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RFC 7945 - Information-Centric Networking: Evaluation and Securi


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Internet Research Task Force (IRTF)                  K. Pentikousis, Ed.
Request for Comments: 7945                                    Travelping
Category: Informational                                        B. Ohlman
ISSN: 2070-1721                                                 Ericsson
                                                               E. Davies
                                                  Trinity College Dublin
                                                               S. Spirou
                                                        Intracom Telecom
                                                               G. Boggia
                                                     Politecnico di Bari
                                                          September 2016

 Information-Centric Networking: Evaluation and Security Considerations

Abstract

   This document presents a number of considerations regarding
   evaluating Information-Centric Networking (ICN) and sheds some light
   on the impact of ICN on network security.  It also surveys the
   evaluation tools currently available to researchers in the ICN area
   and provides suggestions regarding methodology and metrics.

Status of This Memo

   This document is not an Internet Standards Track specification; it is
   published for informational purposes.

   This document is a product of the Internet Research Task Force
   (IRTF).  The IRTF publishes the results of Internet-related research
   and development activities.  These results might not be suitable for
   deployment.  This RFC represents the consensus of the <insert_name>
   Research Group of the Internet Research Task Force (IRTF).  Documents
   approved for publication by the IRSG are not a candidate for any
   level of Internet Standard; see Section 2 of RFC 7841.

   Information about the current status of this document, any errata,
   and how to provide feedback on it may be obtained at
   http://www.rfc-editor.org/info/rfc7945.

Copyright Notice

   Copyright (c) 2016 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents
   (http://trustee.ietf.org/license-info) in effect on the date of
   publication of this document.  Please review these documents
   carefully, as they describe your rights and restrictions with respect
   to this document.

Table of Contents

   1.  Introduction . . . . . . . . . . . . . . . . . . . . . . . . .  3
   2.  Evaluation Considerations  . . . . . . . . . . . . . . . . . .  4
     2.1.  Topology Selection . . . . . . . . . . . . . . . . . . . .  5
     2.2.  Traffic Load . . . . . . . . . . . . . . . . . . . . . . .  6
     2.3.  Choosing Relevant Metrics  . . . . . . . . . . . . . . . . 10
       2.3.1.  Traffic Metrics  . . . . . . . . . . . . . . . . . . . 13
       2.3.2.  System Metrics . . . . . . . . . . . . . . . . . . . . 14
     2.4.  Resource Equivalence and Trade-Offs  . . . . . . . . . . . 16
   3.  ICN Security Aspects . . . . . . . . . . . . . . . . . . . . . 16
     3.1. Authentication  . . . . . . . . . . . . . . . . . . . . . . 17
     3.2. Authorization, Access Control, and Logging  . . . . . . . . 18
     3.3. Privacy . . . . . . . . . . . . . . . . . . . . . . . . . . 19
     3.4. Changes to the Network Security Threat Model  . . . . . . . 20
   4.  Evaluation Tools . . . . . . . . . . . . . . . . . . . . . . . 21
     4.1.  Open-Source Implementations  . . . . . . . . . . . . . . . 21
     4.2.  Simulators and Emulators . . . . . . . . . . . . . . . . . 22
       4.2.1.  ndnSIM . . . . . . . . . . . . . . . . . . . . . . . . 22
       4.2.2.  ccnSIM . . . . . . . . . . . . . . . . . . . . . . . . 23
       4.2.3.  Icarus Simulator . . . . . . . . . . . . . . . . . . . 23
     4.3.  Experimental Facilities  . . . . . . . . . . . . . . . . . 24
       4.3.1.  Open Network Lab (ONL) . . . . . . . . . . . . . . . . 24
       4.3.2.  POINT Testbed  . . . . . . . . . . . . . . . . . . . . 25
       4.3.3.  CUTEi: Container-Based ICN Testbed . . . . . . . . . . 25
   5.  Security Considerations  . . . . . . . . . . . . . . . . . . . 25
   6.  Informative References . . . . . . . . . . . . . . . . . . . . 26
   Acknowledgments  . . . . . . . . . . . . . . . . . . . . . . . . . 37
   Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 38

1.  Introduction

   Information-Centric Networking (ICN) is a networking concept that
   arose from the desire to align the operation model of a network with
   the model of its typical use.  For TCP/IP networks, this implies
   changing the mechanisms of data access and transport from a host-to-
   host model to a user-to-information model.  The premise is that the
   effort invested in changing models will be offset, or even surpassed,
   by the potential of a "better" network.  However, such a claim can be
   validated only if it is quantified.

   Different ICN approaches are evaluated in the peer-reviewed
   literature using a mixture of theoretical analysis, simulation and
   emulation techniques, and empirical (testbed) measurements.  The
   specific methodology employed may depend on the experimentation goal,
   e.g., whether one wants to evaluate scalability, quantify resource
   utilization, or analyze economic incentives.  In addition, though, we
   observe that ease and convenience of setting up and running
   experiments can sometimes be a factor in published evaluations.  As
   discussed in [RFC7476], the development phase that ICN is going
   through and the plethora of approaches to tackle the hardest problems
   make this a very active and growing research area but, on the
   downside, it also makes it more difficult to compare different
   proposals on an equal footing.

   Performance evaluation using actual network deployments has the
   advantage of realistic workloads and reflects the environment where
   the service or protocol is to be deployed.  In the case of ICN,
   however, it is not currently clear what qualifies as a "realistic
   workload".  Trace-based analysis of ICN is in its infancy, and more
   work is needed towards defining characteristic workloads for ICN
   evaluation studies.  Accordingly, the experimental process and the
   evaluation methodology per se are actively being researched for
   different ICN architectures.  Numerous factors affect the
   experimental results, including the topology selected; the background
   traffic that an application is being subjected to; network conditions
   such as available link capacities, link delays, and loss-rate
   characteristics throughout the selected topology; failure and
   disruption patterns; node mobility; and the diversity of devices
   used.

   The goal of this document is to summarize evaluation guidelines and
   tools alongside suggested data sets and high-level approaches.  We
   expect this to be of interest to the ICN community as a whole, as it
   can assist researchers and practitioners alike to compare and
   contrast different ICN designs, as well as with the state of the art
   in host-centric solutions, and identify the respective strengths and
   weaknesses.  We note that, apart from the technical evaluation of the

   functionality of an ICN architecture, the future success of ICN will
   be largely driven by its deployability and economic viability.
   Therefore, ICN evaluations should assess incremental deployability in
   the existing network environment together with a view of how the
   technical functions will incentivize deployers to invest in the
   capabilities that allow the architecture to spread across the
   network.

   This document has been produced by the IRTF Information-Centric
   Networking Research Group (ICNRG).  The main objective of the ICNRG
   is to couple ongoing ICN research in the above areas with solutions
   that are relevant for evolving the Internet at large.  The ICNRG
   produces documents that provide guidelines for experimental
   activities in the area of ICN so that different, alternative
   solutions can be compared consistently, and information sharing can
   be accomplished for experimental deployments.  This document
   incorporates input from ICNRG participants and their corresponding
   text contributions; it has been reviewed by several ICNRG active
   participants (see the Acknowledgments), and represents the consensus
   of the research group.  That said, note that this document does not
   constitute an IETF standard; see also [RFC5743].

   The remainder of this document is organized as follows.  Section 2
   presents various techniques and considerations for evaluating
   different ICN architectures.  Section 3 discusses the impact of ICN
   on network security.  Section 4 surveys the tools currently available
   to ICN researchers.

2.  Evaluation Considerations

   It is clear that the way we evaluate IP networks will not be directly
   applicable to evaluating ICN.  In IP, the focus is on the performance
   and characteristics of end-to-end connections between a source and a
   destination.  In ICN, the "source" responding to a request can be any
   ICN node in the network and may change from request to request.  This
   makes it difficult to use concepts like delay and throughput in a
   traditional way.  In addition, evaluating resource usage in ICN is a
   more complicated task, as memory used for caching affects delays and
   use of transmission resources; see the discussion on resource
   equivalents in Section 2.4.

   There are two major types of evaluations of ICN that we see a need to
   make.  One type is to compare ICN to traditional networking, and the
   other type is to compare different ICN implementations and approaches
   against each other.

   In this section, we detail some of the functional components needed
   when evaluating different ICN implementations and approaches.

2.1.  Topology Selection

   There's a wealth of earlier work on topology selection for simulation
   and performance evaluation of host-centric networks.  While the
   classic dumbbell topology is regarded as inappropriate for ICN, most
   ICN studies so far have been based on that earlier work for host-
   centric networks [RFC7476].  However, there is no single topology
   that can be used to easily evaluate all aspects of ICN.  Therefore,
   one should choose from a range of topologies depending on the focus
   of the evaluation.

   For scalability and resilience studies, there is a wide range of
   synthetic topologies, such as the Barabasi-Albert model [Barabasi99]
   and the Watts-Strogatz small-world topology [Watts98].  These allow
   experiments to be performed whilst controlling various key parameters
   (e.g., node degree).  These synthetic topologies are appropriate in
   the general case, as there are no practical assurances that a future
   information-centric network will have the same topology as any of
   today's networks.

   When studies look at cost (e.g., transit cost) or migration to ICN,
   realistic topologies should be used.  These can be inferred from
   Internet traces, such as the CAIDA Macroscopic Internet Topology Data
   Kit (http://www.caida.org/data/active/internet-topology-data-kit) and
   Rocketfuel
   (http://www.cs.washington.edu/research/networking/rocketfuel).  A
   problem is the large size of the topology (approximately 45K
   Autonomous Systems, close to 200K links), which may limit the
   scalability of the employed evaluation tool.  Katsaros et al.
   [Katsaros15] address this problem by using scaled down topologies
   created following the methodology described in [Dimitropoulos09].

   Studies that focus on node or content mobility can benefit from
   topologies and their dynamic aspects as used in the Delay-Tolerant
   Networking (DTN) community.  As mentioned in [RFC7476], DTN traces
   are available to be used in such ICN evaluations.

   As with host-centric topologies, defining just a node graph will not
   be enough for most ICN studies.  The experimenter should also clearly
   define and list the respective matrices that correspond to the
   network, storage, and computation capacities available at each node
   as well as the delay characteristics of each link [Montage].  Real
   values for such parameters can be taken from existing platforms such
   as iPlane (http://iplane.cs.washington.edu).  Synthetic values could
   be produced with specific tools [Kaune09].

2.2.  Traffic Load

   In this subsection, we provide a set of common guidelines, in the
   form of what we will refer to as a content catalog for different
   scenarios.  This catalog, which is based on previously published
   work, can be used to evaluate different ICN proposals, for instance,
   on routing, congestion control, and performance, and can be
   considered as other kinds of ICN contributions emerge.  As we are
   still lacking ICN-specific traffic workloads, we can currently only
   extrapolate from today's workloads.  A significant challenge then
   relates to the identification of the applications contributing to the
   observed traffic (e.g., Web or peer-to-peer), as well as to the exact
   amount of traffic they contribute to the overall traffic mixture.
   Efforts in this direction can take heed of today's traffic mix
   comprising Web, peer-to-peer file sharing, and User-Generated Content
   (UGC) platforms (e.g., YouTube), as well as Video on Demand (VoD)
   services.  Publicly available traces for these include those from web
   sites such as the MultiProbe Framework
   <http://multiprobe.ewi.tudelft.nl/multiprobe.html>,
   <http://an.kaist.ac.kr/traces/IMC2007.html> (see also [Cha07]), and
   the UMass Trace Repository
   <http://traces.cs.umass.edu/index.php/Network/Network>.

   Taking a more systematic approach, and with the purpose of modeling
   the traffic load, we can resort to measurement studies that
   investigate the composition of Internet traffic, such as [Labovitz10]
   and [Maier09].  In [Labovitz10], a large-scale measurement study was
   performed, with the purpose of studying the traffic crossing inter-
   domain links.  The results indicate the dominance of Web traffic,
   amounting to 52% over all measured traffic.  However, Deep Packet
   Inspection (DPI) techniques reveal that 25-40% of all HTTP traffic
   actually carries video traffic.  Results from DPI techniques also
   reveal the difficulty in correctly identifying the application type
   in the case of P2P traffic: mapping observed port numbers to well-
   known applications shows P2P traffic constituting only 0.85% of
   overall traffic, while DPI raises this percentage to 18.32%
   [Labovitz10].  Relevant studies on a large ISP show that the
   percentage of P2P traffic ranges from 17% to 19% of overall traffic
   [Maier09].  Table 1 provides an overview of these figures.  The
   "other" traffic type denotes traffic that cannot be classified in any
   of the first three application categories, and it consists of
   unclassified traffic and traffic heavily fragmented into several
   applications (e.g., 0.17% DNS traffic).

                   Traffic Type | Ratio
                   =====================
                   Web          | 31-39%
                   ---------------------
                   P2P          | 17-19%
                   ---------------------
                   Video        | 13-21%
                   ---------------------
                   Other        | 29-31%
                   =====================

   Table 1: Traffic Type Ratios of Total Traffic [Labovitz10] [Maier09]

   The content catalog for each type of traffic can be characterized by
   a specific set of parameters:

   a) the cardinality of the estimated content catalog

   b) the size of the exchanged contents (either chunks or entire named
      information objects)

   c) the popularity of objects (expressed in their request frequency)

   In most application types, the popularity distribution follows some
   power law, indicating that a small number of information items
   trigger a large proportion of the entire set of requests.  The exact
   shape of the power law popularity distribution directly impacts the
   performance of the underlying protocols.  For instance, highly skewed
   popularity distributions (e.g., a Zipf-like distribution with a high
   slope value) favor the deployment of caching schemes, since caching a
   very small set of information items can dramatically increase the
   cache hit ratio.

   Several studies in the past few years have stated that Zipf's law is
   the discrete distribution that best represents the request frequency
   in a number of application scenarios, ranging from the Web to VoD
   services.  The key aspect of this distribution is that the frequency
   of a content request is inversely proportional to the rank of the
   content itself, i.e., the smaller the rank, the higher the request
   frequency.  If M denotes the content catalog cardinality and 1 <= i
   <= M denotes the rank of the i-th most popular content, we can
   express the probability of requesting the content with rank "i" as:

   P(X=i) = (1 / i^(alpha)) / C, with C = SUM(1 / j^(alpha)), alpha > 0
   where the sum is obtained considering all values of j, 1 <= j <= M.

   A recent analysis of HTTP traffic showed that content popularity is
   better reflected by a trimodal distribution model in which the head
   and tail of a Zipf distribution (with slope value 0.84) are replaced
   by two discrete Weibull distributions with shape parameter values 0.5
   and 0.24, respectively [IMB2014].

   A variation of the Zipf distribution, termed the Mandelbrot-Zipf
   distribution was suggested [Saleh06] to better model environments
   where nodes can locally store previously requested content.  For
   example, it was observed that peer-to-peer file-sharing applications
   typically exhibited a 'fetch-at-most-once' style of behavior.  This
   is because peers tend to persistently store the files they download,
   a behavior that may also be prevalent in ICN.

   Popularity can also be characterized in terms of:

   a) The temporal dynamics of popularity, i.e., how requests are
      distributed in time.  The popularity distribution expresses the
      number of requests submitted for each information item
      participating into a certain workload.  However, they do not
      describe how these requests are distributed in time.  This aspect
      is of primary importance when considering the performance of
      caching schemes since the ordering of the requests obviously
      affects the contents of a cache.  For example, with a Least
      Frequently Used (LFU) cache replacement policy, if all requests
      for a certain item are submitted close in time, the item is
      unlikely to be evicted from the cache, even by a (globally) more
      popular item whose requests are more evenly distributed in time.
      The temporal ordering of requests gains even more importance when
      considering workloads consisting of various applications, all
      competing for the same cache space.

   b) The spatial locality of popularity i.e., how requests are
      distributed throughout a network.  The importance of spatial
      locality relates to the ability to avoid redundant traffic in the
      network.  If requests are highly localized in some area of the
      entire network, then similar requests can be more efficiently
      served with mechanisms such as caching and/or multicast, i.e., the
      concentration of similar requests in a limited area of the network
      allows increasing the perceived cache hit ratios at caches in the
      area and/or the traffic savings from the use of multicast.
      Table 2 provides an overview of distributions that can be used to
      model each of the identified traffic types i.e., Web, Video (based
      on YouTube measurements), and P2P (based on BitTorrent
      measurements).  These distributions are the outcome of a series of
      modeling efforts based on measurements of real traffic workloads
      ([Breslau99] [Mahanti00] [Busari02] [Arlitt97] [Barford98]
      [Barford99] [Hefeeda08] [Guo07] [Bellissimo04] [Cheng08]

      [Cheng13]).  A tool for the creation of synthetic workloads
      following these models, and also allowing the generation of
      different traffic mixes, is described in [Katsaros12].

       |  Object Size   |  Temporal Locality   | Popularity Distribution
   =====================================================================
   Web | Concatenation  | Ordering via the     | Zipf: p(i)=K/i^a
       | of Lognormal   | Least Recently Used  | i: popularity rank
       | (body) and     | (LRU) stack model    | N: total items
       | Pareto (tail)  | [Busari02]           | K: 1/Sum(1/i^a)
       | [Barford98]    |                      | a: distribution slope
       | [Barford99]    | Exact timing via     | values 0.64-0.84
       |                | exponential          | [Breslau99] [Mahanti00]
       |                | distribution         |
       |                | [Arlitt97]           |
   ---------------------------------------------------------------------
   VoD | Duration/size: | No analytical models | Weibull: k=0.513,
       | Concatenated   |                      | lambda=6010
       | normal; most   | Random distribution  |
       | videos         | across total         | Gamma: k=0.372,
       | ~330 kbit/s    | duration             | theta=23910
       | [Cheng13]      |                      | [Cheng08]
   ---------------------------------------------------------------------
   P2P | Wide variation | Mean arrival rate of | Mandelbrot-Zipf
       | on torrent     | 0.9454 torrents/hour | [Hefeeda08]:
       | sizes          | Peers in a swarm     | p(i)=K/((i+q)/a)
       | [Hefeeda08].   | arrive as            | q: plateau factor,
       | No analytical  | l(t)= l0*e^(-t/tau)  | 5 to 100.
       | models exist:  | l0: initial arrival  | Flatter head than in
       | Sample a real  | rate (87.74 average) | Zipf-like distribution
       | BitTorrent     | tau: object          | (where q=0)
       | distribution   | popularity           |
       | [Bellissimo04] | (1.16 average)*      |
       | or use fixed   | [Guo07]              |
       | value          |                      |
   =====================================================================

   * Random ordering of swarm births (first request).  For each swarm,
     calculate a different tau.  Based on average tau and object
     popularity.  Exponential decay rule for subsequent requests.

                 Table 2: Overview of Traffic Type Models

   Table 3 summarizes the content catalog.  With this shared point of
   reference, the use of the same set of parameters (depending on the
   scenario of interest) among researchers will be eased, and different
   proposals could be compared on a common base.

   Traffic | Catalog  |  Mean Object Size  |  Popularity Distribution
   Load    | Size     |  [Zhou11] [Fri12]  |  [Cha07] [Fri12] [Yu06]
           |[Goog08]  |  [Marciniak08]     |  [Breslau99] [Mahanti00]
           |[Zhang10a]|  [Bellissimo04]    |
           |[Cha07]   |  [Psaras11]        |
           |[Fri12]   |  [Carofiglio11]    |
           |          |                    |
           |          |                    |
           |          |                    |
   ===================================================================
   Web     |  10^12   | Chunk: 1-10 KB     | Zipf with
           |          |                    | 0.64 <= alpha <= 0.83
   -------------------------------------------------------------------
   File    | 5x10^6   | Chunk: 250-4096 KB | Zipf with
   sharing |          | Object: ~800 MB    | 0.75 <= alpha <= 0.82
   -------------------------------------------------------------------
   UGC     |  10^8    | Object: ~10 MB     | Zipf, alpha >= 2
   -------------------------------------------------------------------
   VoD     |  10^4    | Object: ~100 MB    | Zipf, 0.65 <= alpha <= 1
   (+HLS)  |          |    ~1 KB (*)       |
   (+DASH) |          |    ~5.6 KB (*)     |
   ===================================================================

    UGC = User-Generated Content
    VoD = Video on Demand
    HLS  = HTTP Live Streaming
    DASH = Dynamic Adaptive Streaming over HTTP

   (*) Using adaptive video streaming (e.g., HLS and DASH), with an
       optimal segment length (10 s for HLS and 2 s for DASH) and a
       bitrate of 4500 kbit/s [RFC7933] [Led12]

                         Table 3: Content Catalog

2.3.  Choosing Relevant Metrics

   Quantification of network performance requires a set of standard
   metrics.  These metrics should be broad enough so they can be applied
   equally to host-centric and information-centric (or other) networks.
   This will allow reasoning about a certain ICN approach in relation to
   an earlier version of the same approach, to another ICN approach, or
   to the incumbent host-centric approach.  It will therefore be less
   difficult to gauge optimization and research direction.  On the other
   hand, the metrics should be targeted to network performance only and
   should avoid unnecessary expansion into the physical and application
   layers.  Similarly, at this point, it is more important to capture as
   metrics only the main figures of merit and to leave more esoteric and
   less frequent cases for the future.

   To arrive at a set of relevant metrics, it would be beneficial to
   look at the metrics used in existing ICN approaches, such as Content-
   Centric Networking (CCN) [Jacobson09] [VoCCN] [Zhang10b], NetInf
   [4WARD6.1] [4WARD6.3] [SAIL-B2] [SAIL-B3], PURSUIT [PRST4.5], COMET
   [CMT-D5.2] [CMT-D6.2], Connect [Muscariello11] [Perino11], and
   CONVERGENCE [Detti12] [Blefari-Melazzi12] [Salsano12].  The metrics
   used in these approaches fall into two categories: metrics for the
   approach as a whole, and metrics for individual components (name
   resolution, routing, and so on).  Metrics for the entire approach are
   further subdivided into traffic and system metrics.  It is important
   to note that the various approaches do not name or define metrics
   consistently.  This is a major problem when trying to find metrics
   that allow comparison between approaches.  For the purposes of
   exposition, we have tried to smooth over differences by classifying
   similarly defined metrics under the same name.  Also, due to space
   constraints, we have chosen to report here only the most common
   metrics between approaches.  For more details, the reader should
   consult the references for each approach.

   Traffic metrics in existing ICN approaches are summarized in Table 4.
   These are metrics for evaluating an approach mainly from the
   perspective of the end user, i.e., the consumer, provider, or owner
   of the content or service.  Depending on the level where these
   metrics are measured, we have made the distinction into user,
   application, and network-level traffic metrics.  So, for example,
   network-level metrics are mostly focused on packet characteristics,
   whereas user-level metrics can cover elements of human perception.
   The approaches do not make this distinction explicitly, but we can
   see from the table that CCN and NetInf have used metrics from all
   levels, PURSUIT and COMET have focused on lower-level metrics, and
   Connect and CONVERGENCE opted for higher-level metrics.  Throughput
   and download time seem to be the most popular metrics altogether.

                   User   |    Application    |        Network
               ======================================================
                 Download | Goodput | Startup | Throughput |  Packet
                   time   |         | latency |            |  delay
   ==================================================================
   CCN         |    x     |    x    |         |      x     |    x
   ------------------------------------------------------------------
   NetInf      |    x     |         |    x    |      x     |    x
   ------------------------------------------------------------------
   PURSUIT     |          |         |    x    |      x     |    x
   ------------------------------------------------------------------
   COMET       |          |         |    x    |      x     |
   ------------------------------------------------------------------
   Connect     |    x     |         |         |            |
   ------------------------------------------------------------------
   CONVERGENCE |    x     |    x    |         |            |
   ==================================================================

            Table 4: Traffic Metrics Used in ICN Evaluations

   While traffic metrics are more important for the end user, the owner
   or operator of the networking infrastructure is normally more
   interested in system metrics, which can reveal the efficiency of an
   approach.  The most common system metrics used are: protocol
   overhead, total traffic, transit traffic, cost savings, router cost,
   and router energy consumption.

   Besides the traffic and systems metrics that aim to evaluate an
   approach as a whole, all surveyed approaches also evaluate the
   performance of individual components.  Name resolution, request/data
   routing, and data caching are the most typical components, as
   summarized in Table 5.  Forwarding Information Base (FIB) size and
   path length, i.e., the routing component metrics, are almost
   ubiquitous among approaches, perhaps due to the networking background
   of the involved researchers.  That might be also the reason for the
   sometimes decreased focus on traffic and system metrics, in favor of
   component metrics.  It can certainly be argued that traffic and
   system metrics are affected by component metrics; however, no
   approach has made the relationship clear.  With this in mind and
   taking into account that traffic and system metrics are readily
   useful to end users and network operators, we will restrict ourselves
   to those in the following subsections.

                      Resolution      |    Routing    |    Cache
               ======================================================
                 Resolution | Request | FIB  |  Path  | Size |  Hit
                    time    |  rate   | size | length |      | ratio
   ==================================================================
   CCN         |     x      |         |  x   |   x    |   x  |   x
   ------------------------------------------------------------------
   NetInf      |     x      |    x    |      |   x    |      |   x
   ------------------------------------------------------------------
   PURSUIT     |            |         |  x   |   x    |      |
   ------------------------------------------------------------------
   COMET       |     x      |    x    |  x   |   x    |      |   x
   ------------------------------------------------------------------
   CONVERGENCE |            |    x    |  x   |        |   x  |
   ==================================================================

        Table 5: Component Metrics in Existing ICN Approaches

   Before proceeding, we should note that we would like our metrics to
   be applicable to host-centric networks as well.  Standard metrics
   already exist for IP networks, and it would certainly be beneficial
   to take them into account.  It is encouraging that many of the
   metrics used by existing ICN approaches can also be used on IP
   networks and that all of the approaches have tried on occasion to
   draw the parallels.

2.3.1.  Traffic Metrics

   The IETF has been working for more than a decade on devising metrics
   and methods for measuring the performance of IP networks.  The work
   has been carried out largely within the IP Performance Metrics (IPPM)
   working group, guided by a relevant framework [RFC2330].  IPPM
   metrics include delay, delay variation, loss, reordering, and
   duplication.  While the IPPM work is certainly based on packet-
   switched IP networks, it is conceivable that it can be modified and
   extended to cover ICN networks as well.  However, more study is
   necessary to turn this claim into a certainty.  Many experts have
   toiled for a long time on devising and refining the IPPM metrics and
   methods, so it would be an advantage to use them for measuring ICN
   performance.  In addition, said metrics and methods work already for
   host-centric networks, so comparison with information-centric
   networks would entail only the ICN extension of the IPPM framework.
   Finally, an important benefit of measuring the transport performance
   of a network at its output, using Quality of Service (QoS) metrics
   such as IPPM, is that it can be done mostly without any dependence to
   applications.

   Another option for measuring transport performance would be to use
   QoS metrics, not at the output of the network like with IPPM, but at
   the input to the application.  For a live video-streaming application
   the relevant metrics would be startup latency, playout lag, and
   playout continuity.  The benefit of this approach is that it
   abstracts away all details of the underlying transport network, so it
   can be readily applied to compare between networks of different
   concepts (host-centric, information-centric, or other).  As implied
   earlier, the drawback of the approach is its dependence on the
   application, so it is likely that different types of applications
   will require different metrics.  It might be possible to identify
   standard metrics for each type of application, but the situation is
   not as clear as with IPPM metrics, and further investigation is
   necessary.

   At a higher level of abstraction, we could measure the network's
   transport performance at the application output.  This entails
   measuring the quality of the transported and reconstructed
   information as perceived by the user during consumption.  In such an
   instance we would use Quality of Experience (QoE) metrics, which are
   by definition dependent on the application.  For example, the
   standardized methods for obtaining a Mean Opinion Score (MOS) for
   VoIP (e.g., ITU-T Recommendation P.800) is quite different from those
   for IPTV (e.g., Perceptual Evaluation of Video Quality (PEVQ)).
   These methods are notoriously hard to implement, as they involve real
   users in a controlled environment.  Such constraints can be relaxed
   or dropped by using methods that model human perception under certain
   environments, but these methods are typically intrusive.  The most
   important drawback of measuring network performance at the output of
   the application is that only one part of each measurement is related
   to network performance.  The rest is related to application
   performance, e.g., video coding, or even device capabilities, both of
   which are irrelevant to our purposes here and are generally hard to
   separate.  We therefore see the use of QoE metrics in measuring ICN
   performance as a poor choice at this stage.

2.3.2.  System Metrics

   Overall system metrics that need to be considered include
   reliability, scalability, energy efficiency, and delay/disconnection
   tolerance.  In deployments where ICN is addressing specific
   scenarios, relevant system metrics could be derived from current
   experience.  For example, in Internet of Things (IoT) scenarios,
   which are discussed in [RFC7476], it is reasonable to consider the
   current generation of sensor nodes, sources of information, and even
   measurement gateways (e.g., for smart metering at homes) or
   smartphones.  In this case, ICN operation ought to be evaluated with
   respect not only to overall scalability and network efficiency, but

   also the impact on the nodes themselves.  Karnouskos et al.
   [SensReqs] provide a comprehensive set of sensor and IoT-related
   requirements, for example, which include aspects such as resource
   utilization, service life-cycle management, and device management.

   Additionally, various specific metrics are also critical in
   constrained environments, such as processing requirements, signaling
   overhead, and memory allocation for caching procedures, in addition
   to power consumption and battery lifetime.  For gateways (which
   typically act as a point of service to a large number of nodes and
   have to satisfy the information requests from remote entities), we
   need to consider scalability-related metrics, such as frequency and
   processing of successfully satisfied information requests.

   Finally, given the in-network caching functionality of ICNs,
   efficiency and performance metrics of in-network caching have to be
   defined.  Such metrics will need to guide researchers and operators
   regarding the performance of in-network caching algorithms.  A first
   step on this direction has been made in [Psaras11].  The paper
   proposes a formula that approximates the proportion of time that a
   piece of content stays in a network cache.  The model takes as input
   the rate of requests for a given piece of content (the Content of
   Interest (CoI)) and the rate of requests for all other contents that
   go through the given network element (router) and move the CoI down
   in the (LRU) cache.  The formula takes also into account the size of
   the cache of this router.

   The output of the model essentially reflects the probability that the
   CoI will be found in a given cache.  An initial study [Psaras11] is
   applied to the CCN / Named Data Networking (NDN) framework, where
   contents get cached at every node they traverse.  The formula
   according to which the probability or proportion is calculated is
   given by:

   pi = [mu / (mu + lambda)]^N

   where lambda is the request rate for the CoI, mu is the request rate
   for contents that move the CoI down the cache, and N is the size of
   the cache (in slots).

   The formula can be used to assess the caching performance of the
   system and can also potentially be used to identify the gain of the
   system due to caching.  This can then be used to compare against
   gains by other factors, e.g., addition of extra bandwidth in the
   network.

2.4.  Resource Equivalence and Trade-Offs

   As we have seen above, every ICN network is built from a set of
   resources, which include link capacities, and different types of
   memory structures and repositories used for storing named data
   objects and chunks temporarily (i.e., caching) or persistently, as
   well as name resolution and other lookup services.  A range of
   engineering trade-offs arise from the complexity and processing
   requirements of forwarding decisions, management needs (e.g., manual
   configuration, explicit garbage collection), and routing needs (e.g.,
   amount of state, manual configuration of routing tables, support for
   mobility).

   In order to be able to compare different ICN approaches, it would be
   beneficial to be able to define equivalence in terms of different
   resources that today are considered incomparable.  For example, would
   provisioning an additional 5 Mbit/s link capacity lead to better
   performance than adding 100 GB of in-network storage?  Within this
   context, one would consider resource equivalence (and the associated
   trade-offs) -- for example, for cache hit ratios per GB of cache,
   forwarding decision times, CPU cycles per forwarding decision, and so
   on.

3.  ICN Security Aspects

   The introduction of an information-centric networking architecture
   and the corresponding communication paradigm results in changes to
   many aspects of network security.  These will affect all scenarios
   described in [RFC7476].  Additional evaluation will be required to
   ensure relevant security requirements are appropriately met by the
   implementation of the chosen architecture in the various scenarios.

   The ICN security aspects described in this document reflect the ICN
   security challenges outlined in [RFC7927].

   The ICN architectures currently proposed have concentrated on
   authentication of delivered content to ensure its integrity.  Even
   though the approaches are primarily applicable to freely accessible
   content that does not require access authorization, they will
   generally support delivery of encrypted content.

   The introduction of widespread caching mechanisms may also provide
   additional attack surfaces.  The caching architecture to be used also
   needs to be evaluated to ensure that it meets the requirements of the
   usage scenarios.

   In practice, the work on security in the various ICN research
   projects has been heavily concentrated on authentication of content.
   Work on authorization, access control, and privacy and security
   threats due to the expanded role of in-network caches has been quite
   limited.  For example, a roadmap for improving the security model in
   NetInf can be found in [Renault09].  As secure communications on the
   Internet are becoming the norm, major gaps in ICN security aspects
   are bound to undermine the adoption of ICN.  A comprehensive overview
   of ICN security is also provided in [Tourani16].

   In the following subsections, we briefly consider the issues and
   provide pointers to the work that has been done on the security
   aspects of the architectures proposed.

3.1.  Authentication

   For fully secure content distribution, content access requires that
   the receiver be able to reliably assess:

      validity:   Is it a complete, uncorrupted copy of what was
                  originally published?

      provenance: Can the receiver identify the publisher? If so, can it
                  and the source of any cached version of the document
                  be adequately trusted?

      relevance:  Is the content an answer to the question that the
                  receiver asked?

   All ICN architectures considered in this document primarily target
   the validity requirement using strong cryptographic means to tie the
   content request name to the content.  Provenance and relevance are
   directly targeted to varying extents:  There is a tussle or trade-off
   between simplicity and efficiency of access and level of assurance of
   all these traits.  For example, maintaining provenance information
   can become extremely costly, particularly when considering (historic)
   relationships between multiple objects.  Architectural decisions have
   therefore been made in each case as to whether the assessment is
   carried out by the information-centric network or left to the
   application.

   An additional consideration for authentication is whether a name
   should be irrevocably and immutably tied to a static piece of
   preexisting content or whether the name can be used to refer to
   dynamically or subsequently generated content.  Schemes that only
   target immutable content can be less resource-hungry as they can use
   digest functions rather than public key cryptography for generating
   and checking signatures.  However, this can increase the load on

   applications because they are required to manage many names, rather
   than use a single name for an item of evolving content that changes
   over time (e.g., a piece of data containing an age reference).

   Data-Oriented Network Architecture (DONA) [DONA] and CCN [Jacobson09]
   [Smetters09] integrate most of the data needed to verify provenance
   into all content retrievals but need to be able to retrieve
   additional information (typically a security certificate) in order to
   complete the provenance authentication.  Whether the application has
   any control of this extra retrieval will depend on the
   implementation.  CCN is explicitly designed to handle dynamic content
   allowing names to be pre-allocated and attached to subsequently
   generated content.  DONA offers variants for dynamic and immutable
   content.

   Publish-Subscribe Internet Technology (PURSUIT) [Tagger12] appears to
   allow implementers to choose the authentication mechanism so that it
   can, in theory, emulate the authentication strategy of any of the
   other architectures.  It is not clear whether different choices would
   lead to lack of interoperability.

   NetInf uses the Named Information (ni) URI scheme [RFC6920] to
   identify content.  This allows NetInf to assure validity without any
   additional information but gives no assurance on provenance or
   relevance.  A "search" request allows an application to identify
   relevant content, and applications may choose to structure content to
   allow provenance assurance, but this will typically require
   additional network access.  NetInf validity authentication is
   consequently efficient in a network environment with intermittent
   connectivity as it does not force additional network accesses and
   allows the application to decide on provenance validation if
   required.  For dynamic content, NetInf can use, e.g., signed
   manifests.  For more details on NetInf security, see [Dannewitz10].

3.2.  Authorization, Access Control, and Logging

   A potentially major concern for all ICN architectures considered here
   is that they do not provide any inbuilt support for an authorization
   framework or for logging.  Once content has been published and cached
   in servers, routers, or endpoints not controlled by the publisher,
   the publisher has no way to enforce access control, determine which
   users have accessed the content, or revoke its publication.  In fact,
   in some cases (where requests do not necessarily contain host/user
   identifier information), it is difficult for the publishers
   themselves to perform access control.

   Access could be limited by encrypting the content, but the necessity
   of distributing keys out-of-band appears to negate the advantages of
   in-network caching.  This also creates significant challenges when
   attempting to manage and restrict key access.  An authorization
   delegation scheme has been proposed [Fotiou12].  This scheme allows
   semi-trusted entities (such as caches or CDN nodes) to delegate
   access control decisions to third-party access control providers that
   are trusted by the content publisher.  The former entities have no
   access to subscriber-related information and should respect the
   decisions of the access control providers.

   A recent proposal for an extra layer in the protocol stack [LIRA]
   gives control of the name resolution infrastructure to the publisher.
    This enables access logging as well some degree of active cache
   management, e.g., purging of stale content.

   One possible technique that could allow for providing access control
   to heterogeneous groups and still allow for a single encrypted object
   representation that remains cacheable is Attribute-Based Encryption
   (ABE).  A first proposal for this is presented in [Ion13].  To
   support heterogeneous groups and avoid having a single authority that
   has a master key multi-authority, ABE can be used [Lewko11].

   Evaluating the impact of the absence of these features will be
   essential for any scenario where an ICN architecture might be
   deployed.  It may have a seriously negative impact on the
   applicability of ICN in commercial environments unless a solution can
   be found.

3.3.  Privacy

   Another area where the architectures have not been significantly
   analyzed is privacy.  Caching implies a trade-off between network
   efficiency and privacy.  The activity of users is significantly more
   exposed to the scrutiny of cache owners with whom they may not have
   any relationship.  However, it should be noted that it is only the
   first-hop router/cache that can see who requests what, as requests
   are aggregated and only the previous-hop router is visible when a
   request is forwarded.

   Although in many ICN architectures the source of a request is not
   explicitly identified, an attacker may be able to obtain considerable
   information if he or she can monitor transactions on the cache and
   obtain details of the objects accessed, the topological direction of
   requests, and information about the timing of transactions.  The
   persistence of data in the cache can make life easier for an attacker
   by giving a longer timescale for analysis.

   The impact of CCN on privacy has been investigated in [Lauinger10],
   and the analysis is applicable to all ICN architectures because it is
   mostly focused on the common caching aspect.  The privacy risks of
   Named Data Networking are also highlighted in [Lauinger12].  Further
   work on privacy in ICNs can be found in [Chaabane13].  Finally,
   Fotiou et al. define an ICN privacy evaluation framework in
   [Fotiou14].

3.4.  Changes to the Network Security Threat Model

   The architectural differences of the various ICN models versus TCP/IP
   have consequences for network security.  There is limited
   consideration of the threat models and potential mitigation in the
   various documents describing the architectures.  [Lauinger10] and
   [Chaabane13] also consider the changed threat model.  Some of the key
   aspects are:

      o  Caching implies a trade-off between network efficiency and user
         privacy as discussed in Section 3.3.

      o  More-powerful routers upgraded to handle persistent caching
         increase the network's attack surface.  This is particularly
         the case in systems that may need to perform cryptographic
         checks on content that is being cached.  For example, not doing
         this could lead routers to disseminate invalid content.

      o  ICNs makes it difficult to identify the origin of a request (as
         mentioned in Section 3.3), slowing down the process of blocking
         requests and requiring alternative mechanisms to differentiate
         legitimate requests from inappropriate ones as access control
         lists (ACLs) will probably be of little value for ICN requests.

      o  Denial-of-service (DoS) attacks may require more effort on ICN
         than on TCP/IP-based host-centric networks, but they are still
         feasible.  One reason for this is that it is difficult for the
         attacker to force repeated requests for the same content onto a
         single node; ICNs naturally spread content so that after the
         initial few requests, subsequent requests will generally be
         satisfied by alternative sources, blunting the impact of a DoS
         attack.  That said, there are many ways around this, e.g.,
         generating random suffix identifiers that always result in
         cache misses.

      o  Per-request state in routers can be abused for DoS attacks.

      o  Caches can be misused in the following ways:

         +  Attackers can use caches as storage to make their own
            content available.

         +  The efficiency of caches can be decreased by attackers with
            the goal of DoS attacks.

         +  Content can be extracted by any attacker connected to the
            cache, putting users' privacy at risk.

   Appropriate mitigation of these threats will need to be considered in
   each scenario.

4.  Evaluation Tools

   Since ICN is an emerging area, the community is in the process of
   developing effective evaluation environments, including releasing
   open-source implementations, simulators, emulators, and testbeds.  To
   date, none of the available evaluation tools can be seen as the one
   and only community reference evaluation tool.  Furthermore, no single
   environment supports all well-known ICN approaches, as we describe
   below, hindering the direct comparison of the results obtained for
   different ICN approaches.  The subsections that follow review the
   currently publicly available ICN implementations, simulators, and
   experimental facilities.

   An updated list of the available evaluation tools will be maintained
   at the ICNRG Wiki page: <https://trac.tools.ietf.org/group/irtf/trac/
   wiki/IcnEvaluationAndTestbeds>

4.1.  Open-Source Implementations

   The Named Data Networking (NDN) project has open-sourced a software
   reference implementation of the architecture and protocol called NDN
   (http://named-data.net).  NDN is available for deployment on various
   operating systems and includes C and Java libraries that can be used
   to build applications.

   CCN-lite (http://www.ccn-lite.net) is a lightweight implementation of
   the CCN protocol that supports most of the key features of CCNx and
   is interoperable with CCNx.  CCN-lite implements the core CCN logic
   in about 1000 lines of code, so it is ideal for classroom work and
   course projects as well as for quickly experimenting with CCN
   extensions.  For example, Baccelli et al. use CCN-lite on top of the
   RIOT operating system to conduct experiments over an IoT testbed
   [Baccelli14].

   PARC is offering CCN source code under various licensing schemes,
   please see <http://www.ccnx.org> for details.

   The PURSUIT project (http://www.fp7-pursuit.eu) has open-sourced its
   Blackhawk publish-subscribe (Pub/Sub) implementation for Linux and
   Android; more details are available at
   <https://github.com/fp7-pursuit/blackadder>.  Blackadder uses the
   Click modular router for ease of development.  The code distribution
   features a set of tools, test applications, and scripts.  The POINT
   project (http://www.point-h2020.eu) is currently maintaining
   Blackadder.

   The 4WARD and SAIL projects have open-sourced software that
   implements different aspects of NetInf, e.g., the NetInf URI format
   and HTTP and UDP convergence layer, using different programming
   languages.  The Java implementation provides a local caching proxy
   and client.  Further, an OpenNetInf prototype is available as well as
   a hybrid host-centric and information-centric network architecture
   called the Global Information Network (GIN), a browser plug-in and
   video-streaming software.  See <http://www.netinf.org/open-source>
   for more details.

4.2.  Simulators and Emulators

   Simulators and emulators should be able to capture faithfully all
   features and operations of the respective ICN architecture(s) and any
   limitations should be openly documented.  It is essential that these
   tools and environments come with adequate logging facilities so that
   one can use them for in-depth analysis as well as debugging.
   Additional requirements include the ability to support medium- to
   large-scale experiments, the ability to quickly and correctly set
   various configurations and parameters, as well as to support the
   playback of traffic traces captured on a real testbed or network.
   Obviously, this does not even begin to touch upon the need for strong
   validation of any evaluated implementations.

4.2.1.  ndnSIM

   The Named Data Networking (NDN) project (http://named-data.net) has
   developed ndnSIM [ndnSIM] [ndnSIM2]; this is a module that can be
   plugged into the ns-3 simulator (https://www.nsnam.org) and supports
   the core features of NDN.  One can use ndnSIM to experiment with
   various NDN applications and services as well as components developed
   for NDN such as routing protocols and caching and forwarding
   strategies, among others.  The code for ns-3 and ndnSIM is openly
   available to the community and can be used as the basis for
   implementing ICN protocols or applications.  For more details, see
   <http://ndnsim.net/2.0/>.

4.2.2.  ccnSIM

   ccnSim [ccnSim] is a CCN-specific simulator that was specially
   designed to handle forwarding of a large number of CCN-chunks
   (http://www.infres.enst.fr/~drossi/index.php?n=Software.ccnSim).
   ccnSim is written in C++ for the OMNeT++ simulation framework
   (https://omnetpp.org).  Other CCN-specific simulators include the CCN
   Packet-Level Simulator [CCNPL] and CCN-Joker [Cianci12].  CCN-Joker
   emulates in user space all basic aspects of a CCN node (e.g.,
   handling of Interest and Data packets, cache sizing, replacement
   policies), including both flow and congestion control.  The code is
   open source and is suitable for both emulation-based analyses and
   real experiments.  Finally, Cabral et al. [MiniCCNx] use container-
   based emulation and resource isolation techniques to develop a
   prototyping and emulation tool.

4.2.3.  Icarus Simulator

   The Icarus simulator [ICARUS] focuses on caching in ICN and is
   agnostic with respect to any particular ICN implementation.  The
   simulator is implemented in Python, uses the Fast Network Simulator
   Setup tool [Saino13], and is available at
   <http://icarus-sim.github.io>.  Icarus has several caching strategies
   implemented, including among others ProbCache [Psaras12], node-
   centrality-based caching [Chai12], and hash-route-based caching
   [HASHROUT].

   ProbCache [Psaras12] is taking a resource management view on caching
   decisions and approximates the available cache capacity along the
   path from source to destination.  Based on this approximation and in
   order to reduce caching redundancy across the path, it caches content
   probabilistically.  According to [Chai12], the node with the highest
   "betweenness centrality" along the path from source to destination is
   responsible for caching incoming content.  Finally, [HASHROUT]
   calculates the hash function of a content's name and assigns contents
   to caches of a domain according to that.  The hash space is split
   according to the number of caches of the network.  Then, upon
   subsequent requests, and based again on the hash of the name included
   in the request, edge routers redirect requests to the cache assigned
   with the corresponding hash space.  [HASHROUT] is an off-path caching
   strategy; in contrast to [Psaras12] and [Chai12], it requires minimum
   coordination and redirection overhead.  In its latest update, Icarus
   also includes implementation of the "Satisfied Interest Table" (SIT)
   [Sourlas15].  The SIT points in the direction where content has been
   sent recently.  Among other benefits, this enables information
   resilience in case of network fragmentation (i.e., content can still

   be found in neighbor caches or in users' devices) and inherently
   supports user-assisted caching (i.e., P2P-like content distribution).

   Tortelli et al. [ICNSIMS] provide a comparison of ndnSIM, ccnSim, and
   Icarus.

4.3.  Experimental Facilities

   An important consideration in the evaluation of any kind of future
   Internet mechanism lies in the characteristics of that evaluation
   itself.  Central to the assessment of the features provided by a
   novel mechanism is the consideration of how it improves over already
   existing technologies, and by "how much".  With the disruptive nature
   of clean-slate approaches generating new and different technological
   requirements, it is complex to provide meaningful results for a
   network-layer framework, in comparison with what is deployed in the
   current Internet.  Thus, despite the availability of ICN
   implementations and simulators, the need for large-scale environments
   supporting experimental evaluation of novel research is of prime
   importance to the advancement of ICN deployment.

   Different experimental facilities have different characteristics and
   capabilities, e.g., having low cost of use, reproducible
   configuration, easy-to-use tools, and available background traffic,
   and being sharable.

4.3.1.  Open Network Lab (ONL)

   An example of an experimental facility that supports CCN is the Open
   Network Lab [ONL] that currently comprises 18 extensible gigabit
   routers and over a 100 computers representing clients and is freely
   available to the public for running CCN experiments.  Nodes in ONL
   are preloaded with CCNx software.  ONL provides a graphical user
   interface for easy configuration and testbed setup as per the
   experiment requirements, and also serves as a control mechanism,
   allowing access to various control variables and traffic counters.

   Further, it is also possible to run and evaluate CCN over popular
   testbeds [PLANETLAB] [EMULAB] [DETERLAB] [OFELIA] by directly
   running, for example, the CCNx open-source code [Salsano13]
   [Carofiglio13] [Awiphan13] [Bernardini14].  Also, the Network
   Experimentation Programming Interface (NEPI) [NEPI] is a tool
   developed for controlling and managing large-scale network
   experiments.  NEPI can be used to control and manage large-scale CCNx
   experiments, e.g., on PlanetLab [Quereilhac14].

4.3.2.  POINT Testbed

   The POINT project is maintaining a testbed with 40 machines across
   Europe, North America (Massachusetts Institute of Technology (MIT)),
   and Japan (National Institute of Information and Communications
   Technology (NICT)) interconnected in a topology containing one
   Topology Manager and one rendezvous node that handle all
   publish/subscribe and topology formation requests [Parisis13].  All
   machines run Blackadder (see Section 4.1).  New nodes can join, and
   experiments can be run on request.

4.3.3.  CUTEi: Container-Based ICN Testbed

   NICT has also developed a testbed used for ICN experiments [Asaeda14]
   comprising multiple servers located in Asia and other locations.
   Each testbed server (or virtual machine) utilizes a Linux kernel-
   based container (LXC) for node virtualization.  This testbed enables
   users to run applications and protocols for ICN in two
   experimentation modes using two different container designs:

      1.  application-level experimentation using a "common container"
          and

      2.  network-level experimentation using a "user container."

   A common container is shared by all testbed users, and a user
   container is assigned to one testbed user.  A common container has a
   global IP address to connect with other containers or external
   networks, whereas each user container uses a private IP address and a
   user space providing a closed networking environment.  A user can
   login to his/her user containers using SSH with his/her certificate,
   or access them from PCs connected to the Internet using SSH
   tunneling.

   This testbed also implements an "on-filesystem cache" to allocate
   caching data on a UNIX filesystem.  The on-filesystem cache system
   accommodates two kinds of caches: "individual cache" and "shared
   cache."  Individual cache is accessible for one dedicated router for
   the individual user, while shared cache is accessible for a set of
   routers in the same group to avoid duplicated caching in the
   neighborhood for cooperative caching.

5.  Security Considerations

   This document does not impact the security of the Internet, but
   Section 3 outlines security and privacy concerns that might affect a
   deployment of a future ICN approach.

6.  Informative References

   [4WARD6.1] Ohlman, B., et al., "First NetInf Architecture
              Description", 4WARD Project Deliverable D-6.1, April 2009.

   [4WARD6.3] Ahlgren, B., et al., "NetInf Evaluation", 4WARD Project
              Deliverable D-6.3, June 2010.

   [Arlitt97] Arlitt, M. and C. Williamson, "Internet web servers:
              workload characterization and performance implications",
              IEEE/ACM Transactions on Networking, vol. 5, pp. 631-645,
              DOI 10.1109/90.649565, 1997.

   [Asaeda14] Asaeda, H., Li, R., and N. Choi, "Container-Based Unified
              Testbed for Information-Centric Networking", IEEE Network,
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Acknowledgments

   Konstantinos Katsaros contributed the updated text of Section 2.2
   along with an extensive set of references.

   Priya Mahadevan, Daniel Corujo, and Gareth Tyson contributed to a
   draft version of this document.

   This document has benefited from reviews, pointers to the growing ICN
   literature, suggestions, comments, and proposed text provided by the
   following members of the IRTF Information-Centric Networking Research
   Group (ICNRG), listed in alphabetical order: Marica Amadeo, Hitoshi
   Asaeda, E. Baccelli, Claudia Campolo, Christian Esteve Rothenberg,
   Suyong Eum, Nikos Fotiou, Dorothy Gellert, Luigi Alfredo Grieco,
   Myeong-Wuk Jang, Ren Jing, Will Liu, Antonella Molinaro, Luca
   Muscariello, Ioannis Psaras, Dario Rossi, Stefano Salsano, Damien
   Saucez, Dirk Trossen, Jianping Wang, Yuanzhe Xuan, and Xinwen Zhang.

   The IRSG review was provided by Aaron Falk.

Authors' Addresses

   Kostas Pentikousis (editor)
   Travelping
   Koernerstr. 7-10
   10785 Berlin
   Germany

   Email: k.pentikousis@travelping.com

   Borje Ohlman
   Ericsson Research
   S-16480 Stockholm
   Sweden

   Email: Borje.Ohlman@ericsson.com

   Elwyn Davies
   Trinity College Dublin/Folly Consulting Ltd
   Dublin, 2
   Ireland

   Email: davieseb@scss.tcd.ie

   Spiros Spirou
   Intracom Telecom
   19.7 km Markopoulou Avenue
   19002 Peania, Athens
   Greece

   Email: spis@intracom-telecom.com

   Gennaro Boggia
   Dept. of Electrical and Information Engineering
   Politecnico di Bari
   Via Orabona 4
   70125 Bari
   Italy

   Email: g.boggia@poliba.it

 

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