leaves abruptly, its neighbours
   send recovery messages towards the designated takeover node.  CAN
   ensures the recovery messages reach the takeover node, even if nodes
   die simultaneously, by maintaining a VID chain with Chord's
   stabilization algorithm.  Some initial 'proof of concept' resilience
   simulations were run using the Network Simulator (NS) [335] for up to
   a few hundred nodes.  Average hop counts and lookup failure
   probabilities were plotted against the total number of nodes for
   various node failure rates [8].  The CAN team documented several open
   research questions pertaining to state/hop count trade-offs,
   resilience, load, locality, and heterogeneous peers [44, 334].

3.5.4.  Butterflies (Viceroy)

   Viceroy approximates a butterfly network [46].  It generally has
   constant degree like CAN.  Like Chord, Tapestry, and Pastry, it has
   logarithmic diameter.  It improves on these systems, inasmuch as its
   diameter is better than CAN and its degree is better than Chord,
   Tapestry, and Pastry.  As with most DHTs, it utilizes Consistent
   Hashing.  When a peer joins the Viceroy network, it takes a random
   but permanent 'identity' and selects its 'level' within the network.
   Each peer maintains general ring pointers ('predecessor' and
   'successor'), level ring pointers ('nextonlevel' and 'prevonlevel'),
   and butterfly pointers ('left', 'right', and 'up').  When a peer
   departs, it normally passes its key pairs to a successor, and
   notifies other peers to find a replacement peer.

   The Viceroy paper scoped out the issue of robustness.  It explicitly
   assumed that peers do not fail [46].  It assumed that join and leave
   operations do not overlap, so as to avoid the complication of
   concurrency mechanisms like locking.  Kaashoek and Karger were
   somewhat critical of Viceroy's complexity [37].  They also pointed to
   its fault-tolerance blind spot.  Li and Plaxton suggested that such
   constant-degree algorithms deserve further consideration [47].  They
   offered several pros and cons.  The limited degree may increase the
   risk of a network partition, or inhibit use of local neighbours (for
   the simple reason that there are less of them).  On the other hand,
   it may be easier to reason about the correctness of fixed-degree
   networks.  One of the Viceroy authors has since proposed constant-
   degree peers in a two-tier, locality-aware DHT [310] -- the lower
   degree maintained by each lower-tier peer purportedly improves
   network adaptability.  Another Viceroy author has since explored an

   alternative bounded-degree graph for P2P, namely the de Bruijn graph
   [336].

3.5.5.  de Bruijn (D2B, Koorde, Distance Halving, ODRI)

   De Bruijn graphs have had numerous refinements since their inception
   [337, 338].  Schlumberger was the first to use them for networking
   [339].  Two research teams independently devised the 'generalized' de
   Bruijn graph that accommodates a flexible number of nodes in the
   system [340, 341].  Rowley and Bose studied fault-tolerant rings
   overlaid on the de Bruijn graph [342].  Lee, Liu, et al. devised a
   two-level de Bruijn hierarchy, whereby clusters of local nodes are
   interconnected by a second-tier ring [343].

   Many of the algorithms discussed previously are 'greedy' in that each
   time a query is forwarded, it moves closer to the destination.
   Unfortunately, greedy algorithms are generally suboptimal -- for a
   given degree, the routing distance is longer than necessary [344].
   Unlike these earlier P2P designs, de Bruijn graphs of degree k
   achieve an asymptotically optimal diameter log n, where n is the
   number of nodes in the system and k can be varied to improve
   resilience.  If there are O(log n) neighbours per node, the de Bruijn
   hop count is O(log n/log log n).  To illustrate de Bruijn's practical
   advantage, consider a network with one million nodes of degree 20:
   Chord has a diameter of 20, while de Bruijn has a diameter of 5 [36].
   In 2003, there were a quick succession of de Bruijn proposals -- D2B
   [345], Koorde [37], Distance Halving [132, 336], and the Optimal
   Diameter Routing Infrastructure (ODRI) [36].

   Fraigniaud and Gauron began the D2B design by laying out an informal
   problem statement: keys should be evenly distributed; lookup latency
   should be small; traffic load should be evenly distributed; updates
   of routing tables and redistribution of keys should be fast when
   nodes join or leave the network.  They defined a node's "congestion"
   to be the probability that a lookup will traverse it.  Apart from its
   optimal de Bruijn diameter, they highlighted D2B's merits: a constant
   expected update time when nodes join and leave (O(log n) with high
   probability (w.h.p.)); the expected node congestion is O((log n)/n)
   (O(((log n)^2)/n) w.h.p.) [345].  D2B's resilience was discussed only
   in passing.

   Koorde extends Chord to attain the optimal de Bruijn degree/diameter
   trade-off above [37].  Unlike D2B, Koorde does not constrain the
   selection of node identifiers.  Also unlike D2B, it caters to
   concurrent joins, by extension of Chord's functionality.  Kaashoek
   and Karger investigated Koorde's resilience to a rather harsh failure
   scenario: "in order for a network to stay connected when all nodes
   fail with probability of 1/2, some nodes must have degree

   omega(log n)" [37].  They sketched a mechanism to increase Koorde's
   degree for this more stringent fault tolerance, losing de Bruijn's
   constant degree advantage.  Similarly, to achieve a constant-factor
   load balance, Koorde would have to sacrifice its degree optimality.
   They suggested that the ability to trade the degree, and hence the
   maintenance overhead, against the expected hop count may be important
   for churning systems.  They also identified an open problem: find a
   load-balanced, degree optimal DHT.  Datta, Girdzijauskas, et al.
   showed that for arbitrary key distributions, de Bruijn graphs fail to
   meet the dual goals of load balancing and search efficiency [346].
   They posed the question, "(Is there) a constant routing table sized
   DHT which meets the conflicting goals of storage load balancing and
   search efficiency for an arbitrary and changing key distribution?"

   Distance Halving was also inspired by de Bruijn [336] and shares its
   optimal diameter.  Naor and Wieder argued for a two-step
   "continuous-discrete" approach for its design.  The correctness of
   its algorithms is proven in a continuous setting.  The algorithms are
   then mapped to a discrete space.  The source x and target y are
   points on the continuous interval [0,1).  Data items are hashed to
   this same interval.  <str> is a string that determines how messages
   leave any point on the ring: if bit t of the string is 0, the left
   leg is taken; if it is 1, the right leg is taken.  <str> increases by
   one bit each hop, giving a sequence by which to step around the ring.
   A lookup has two phases.  In the first, the lookup message containing
   the source, target, and the random string hops toward the midpoint of
   the source and target.  On each hop, the distance between <str>(x)
   and <str>(y) is halved, by virtue of the specific 'left' and 'right'
   functions.  In the second phase, the message steps 'backward' from
   the midpoint to the target, removing the last bit in <str> at each
   hop. 'Join' and 'leave' algorithms were outlined but there was no
   consideration of recovery times or message load on churn.  Using the
   Distance Halving properties, the authors devised a caching scheme to
   relieve congestion in a large P2P network.  They have also modified
   the algorithm to be more robust in the presence of random faults
   [132].

   Solid comparisons of DHT resilience are scarce, but Loguinov, Kumar,
   et al. give just that in their ODRI paper [36].  They compare Chord,
   CAN, and de Bruijn in terms of routing performance, graph expansion
   and clustering.  At the outset, they give the optimal diameter (the
   maximum hop count between any two nodes in the graph) and average hop
   count for graphs of fixed degree.  De Bruijn graphs converge to both
   optima, and outperform Chord and CAN on both counts.  These optima
   impact both delay and aggregate lookup load.  They present two
   clustering measures (edge expansion and node expansion), which are
   interesting for resilience.  Unfortunately, after decades of de
   Bruijn research, they have no exact solution.  De Bruijn was shown to

   be superior in terms of path overlap - "de Bruijn automatically
   selects backup paths that do not overlap with the best shortest path
   or with each other" [36].

3.5.6.  Skip Graphs

   Skip Graphs have been pursued by two research camps [38, 41].  They
   augment the earlier Skip Lists [347, 348].  Unlike earlier balanced
   trees, the Skip List is probabilistic -- its insert and delete
   operations do not require tree rearrangements and so are faster by a
   constant factor.  The Skip List consists of layers of ordered linked
   lists.  All nodes participate in the bottom layer 0 list.  Some of
   these nodes participate in the layer 1 list with some fixed
   probability.  A subset of layer 1 nodes participate in the layer 2
   list, and so on.  A lookup can proceed quickly through the list by
   traversing the sparse upper layers until it is close to, or at, the
   target.  Unfortunately, nodes in the upper layers of a Skip List are
   potential hot spots and single points of failure.  Unlike Skip Lists,
   Skip Graphs provide multiple lists at each level for redundancy, and
   every node participates in one of the lists at each level.

   Each node in a Skip Graph has theta(log n) neighbours on average,
   like some of the preceding DHTs.  The Skip Graph's primary edge over
   the DHTs is its support for prefix and proximity search.  DHTs hash
   objects to a random point in the graph.  Consequently, they give no
   guarantees over where the data is stored.  Nor do they guarantee that
   the path to the data will stay within the one administration as far
   as possible [38].  Skip graphs, on the other hand, provide for
   location-sensitive name searches.  For example, to find the document
   docname on the node user.company.com, the Skip Graph might step
   through its ordered lists for the prefix com.company.user [38].
   Alternatively, to find an object with a numeric identifier, an
   algorithm might search the lowest layer of the Skip Graph for the
   first digit, the next layer for the next digit, in the same vein
   until all digits are resolved.  Being ordered, Skip Graphs also
   facilitate range searches.  In each of these examples, the Skip Graph
   can be arranged such that the path to the target, as far as possible,
   stays within an administrative boundary.  If one administration is
   detached from the rest of the Skip Graph, routing can continue within
   each of the partitions.  Mechanisms have been devised to merge
   disconnected segments [157], though at this stage, segments are re-
   merged one at a time.  A parallel merge algorithm has been flagged
   for future work.

   The advantages of Skip Graphs come at a cost.  To be able to provide
   range queries and data placement flexibility, Skip Graph nodes
   require many more pointers than their DHT counterparts.  An increased
   number of pointers implies increased maintenance traffic.  Another

   shortcoming of at least one of the early proposals was that no
   algorithm was given to assign keys to machines.  Consequently, there
   are no guarantees on system-wide load balancing or on the distance
   between adjacent keys [100].  Aspnes, Kirsch, et al. have recently
   devised a scheme to reduce the inter-machine pointer count from
   O(mlogm), where m is the number of data elements, to O(nlog n), where
   n is the number of nodes [100].  They proposed a two-layer scheme --
   one layer for the Skip Graph itself and the second 'bucket layer'.
   Each machine is responsible for a number of buckets and each bucket
   elects a representative key.  Nodes locally adjust their load.  They
   accept additional keys if they are below their threshold or disperse
   keys to nearby nodes if they are above threshold.  There appear to be
   numerous open issues: simulations have been done but analysis is
   outstanding; mechanisms are required to handle the arrival and
   departure of nodes; there were only brief hints as to how to handle
   nodes with different capacities.

4.  Semantic Index

   Semantic indexes capture object relationships.  While the semantic-
   free methods (DHTs) have firmer theoretic foundations and guarantee
   that a key can be found if it exists, they do not capture the
   relationships between the document name and its content or metadata
   on their own.  Semantic P2P designs do.  However, since their design
   is often driven by heuristics, they may not guarantee that scarce
   items will be found.

   So what might the semantically indexed P2Ps add to an already crowded
   field of distributed information architectures?  At one extreme,
   there are the distributed relational database management systems
   (RDBMSs), with their strong consistency guarantees [284].  They
   provide strong data independence, the flexibility of SQL queries, and
   strong transactional semantics -- Atomicity, Consistency, Isolation
   and Durability (ACID) [349].  They guarantee that the query response
   is complete -- all matching results are returned.  The price is
   performance.  They scale to perhaps 1000 nodes, as evidenced in
   Mariposa [350, 351], or require query caching front ends to constrain
   the load [284].  Database research has "arguably been cornered into
   traditional, high-end, transactional applications" [72].  Then there
   are distributed file systems, like the Network File System (NFS) or
   the Serverless Network File Systems (xFS), with little data
   independence, low-level file retrieval interfaces, and varied
   consistency [284].  Today's eclectic mix of Content Distribution
   Networks (CDNs) generally deload primary servers by redirecting Web
   requests to a nearby replica.  Some intercept the HTTP requests at
   the DNS level and then use consistent hashing to find a replica [23].
   Since this same consistent hashing was a forerunner to the DHT

   approaches above, CDNs are generally constrained to the same simple
   key lookups.

   The opportunity for semantically indexed P2Ps, then, is to provide:

   a) graduated data independence, consistency, and query flexibility,
      and

   b) probabilistically complete query responses, across

   c) very large numbers of low-cost, geographically distributed,
      dynamic nodes.

4.1.  Keyword Lookup

   P2P keyword lookup is best understood by considering the structure of
   the underlying index and the algorithms by which queries are routed
   over that index.  Figure 3 summarizes the following paragraphs by
   classifying the keyword query algorithms, index structures, and
   metrics.  The research has largely focused on scalability, not
   dependability.  There have been very few studies that quantify the
   impact of network churn.  One exception is the work by Chawathe, et
   al. on the Gia system [61].  Gia's combination of algorithms from
   Figure 3 (receiver-based flow control, biased random walk, and one-
   hop replication) gave 2-4 orders of magnitude improvement in query
   success rates in churning networks.

   QUERY
   Query routing
     Flooding: Peers only index local files so queries must propagate
       widely [4]
     Policy-based: Choice of the next hop node: random; most/least
       recently used; most files shared; most results [265, 352]
     Random walks: Parallel [67] or biased random walks [61, 66]
   Query forwarding
     Iterative: Nodes perform iterative unicast searches of ultrapeers,
       until the desired number of results is achieved.  See Gnutella
       UDP Extension for Scalable Searches (GUESS) [265, 353]
     Recursive
   Query flow control
     Receiver-controlled: Receivers grant query tokens to senders, so
       as to avoid overload [61]
     Reactive: sender throttles queries when it notices receivers are
       discarding packets [61, 66]
     Dynamic Time To Live: In the Dynamic Query Protocol, the sender
       adjusts the time-to-live on each iteration based on the number
       of results received, the number of connections left, and the
       number of nodes already theoretically reached by the search [354]

   INDEX
   Distribution
     Compression: Leaf nodes periodically send ultrapeers compressed
       query routing tables, as in the Query Routing Protocol [260]
     One hop replication: Nodes maintain an index of content on their
       nearest neighbors [61, 352]
   Partitioning
     By document [210]
     By keyword: Use an inverted list to find a matching document,
       either locally or at another peer [21].  Partition by keyword
       sets [355]
     By document and keyword: Also called Multi-Level Partitioning [21]

   METRIC
   Query load: Queries per second per node/link [65, 265]
   Degree: The number of links per node [66, 352].  Early P2P networks
     approximated power-law networks, where the number of nodes with L
     links is proportional to L^(-k), where k is a constant [65]
   Query delay: Reported in terms of time and hop count [61, 66]
   Query success rate: The "Collapse Point" is the per-node query rate
     at which the query success rate drops below 90% [61].  See
     also [61, 265, 352].

                  Figure 3: Keyword Lookup in P2P Systems

4.1.1.  Gnutella Enhancements

   Perhaps the most widely referenced P2P system for simple keyword
   match is Gnutella [4].  Gnutella queries contain a string of
   keywords.  Gnutella peers answer when they have files whose names
   contain all the keywords.  As discussed in Section 2.1, early
   versions of Gnutella did not forward the document index.  Queries
   were flooded and peers searched their own local indexes for filename
   matches.  An early review highlighted numerous areas for improvement
   [65].  It was estimated that the query traffic alone from 50,000
   early-generation Gnutella nodes would amount to 1.7% of the total
   U.S. Internet backbone traffic at December 2000 levels.  It was
   speculated that high-degree Gnutella nodes would impede
   dependability.  An unnecessarily high percentage of Gnutella traffic
   crossed Autonomous System (AS) boundaries -- a locality mechanism may
   have found suitable nearby peers.

   Fortunately, there have since been numerous enhancements within the
   Gnutella Developer Forum.  At the time of writing, it has been
   reported that Gnutella has almost 350,000 unique hosts, of which
   nearly 90,000 accept incoming connections [356].  One of the main
   improvements is that an index of filename keywords, called the Query
   Routing Table (QRT), can now be forwarded from 'leaf peers' to its
   'ultrapeers' [260].  Ultrapeers can then ensure that the leaves only
   receive queries for which they have a match, dramatically reducing
   the query traffic at the leaves.  Ultrapeers can have connections to
   many leaf nodes (~10-100) and a small number of other ultrapeers
   (<10) [260].  Originally, a leaf node's QRT was not forwarded by the
   parent ultrapeer to other ultrapeers.  More recently, there has been
   a proposal to distribute aggregated QRTs amongst ultrapeers [357].
   To further limit traffic, QRTs are compressed by hashing, according
   to the Query Routing Protocol (QRP) specification [281].  This same
   specification claims QRP may reduce Gnutella traffic by orders of
   magnitude, but cautions that simulation is required before mass
   deployment.  A known shortcoming of QRP was that the extent of query
   propagation was independent of the popularity of the search terms.
   The Dynamic Query Protocol addressed this [358].  It required leaf
   nodes to send single queries to high-degree ultrapeers that adjust
   the queries' time-to-live (TTL) bounds according to the number of
   received query results.  An earlier proposal, called the Gnutella UDP
   Extension for Scalable Searches (GUESS) [353], similarly aimed to
   reduce the number of queries for widely distributed files.  GUESS
   reuses the non-forwarding idea (Section 2).  A GUESS peer repeatedly
   queries single ultrapeers with a TTL of 1, with a small timeout on
   each query to limit load.  It chooses the number of iterations and
   selects ultrapeers so as to satisfy its search needs.  For
   adaptability, a small number of experimental Gnutella nodes have

   implemented eXtensible Markup Language (XML) schemas for richer
   queries [359, 360].  None of the above Gnutella proposals explicitly
   assess robustness.

   The broader research community has recently been leveraging aspects
   of the Gnutella design.  Lv, Ratnasamy, et al. exposed one assumption
   implicit in some of the early DHT work -- that designs "such as
   Gnutella are inherently not scalable, and therefore should be
   abandoned" [66].  They argued that by making better use of the more
   powerful peers, Gnutella's scalability issues could be alleviated.
   Instead of its flooding mechanism, they used random walks.  Their
   preliminary design to bias random walks towards high capacity nodes
   did not go as far as the ultrapeer proposals in that the indexes did
   not move to the high-capacity nodes.  Chawathe, Ratnasamy, et al.
   chose to extend the Gnutella design with their Gia system, in
   response to the perceived shortcomings of DHTs in Section 1.2 [61].
   Compared to the early Gnutella designs, they incorporated several
   novel features.  They devise a topology adaptation algorithm so that
   most peers are attached to high-degree peers.  They use a random walk
   search algorithm, in lieu of flooding, and bias the query load
   towards higher-degree peers.  For 'one-hop replication', they require
   all nodes to keep pointers to content on adjacent peers.  To
   implement a receiver-controlled token-based flow control, a peer must
   have a token from its neighbouring peer before it sends a query to
   it.  Chawathe, Ratnasamy, et al. show by simulations that the
   combination of these features provides a scalability improvement of
   three to five orders of magnitude over Gnutella "while retaining
   significant robustness".  The main robustness metrics they used were
   the 'collapse point' query rate (the per-node query rate at which the
   successful query rate falls below 90%) and the average hop count
   immediately prior to collapse.  Their comparison with Gnutella did
   not take into account the Gnutella enhancements above -- this was
   left as future work.  Castro, Costa, and Rowstron argued that if
   Gnutella were built on top of a structured overlay, then both the
   query and overlay maintenance traffic could be reduced [259].  Yang,
   Vinograd, et al. explore various policies for peer selection in the
   GUESS protocol, since the issue is left open in the original proposal
   [265].  For example, the peer initiating the query could choose peers
   that have been "most recently used" or that have the "most files
   shared".  Various policy pitfalls are identified.  For example, good
   peers could be overloaded, victims of their own success.
   Alternatively, malicious peers could encourage the querying peer to
   try inactive peers.  They conclude that a "most results" policy gives
   the best balance of robustness and efficiency.  Like Castro, Costa,
   and Rowstron, they concentrated on the static network scenario.
   Cholvi, Felber, et al. very briefly describe how similar "least
   recently used" and "most often used" heuristics can be used by a peer
   to select peer 'acquaintances' [352].  They were motivated by the

   congestion associated with Gnutella's TTL-limited flooding.
   Recognizing that the busiest peers can quickly become overloaded
   central hubs for the entire network, they limit the number of
   acquaintances for any given peer to 25.  They sketch a mechanism to
   decrement a query's TTL multiple times when it traverses "interested
   peers".  In summary, these Gnutella-related investigations are
   characterized by a bias for high-degree peers and very short directed
   query paths, a disdain for flooding, and concern about excessive load
   on the 'better' peers.  Generally, the robustness analysis for
   dynamic networks (content updates and node arrivals/departures)
   remains open.

4.1.2.  Partition-by-Document, Partition-by-Keyword

   One aspect of P2P keyword search systems has received particular
   attention: should the index be partitioned by document or by keyword?
   The issue affects scalability.  To be partitioned by document, each
   node has a local index of documents for which it is responsible.
   Gnutella is a prime example.  Queries are generally flooded in
   systems partitioned by document.  On the other hand, a peer may
   assume responsibility for a set of keywords.  The peer uses an
   inverted list to find a matching document, either locally or at
   another peer.  If the query contains several keywords, inverted lists
   may need to be retrieved from several different peers to find the
   intersection [21].  The initial assessment by Li, Loo, et al. was
   that the partition-by-document approach was superior [210].  For one
   scenario of a full-text Web search, they estimated the communications
   costs to be about six times higher than the feasible budget.
   However, wanting to exploit prior work on inverted list intersection,
   they studied the partition-by-keyword strategy.  They proposed
   several optimizations that put the communication costs for a
   partition-by-keyword system within an order of magnitude of
   feasibility.  There had been a couple of prior papers that suggested
   partitioned-by-keyword designs incorporate DHTs to map keywords to
   peers [355, 361].  In Gnawali's Keyword-set Search System (KSS), the
   index is partitioned by sets of keywords [355].  Terpstra, Behnel, et
   al. point out that by keeping keyword pairs or triples, the number of
   lists per document in KSS is squared or tripled [362].  Shi,
   Guangwen, et al. interpreted the approximations of Li, Loo, et al. to
   mean that neither approach is feasible on its own [21].  Their
   Multi-Level Partitioning (MLP) scheme incorporates both partitioning
   approaches.  They arrange nodes into a group hierarchy, with all
   nodes in the single 'level 0' group, and with the same nodes sub-
   divided into k logical subgroups on 'level 1'.  The subgroups are
   again divided, level by level, until level l.  The inverted index is
   partitioned by document between groups and by keyword within groups.
   MLP avoids the query flooding normally associated with systems
   partitioned by document, since a small number of nodes in each group

   process the query.  It reduces the bandwidth overheads associated
   with inverted list intersection in systems partitioned solely by
   keyword, since groups can calculate the intersection independently
   over the documents for which they are responsible.  MLP was overlaid
   on SkipNet, per Section 3.5.6 [38].  Some initial analyses of
   communications costs and query latencies were provided.

4.1.3.  Partial Search, Exhaustive Search

   Much of the research above addresses partial keyword search.
   Daswani, et al. highlighted the open problem of efficient,
   comprehensive keyword search [25].  How can exhaustive searches be
   achieved without flooding queries to every peer in the network?
   Terpstra, Behnel et al. couched the keyword search problem in
   rendezvous terms: dynamic keyword queries need to 'meet' with static
   document lists [362].  Their Bitzipper scheme is partitioned by
   document.  They improved on full flooding by putting document
   metadata on 2sqrt(n) nodes and forwarding queries through only
   6sqrt(n) nodes.  They reported that Bitzipper nodes need only 1/166th
   of the bandwidth of full-flooding Gnutella nodes for an exhaustive
   search.  An initial comparison of query load was given.  There was
   little consideration of either static or dynamic resilience; that is,
   of nodes failing, of documents continually changing, or of nodes
   continually joining and leaving the network.

4.2.  Information Retrieval

   The field of Information Retrieval (IR) has matured considerably
   since its inception in the 1950s [363].  A taxonomy for IR models has
   been formalized [262].  It consists of four elements: a
   representation of documents in a collection; a representation of user
   queries; a framework describing relationships between document
   representations and queries; and a ranking function that quantifies
   an ordering amongst documents for a particular query.  Three main
   issues motivate current IR research -- information relevance, query
   response time, and user interaction with IR systems.  The dominant IR
   trends for searching large text collections are also threefold [262].
   The size of collections is increasing dramatically.  More complicated
   search mechanisms are being found to exploit document structure, to
   accommodate heterogeneous document collections, and to deal with
   document errors.  Compression is in favour -- it may be quicker to
   search compact text or retrieve it from external devices.  In a
   distributed IR system, query processing has four parts.  Firstly,
   particular collections are targeted for the search.  Secondly,
   queries are sent to the targeted collections.  Queries are then
   evaluated at the individual collections.  Finally, results from the
   collections are collated.

   So how do P2P networks differ from distributed IR systems?  Bawa,
   Manku, et al. presented four differences [62].  They suggested that a
   P2P network is typically larger, with tens or hundreds of thousands
   of nodes.  It is usually more dynamic, with node lifetimes measured
   in hours.  They suggested that a P2P network is usually homogeneous,
   with a common resource description language.  It lacks the
   centralized "mediators" found in many IR systems that assume
   responsibility for selecting collections, for rewriting queries, and
   for merging ranked results.  These distinctions are generally aligned
   with the peer characteristics in Section 1.  One might add that P2P
   nodes display more symmetry -- peers are often both information
   consumers and producers.  Daswani, Garcia-Molina, et al. pointed out
   that, while there are IR techniques for ranked keyword search at
   moderate scale, research is required so that ranking mechanisms are
   efficient at the larger scale targeted by P2P designs [25].  Joseph
   and Hoshiai surveyed several P2P systems using metadata techniques
   from the IR toolkit [60].  They described an assortment of IR
   techniques and P2P systems, including various metadata formats,
   retrieval models, bloom filters, DHTs, and trust issues.

   In the ensuing paragraphs, we survey P2P work that has incorporated
   information retrieval models, particularly the Vector Model and the
   Latent Semantic Indexing Model.  We omit the P2P work based on
   Bayesian models.  Some have pointed to such work [60], but made no
   explicit mention of the model [364].  One early paper on P2P
   content-based image retrieval also leveraged the Bayesian model
   [365].  For the former two models, we briefly describe the design,
   then try to highlight robustness aspects.  On robustness, we are
   again stymied for lack of prior work.  Indeed, a search across all
   proceedings of the Annual ACM Conference on Research and Development
   in Information Retrieval for the words "reliable", "available",
   "dependable", or "adaptable" did not return any results at the time
   of writing.  In contrast, a standard text on distributed database
   management systems [366] contains a whole chapter on reliability.  IR
   research concentrates on performance measures.  Common performance
   measures include recall, the fraction of the relevant documents that
   has been retrieved and precision, the fraction of the retrieved
   documents that is relevant [262].  Ideally, an IR system would have
   high recall and high precision.  Unfortunately techniques favouring
   one often disadvantage the other [363].

4.2.1.  Vector Model (PlanetP, FASD, eSearch)

   The vector model [367] represents both documents and queries as term
   vectors, where a term could be a word or a phrase.  If a document or
   query has a term, the weight of the corresponding dimension of the
   vector is non-zero.  The similarity of the document and query vectors
   gives an indication of how well a document matches a particular
   query.

   The weighting calculation is critical across the retrieval models.
   Amongst the numerous proposals for the probabilistic and vector
   models, there are some commonly recurring weighting factors [363].
   One is term frequency.  The more a term is repeated in a document,
   the more important the term is.  Another is inverse document
   frequency.  Terms common to many documents give less information
   about the content of a document.  Then there is document length.
   Larger documents can bias term frequencies, so weightings are
   sometimes normalized against document length.  The expression "TFIDF
   weighting" refers to the collection of weighting calculations that
   incorporate term frequency and inverse document frequency, not just
   to one.  Two weighting calculations have been particularly dominant
   -- Okapi [368] and pivoted normalization [369].  A distributed
   version of Google's Pagerank algorithm has also been devised for a
   P2P environment [370].  It allows incremental, ongoing Pagerank
   calculations while documents are inserted and deleted.

   A couple of early P2P systems leveraged the vector model.  Building
   on the vector model, PlanetP divided the ranking problem into two
   steps [215].  In the first, peers are ranked for the probability that
   they have matching documents.  In the second, higher-priority peers
   are contacted and the matching documents are ranked.  An Inverse Peer
   Frequency, analogous to the Inverse Document Frequency, is used to
   rank relevant peers.  To further constrain the query traffic, PlanetP
   contacts only the first group of m peers to retrieve a relevant set
   of documents.  In this way, it repeatedly contacts groups of m peers
   until the top k document rankings are stable.  While the PlanetP
   designers first quantified recall and precision, they also considered
   reliability.  Each PlanetP peer has a global index with a list of all
   other peers, their IP addresses, and their Bloom filters.  This large
   volume of shared information needs to be maintained.  Klampanos and
   Jose saw this as PlanetP's primary shortcoming [371].  Each Bloom
   filter summarized the set of terms in the local index of each peer.
   The time to propagate changes, be they new documents or peer
   arrivals/departures, was studied by simulation for up to 1000 peers.
   The reported propagation times were in the hundreds of seconds.
   Design workarounds were required for PlanetP to be viable across
   slower dial-up modem connections.  For future work, the authors were

   considering some sort of hierarchy to scale to larger numbers of
   peers.

   A second early system using the vector model is the Fault-tolerant,
   Adaptive, Scalable Distributed (FASD) search engine [283], which
   extended the Freenet design (Section 2.3) for richer queries.  The
   original Freenet design could find a document based on a globally
   unique identifier.  Kronfol's design added the ability to search, for
   example, for documents about "apples AND oranges NOT bananas".  It
   uses a TFIDF weighting scheme to build a document's term vector.
   Each peer calculates the similarity of the query vector and local
   documents and forwards the query to the best downstream peer.  Once
   the best downstream peer returns a result, the second-best peer is
   tried, and so on.  Simulations with 1000 nodes gave an indication of
   the query path lengths in various situations -- when routing queries
   in a network with constant rates of node and document insertion, when
   bootstrapping the network in a "worst-case" ring topology, or when
   failing randomly and specifically selected peers.  Kronfol claimed
   excellent average-case performance -- less than 20 hops to retrieve
   the same top n results as a centralized search engine.  There were,
   however, numerous cases where the worst-case path length was several
   hundred hops in a network of only 1000 nodes.

   In parallel, there have been some P2P designs based on the vector
   model from the University of Rochester -- pSearch [9, 372] and
   eSearch [373].  The early pSearch paper suggested a couple of
   retrieval models, one of which was the Vector Space Model, to search
   only the nodes likely to have matching documents.  To obtain
   approximate global statistics for the TFIDF calculation, a spanning
   tree was constructed across a subset of the peers.  For the m top
   terms, the term-to-document index was inserted into a Content-
   Addressable Network [334].  A variant that mapped terms to document
   clusters was also suggested. eSearch is a hybrid of the partition-
   by-document and partition-by-term approaches (Section 4.1.2) eSearch
   nodes are primarily partitioned by term.  Each is responsible for the
   inverted lists for some top terms.  For each document in the inverted
   list, the node stores the complete term list.  To reduce the size of
   the index, the complete term lists for a document are only kept on
   nodes that are responsible for top terms in the document.  eSearch
   uses the Okapi term weighting to select top terms.  It relies on the
   Chord DHT [34] to associate terms with nodes storing the inverted
   lists.  It also uses automatic query expansion.  This takes the
   significant terms from the top document matches and automatically
   adds them to the user's query to find additional relevant documents.
   The eSearch performance was quantified in terms of search precision,
   the number of retrieved documents, and various load-balancing
   metrics.  Compared to the more common proposals for partitioning by

   keywords, eSearch consumed 6.8 times the storage space to achieve
   faster search times.

4.2.2.  Latent Semantic Indexing (pSearch)

   Another retrieval model used in P2P proposals is Latent Semantic
   Indexing (LSI) [374].  Its key idea is to map both the document and
   query vectors to a concept space with lower dimensions.  The starting
   point is a t*N weighting matrix, where t is the total number of
   indexed terms, N is the total number of documents, and the matrix
   elements could be TFIDF rankings.  Using singular value
   decomposition, this matrix is reduced to a smaller number of
   dimensions, while retaining the more significant term-to-document
   mappings.  Baeza-Yates and Ribeiro-Neto suggested that LSI's value is
   a novel theoretic framework, but that its practical performance
   advantage for real document collections had yet to be proven [262].
   pSearch incorporated LSI [9].  By placing the indices for
   semantically similar documents close in the network, Tang, Xu, et al.
   touted significant bandwidth savings relative to the early full-
   flooding variant of Gnutella [372].  They plotted the number of nodes
   visited by a query.  They also explored the trade-off with accuracy,
   the percentage match between the documents returned by the
   distributed pSearch algorithm and those from a centralized LSI
   baseline.  In a more recent update to the pSearch work, Tang,
   Dwarkadas, et al. summarized LSI's shortcomings [375].  Firstly, for
   large document collections, its retrieval quality is inherently
   inferior to Okapi.  Secondly, singular value decomposition consumes
   excessive memory and computation time.  Consequently, the authors
   used Okapi for searching while retaining LSI for indexing.  With
   Okapi, they selected the next node to be searched and selected
   documents on searched nodes.  With LSI, they ensured that similar
   documents are clustered near each other, thereby optimizing the
   network search costs.  When retrieving a small number of top
   documents, the precision of LSI+Okapi approached that of Okapi.
   However, if retrieving a large number of documents, the LSI+Okapi
   precision is inferior.  The authors want to improve this in future
   work.

4.2.3.  Small Worlds

   The "small world" concept originally described how people are
   interconnected by short chains of acquaintances [376].  Kleinberg was
   struck by the algorithmic lesson of the small world, namely "that
   individuals using local information are collectively very effective
   at constructing short paths between two points in a social network"
   [377].  Small world networks have a small diameter and a large
   clustering coefficient (a large number of connections amongst
   relevant nodes) [378].

   The small world idea has had a limited impact on peer-to-peer
   algorithms.  It has influenced only a few unstructured [62, 378-380]
   and structured [344, 381] algorithms.  The most promising work on
   "small worlds" in P2P networks are those concerned with the
   information retrieval metrics, precision and recall [62, 378, 380].

5.  Queries

   Database research suggests directions for P2P research.  Hellerstein
   observed that, while work on fast P2P indexes is well underway, P2P
   query optimization remains a promising topic for future research
   [23].  Kossman reviewed the state of the art of distributed query
   processing, highlighting areas for future research: simulation and
   query optimization for networks of tens of thousands of servers and
   millions of clients; non-relational data types (e.g., XML, text, and
   images); and partial query responses since on the Internet, "failure
   is the rule rather than the exception" [19].  A primary motivation
   for the P2P system, PIER, was to scale from the largest database
   systems of a few hundred nodes to an Internet environment in which
   there are over 160 million nodes [22].  Litwin and Sahri have also
   considered ways to combine distributed hashing, more specifically the
   Scalable Distributed Data Structures, with SQL databases, claiming to
   be first to implement scalable distributed database partitioning
   [382].  Motivated by the lack of transparent distribution in current
   distributed databases, they measure query execution times for
   Microsoft SQL servers aggregated by means of an SDDS layer.  One of
   their starting assumptions was that it is too challenging to change
   the SQL query optimizer.

   Database research also suggests the approach to P2P research.
   Researchers of database query optimization were divided between those
   looking for optimal solutions in special cases and those using
   heuristics to answer all queries [383].  Gribble, et al. cast query
   optimization in terms of the data placement problem, which is to
   "distribute data and work so the full query workload is answered with
   lowest cost under the existing bandwidth and resource constraints"
   [250].  They pointed out that even the static version of this problem
   is NP-complete in P2P networks.  Consequently, research on massive,
   dynamic P2P networks will likely progress using both strategies of
   early database research - heuristics and special-case optimizations.

   If P2P networks are going to be adaptable, if they are to support a
   wide range of applications, then they need to accommodate many query
   types [72].  Up to this point, we have reviewed queries for keys
   (Section 3) and keywords (Sections 4.1. and 4.2).  Unfortunately, a
   major shortcoming of the DHTs in Section 3.5 is that they primarily
   support exact-match, single-key queries.  Skip Graphs support range
   and prefix queries, but not aggregation queries.  Here we probe below

   the language syntax to identify the open research issues associated
   with more expressive P2P queries [25].  Triantafillou and Pitoura
   observed the disparate P2P designs for different types of queries and
   so outlined a unifying framework [76].  To classify queries, they
   considered the number of relations (single or multiple), the number
   of attributes (single or multiple), and the type of query operator.
   They described numerous operators:  equality, range, join, and
   "special functions".  The latter referred to aggregation (like sum,
   count, average, minimum, and maximum), grouping and ordering.  The
   following sections approximately fit their taxonomy -- range queries,
   multi-attribute queries, join queries and aggregation queries.  There
   has been some initial P2P work on other query types -- continuous
   queries [20, 22, 73], recursive queries [22, 74], and adaptive
   queries [23, 75].  For these, we defer to the primary references.

5.1.  Range Queries

   The support of efficient range predicates in P2P networks was
   identified as an important open research issue by Huebsch, et al.
   [22].  Range partitioning has been important in parallel databases to
   improve performance, so that a transaction commonly needs data from
   only one disk or node [22].  One type of range search, longest prefix
   match, is important because of its prevalence in routing schemes for
   voice and data networks alike.  In other applications, users may pose
   broad, inexact queries, even though they require only a small number
   of responses.  Consequently, techniques to locate similar ranges are
   also important [77].  Various proposals for range searches over P2P
   networks are summarized in Figure 4.  Since the Scalable Distributed
   Data Structure (SDDS) has been an important influence on contemporary
   Distributed Hash Tables (DHTs) [49-51], we also include ongoing work
   on SDDS range searches.

   PEER-TO-PEER (P2P)
   Locality Sensitive Hashing (Chord) [77]
   Prefix Hash Trees (unspecified DHT) [78, 79]
   Space Filling Curves (CAN) [80]
   Space Filling Curves (Chord) [81]
   Quadtrees (Chord) [82]
   Skip Graphs [38, 41, 83, 100]
   Mercury [84]
   P-Grid [85, 86]

   SCALABLE DISTRIBUTED DATA STRUCTURES (SDDS)
   RP*   [87, 88]

       Figure 4: Solutions for Range Queries on P2P and SDDS Indexes

   The papers on P2P range search can be divided into those that rely on
   an underlying DHT (the first five entries in Figure 4) and those that
   do not (the subsequent three entries).  Bharambe, Agrawal, et al.
   argued that DHTs are inherently ill-suited to range queries [84].
   The very feature that makes for their good load balancing properties,
   randomized hash functions, works against range queries.  One possible
   solution would be to hash ranges, but this can require a priori
   partitioning.  If the partitions are too large, partitions risk
   overload.  If they are too small, there may be too many hops.

   Despite these potential shortcomings, there have been several range
   query proposals based on DHTs.  If hashing ranges to nodes, it is
   entirely possible that overlapping ranges map to different nodes.
   Gupta, Agrawal, et al. rely on locality sensitive hashing to ensure
   that, with high probability, similar ranges are mapped to the same
   node [77].  They propose one particular family of locality sensitive
   hash functions, called min-wise independent permutations.  The number
   of partitions per node and the path length were plotted against the
   total numbers of peers in the system.  For a network with 1000 nodes,
   the hop count distribution was very similar to that of the exact-
   matching Chord scheme.  Was it load-balanced?  For the same network
   with 50,000 partitions, there were over two orders of magnitude
   variation in the number of partitions at each node (first and
   ninety-ninth percentiles).  The Prefix Hash Tree is a trie in which
   prefixes are hashed onto any DHT.  The preliminary analysis suggests
   efficient doubly logarithmic lookup, balanced load, and fault
   resilience [78, 79].  Andrzejak and Xu were perhaps the first to
   propose a mapping from ranges to DHTs [80].  They use one particular
   Space Filling Curve, the Hilbert curve, over a Content Addressable
   Network (CAN) construction (Section 3.5.3).  They maintain two
   properties: nearby ranges map to nearby CAN zones; if a range is
   split into two sub-ranges, then the zones of the sub-ranges partition
   the zone of the primary range.  They plot path length and load proxy
   measures (the total number of messages and nodes visited) for three
   algorithms to propagate range queries: brute force, controlled
   flooding, and directed controlled flooding.  Schmidt and Parashar
   also advocated Space Filling Curves to achieve range queries over a
   DHT [81].  However, they point out that, while Andrzejak and Xu use
   an inverse Space Filling Curve to map a one-dimensional space to d-
   dimensional zones, they map a d-dimensional space back to a one-
   dimensional index.  Such a construction gives the ability to search
   across multiple attributes (Section 5.2).  Tanin, Harwood, et al.
   suggested quadtrees over Chord [82], and gave preliminary simulation
   results for query response times.

   Because DHTs are naturally constrained to exact-match, single-key
   queries, researchers have considered other P2P indexes for range
   searches.  Several were based on Skip Graphs [38, 41], which, unlike

   the DHTs, do not necessitate randomizing hash functions and are
   therefore capable of range searches.  Unfortunately, they are not
   load balanced [83].  For example, in SkipNet [48], hashing was added
   to balance the load -- the Skip Graph could support range searches or
   load balancing, but not both.  One solution for load-balancing relies
   on an increased number of 'virtual' servers [168] but, in their
   search for a system that can both search for ranges and balance
   loads, Bharambe, Agrawal, et al. rejected the idea [84].  The virtual
   servers work assumed load imbalance stems from hashing; that is, by
   skewed data insertions and deletions.  In some situations, the
   imbalance is triggered by a skewed query load.  In such
   circumstances, additional virtual servers can increase the number of
   routing hops and increase the number of pointers that a Skip Graph
   needs to maintain.  Ganesan, Bawa, et al. devised an alternate method
   to balance load [83].  They proposed two Skip Graphs, one to index
   the data itself and the other to track load at each node in the
   system.  Each node is able to determine the load on its neighbours
   and the most (least) loaded nodes in the system.  They devise two
   algorithms: NBRADJUST balances load on neighbouring nodes; using
   REORDER, empty nodes can take over some of the tuples on heavily
   loaded nodes.  Their simulations focus on skewed storage load, rather
   than on skewed query loads, but they surmise that the same approach
   could be used for the latter.

   Other proposals for range queries avoid both the DHT and the Skip
   Graph.  Bharambe, Agrawal, et al. distinguish their Mercury design by
   its support for multi-attribute range queries and its explicit load
   balancing [84].  In Mercury, nodes are grouped into routing hubs,
   each of which is responsible for various query attributes.  While it
   does not use hashing, Mercury is loosely similar to the DHT
   approaches: nodes within hubs are arranged into rings, like Chord
   [34]; for efficient routing within hubs, k long-distance links are
   used, like Symphony [381].  Range lookups require O(((log n)^2)/k)
   hops.  Random sampling is used to estimate the average load on nodes
   and to find the parts of the overlay that are lightly loaded.
   Whereas Symphony assumed that nodes are responsible for ranges of
   approximately equal size, Mercury's random sampling can determine the
   location of the start of the range, even for non-uniform ranges [84].
   P-Grid [42] does provide for range queries, by virtue of the key
   ordering in its tree structures.  Ganesan, Bawa, et al. critiqued its
   capabilities [83]: P-Grid assumes fixed-capacity nodes; there was no
   formal characterization of imbalance ratios or balancing costs; every
   P-Grid periodically contacts other nodes for load information.

   The work on Scalable Distributed Data Structures (SDDSs) has
   progressed in parallel with P2P work and has addressed range queries.
   Like the DHTs above, the early SDDS Linear Hashing (LH*) schemes were
   not order-preserving [52].  To facilitate range queries, Litwin,

   Niemat, et al. devised a Range Parititioning variant, RP* [87].
   There are options to dispense with the index, to add indexes to
   clients, and to add them to servers.  In the variant without an
   index, every query is issued via multicasting.  The other variants
   also use some multicasting.  The initial RP* paper suggested
   scalability to thousands of sites, but a more recent RP* simulation
   was capped at 140 servers [88].  In that work, Tsangou, Ndiaye, et
   al. investigated TCP and UDP mechanisms by which servers could return
   range query results to clients.  The primary metrics were search and
   response times.  Amongst the commercial parallel database management
   systems, they reported that the largest seems only to scale to 32
   servers (SQL Server 2000).  For future work, they planned to explore
   aggregation of query results, rather than establishing a connection
   between the client and every single server with a response.

   All in all, it seems there are numerous open research questions on
   P2P range queries.  How realistic is the maintenance of global load
   statistics considering the scale and dynamism of P2P networks?
   Simulations at larger scales are required.  Proposals should take
   into account both the storage load (insert and delete messages) and
   the query load (lookup messages).  Simplifying assumptions need to be
   attacked.  For example, how well do the above solutions work in
   networks with heterogeneous nodes, where the maximum message loads
   and index sizes are node-dependent?

5.2.  Multi-Attribute Queries

   There has been some work on multi-attribute P2P queries.  As late as
   September 2003, it was suggested that there has not been an efficient
   solution [76].

   Again, an early significant work on multi-attribute queries over
   aggregated commodity nodes germinated amongst SDDSs.  k-RP* [89] uses
   the multi-dimensional binary search tree (or k-d tree, where k
   indicates the number of dimensions of the search index) [384].  It
   builds on the RP* work from the previous section and inherits their
   capabilities for range search and partial match.  Like the other
   SDDSs, k-RP* indexes can fit into RAM for very fast lookup.  For
   future work, Litwin and Neimat suggested a) a formal analysis of the
   range search termination algorithm and the k-d paging algorithm, b) a
   comparison with other multi-attribute data structures (quad-trees and
   R-trees) and c) exploration of query processing, concurrency control,
   and transaction management for k-RP* files [89].  On the latter
   point, others have considered transactions to be inconsequential to
   the core problem of supporting more complex queries in P2P networks
   [72].

   In architecting their secure wide-area Service Discovery Service
   (SDS), Hodes, Czerwinski, et al. considered three possible designs
   for multi-criteria search -- Centralization, Mapping and Flooding
   [90].  These correlate to the index classifications of Section 2 --
   Central, Distributed, and Local.  They discounted the centralized,
   Napster-like index for its risk of a single point of failure.  They
   considered the hash-based mappings of Section 3, but concluded that
   it would not be possible to adequately partition data.  A document
   satisfying many criteria would be wastefully stored in many
   partitions.  They rejected full flooding for its lack of scalability.
   Instead, they devised a query filtering technique, reminiscent of
   Gnutella's query routing protocol (Section 4.1).  Nodes push
   proactive summaries of their data rather than waiting for a query.
   Summaries are aggregated and stored throughout a server hierarchy, to
   guide subsequent queries.  Some initial prototype measurements were
   provided for total load on the system, but not for load distribution.
   They put several issues forward for future work.  The indexing needs
   to be flexible to change according to query and storage workloads.  A
   mesh topology might improve on their hierarchic topology since query
   misses would not propagate to root servers.  The choice is analogous
   to BGP meshes and DNS trees.

   More recently, Cai, Frank, et al. devised the Multi-Attribute
   Addressable Network (MAAN) [91].  They built on Chord to provide both
   multi-attribute and range queries, claiming to be the first to
   service both query types in a structured P2P system.  Each MAAN node
   has O(log n) neighbours, where N is the number of nodes.  MAAN
   multi-attribute range queries require O(log n+N*Smin) hops, where
   Smin is the minimum range selectivity across all attributes.
   Selectivity is the ratio of the query range to the entire identifier
   range.  The paper assumed that a locality preserving hash function
   would ensure balanced load.  Per Section 5.1, the arguments by
   Bharambe, Agrawal, et al. have highlighted the shortcomings of this
   assumption [84].  MAAN required that the schema must be fixed and
   known in advance -- adaptable schemas were recommended for subsequent
   attention.  The authors also acknowledged that there is a selectivity
   breakpoint at which full flooding becomes more efficient than their
   scheme.  This begs for a query resolution algorithm that adapts to
   the profile of queries.  Cai and Frank followed up with RDFPeers
   [55].  They differentiate their work from other RDF proposals by a)
   guaranteeing to find query results if they exist and b) removing the
   requirement of prior definition of a fixed schema.  They hashed
   <subject, predicate, object> triples onto the MAAN and reported
   routing hop metrics for their implementation.  Load imbalance across
   nodes was reduced to less than one order of magnitude, but the
   specific measure was the number of triples stored per node - skewed
   query loads were not considered.  They plan to improve load balancing
   with the virtual servers of Section 5.1 [168].

5.3.  Join Queries

   Two research teams have done some initial work on P2P join
   operations.  Harren, Hellerstein, et al. initially described a
   three-layer architecture -- storage, DHT and query processing.  They
   implemented the join operation by modifying an existing Content
   Addressable Network (CAN) simulator, reporting "significant hot-spots
   in all dimensions: storage, processing, and routing" [72].  They
   progressed their design more recently in the context of PIER, a
   distributed query engine based on CAN [22, 385].  They implemented
   two equi-join algorithms.  In their design, a key is constructed from
   the "namespace" and the "resource ID".  There is a namespace for each
   relation and the resource ID is the primary key for base tuples in
   that relation.  Queries are multicast to all nodes in the two
   namespaces (relations) to be joined.  Their first algorithm is a DHT
   version of the symmetric hash join.  Each node in the two namespaces
   finds the relevant tuples and hashes them to a new query namespace.
   The resource ID in the new namespace is the concatenation of join
   attributes.  In the second algorithm, called "fetch matches", one of
   the relations is already hashed on the join attributes.  Each node in
   the second namespace finds tuples matching the query and retrieves
   the corresponding tuples from the first relation.  They leveraged two
   other techniques, namely the symmetric semi-join rewrite and the
   Bloom filter rewrite, to reduce the high bandwidth overheads of the
   symmetric hash join.  For an overlay of 10,000 nodes, they simulated
   the delay to retrieve tuples and the aggregate network bandwidth for
   these four schemes.  The initial prototype was on a cluster of 64
   PCs, but it has more recently been expanded to PlanetLab.

   Triantafillou and Pitoura considered multicasting to large numbers of
   peers to be inefficient [76].  They therefore allocated a limited
   number of special peers, called range guards.  The domain of the join
   attributes was divided, one partition per range guard.  Join queries
   were sent only to range guards, where the query was executed.
   Efficient selection of range guards and a quantitive evaluation of
   their proposal were left for future work.

5.4.  Aggregation Queries

   Aggregation queries invariable rely on tree-structures to combine
   results from a large number of nodes.  Examples of aggregation
   queries are Count, Sum, Maximum, Minimum, Average, Median, and Top-K
   [92, 386, 387].  Figure 5 summarizes the tree and query
   characteristics that affect dependability.

   Tree type: Doesn't use DHT [92], use internal DHT trees [95], use
      independent trees on top of DHTs
   Tree repair: Periodic [93], exceptional [32]
   Tree count: One per key, one per overlay [56]
   Tree flexibility: Static [92], dynamic

   Query interface: install, update, probe [98]
   Query distribution: multicast [98], gossip [92]
   Query applications: leader election, voting, resource location,
      object placement and error recovery [98, 388]
   Query semantics
      Consistency: Best-effort, eventual [92], snapshot / interval /
         single-site validity [99]
      Timeliness [388]
      Lifetime: Continuous [97, 99], single-shot
      No. attributes: Single, multiple
   Query types: Count, sum, maximum, minimum, average, median, top k
      [92, 386, 387]

          Figure 5: Aggregation Trees and Queries in P2P Networks

   Key: Astrolabe [92]; Cone [93]; Distributed Approximative System
   Information Service (DASIS) [95]; Scalable Distributed Information

   Management System (SDIMS) [98]; Self-Organized Metadata Overlay
   (SOMO) [56]; Wildfire [99]; Willow [32]; Newscast [97]

   The fundamental design choices for aggregation trees relate to how
   the overlay uses DHTs, how it repairs itself when there are failures,
   how many aggregation trees there are, and whether the tree is static
   or dynamic (Figure 5).  Astrolabe is one of the most influential P2P
   designs included in Figure 5, yet it makes no use of DHTs [92].
   Other designs make use of the internal trees of Plaxton-like DHTs.
   Others build independent tree structures on top of DHTs.  Most of the
   designs repair the aggregation tree with periodic mechanisms similar
   to those used in the DHTs themselves.  Willow is an exception [32].
   It uses a Tree Maintenance Protocol to "zip" disjoint aggregation
   trees together when there are major failures.  Yalagandula and Dahlin
   found reconfigurations at the aggregation layer to be costly,
   suggesting more research on techniques to reduce the cost and
   frequency of such reconfigurations [98].  Many of the designs use
   multiple aggregation trees, each rooted at the DHT node responsible
   for the aggregation attribute.  On the other hand, the Self-Organized
   Metadata Overlay [56] uses a single tree and is vulnerable to a
   single point of failure at its root.

   At the time of writing, researchers have just begun exploring the
   performance of queries in the presence of churn.  Most designs are
   for best-effort queries.  Bawa, et al. devised a better consistency
   model, called Single-Site Validity [99] to qualify the accuracy of
   results when there is churn.  Its price was a five-fold increase in
   the message load, when compared to an efficient but best-effort
   Spanning Tree.  Gossip mechanisms are resilient to churn, but they
   delay aggregation results and incur high message cost for aggregation
   attributes with small read-to-write ratios.

6.  Security Considerations

   An initial list of references to research on P2P security is given in
   Figure 1, Section 1.  This document addresses P2P search.  P2P
   storage, security, and applications are recommended for further
   investigation in Section 8.

7.  Conclusions

   Research on peer-to-peer networks can be divided into four categories
   -- search, storage, security and applications.  This critical survey
   has focused on search methods.  While P2P networks have been
   classified by the existence of an index (structured or unstructured)
   or the location of the index (local, centralized, and distributed),
   this survey has shown that most have evolved to have some structure,
   whether it is indexes at superpeers or indexes defined by DHT
   algorithms.  As for location, the distributed index is most common.
   The survey has characterized indexes as semantic and semantic-free.
   It has also critiqued P2P work on major query types.  While much of
   it addresses work from 2000 or later, we have traced important
   building blocks from the 1990s.

   The initial motivation in this survey was to answer the question,
   "How robust are P2P search networks?"  The question is key to the
   deployment of P2P technology.  Balakrishnan, Kaashoek, et al. argued
   that the P2P architecture is appealing: the startup and growth
   barriers are low; they can aggregate enormous storage and processing
   resources; "the decentralized and distributed nature of P2P systems
   gives them the potential to be robust to faults or intentional
   attacks" [18].  If P2P is to be a disruptive technology in
   applications other than casual file sharing, then robustness needs to
   be practically verified [20].

   The best comparative research on P2P dependability has been done in
   the context of Distributed Hash Tables (DHTs) [291].  The entire body
   of DHT research can be distilled to four main observations about
   dependability (Section 3.2).  Firstly, static dependability
   comparisons show that no O(log n) DHT geometry is significantly more

   dependable than the other O(log n) geometries.  Secondly, dynamic
   dependability comparisons show that DHT dependability is sensitive to
   the underlying topology maintenance algorithms (Figure 2).  Thirdly,
   most DHTs use O(log n) geometries to suit ephemeral nodes, whereas
   the O(1) hop DHTs suit stable nodes - they deserve more research
   attention.  Fourthly, although not yet a mature science, the study of
   DHT dependability is helped by recent simulation tools that support
   multiple DHTs [299].

   We make the following four suggestions for future P2P research:

   1) Complete the companion P2P surveys for storage, security, and
      applications.  A rough outline has been suggested in Figure 1,
      along with references.  The need for such surveys was highlighted
      within the peer-to-peer research group of the Internet Research
      Task Force (IRTF) [17].

   2) P2P indexes are maturing.  P2P queries are embryonic.  Work on
      more expressive queries over P2P indexes started to gain momentum
      in 2003, but remains fraught with efficiency and load issues.

   3) Isolate the low-level mechanisms affecting robustness.  There is
      limited value in comparing robustness of DHT geometries (like
      rings versus de Bruijn graphs), when robustness is highly
      sensitive to underlying topology maintenance algorithms (Figure
      2).

   4) Build consensus on robustness metrics and their acceptable ranges.
      This paper has teased out numerous measures that impinge on
      robustness, for example, the median query path length for a
      failure of x% of nodes, bisection width, path overlap, the number
      of alternatives available for the next hop, lookup latency,
      average live bandwidth (bytes/node/sec), successful routing rates,
      the number of timeouts (caused by a finger pointing to a departed
      node), lookup failure rates (caused by nodes that temporarily
      point to the wrong successor during churn), and clustering
      measures (edge expansion and node expansion).  Application-level
      robustness metrics need to drive a consistent assessment of the
      underlying search mechanics.

8.  Acknowledgments

   This document was adapted from a paper in Elsevier's Computer
   Networks:

      J. Risson & T. Moors, Survey of Research towards Robust Peer-to-
      Peer Networks: Search Methods, Computer Networks 51(7)2007.

   We thank Bill Yeager, Ali Ghodsi, and several anonymous reviewers for
   thorough comments that significantly improved the quality of earlier
   versions of this document.

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Author's Addresses

   John Risson
   School of Elec Eng and Telecommunications
   University of New South Wales
   Sydney NSW 2052 Australia

   EMail: jr@tuffit.com

   Tim Moors
   School of Elec Eng and Telecommunications
   University of New South Wales
   Sydney NSW 2052 Australia

   EMail: t.moors@unsw.edu.au

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