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RFC 3439 - Some Internet Architectural Guidelines and Philosophy


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Network Working Group                                            R. Bush
Request for Comments: 3439                                      D. Meyer
Updates: 1958                                              December 2002
Category: Informational

         Some Internet Architectural Guidelines and Philosophy

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.

Copyright Notice

   Copyright (C) The Internet Society (2002).  All Rights Reserved.

Abstract

   This document extends RFC 1958 by outlining some of the philosophical
   guidelines to which architects and designers of Internet backbone
   networks should adhere.  We describe the Simplicity Principle, which
   states that complexity is the primary mechanism that impedes
   efficient scaling, and discuss its implications on the architecture,
   design and engineering issues found in large scale Internet
   backbones.

Table of Contents

   1. Introduction . . . . . . . . . . . . . . . . . . . . . . . .  2
   2. Large Systems and The Simplicity Principle . . . . . . . . .  3
   2.1. The End-to-End Argument and Simplicity   . . . . . . . . .  3
   2.2. Non-linearity and Network Complexity   . . . . . . . . . .  3
   2.2.1. The Amplification Principle. . . . . . . . . . . . . . .  4
   2.2.2. The Coupling Principle . . . . . . . . . . . . . . . . .  5
   2.3. Complexity lesson from voice. . . . .  . . . . . . . . . .  6
   2.4. Upgrade cost of complexity. . . . . .  . . . . . . . . . .  7
   3. Layering Considered Harmful. . . . . . . . . . . . . . . . .  7
   3.1. Optimization Considered Harmful . . .  . . . . . . . . . .  8
   3.2. Feature Richness Considered Harmful .  . . . . . . . . . .  9
   3.3. Evolution of Transport Efficiency for IP.  . . . . . . . .  9
   3.4. Convergence Layering. . . . . . . . . . .  . . . . . . . .  9
   3.4.1. Note on Transport Protocol Layering. . . . . . . . . . . 11
   3.5. Second Order Effects   . . . . . . . . . . . . . . . . . . 11
   3.6. Instantiating the EOSL Model with IP   . . . . . . . . . . 12
   4. Avoid the Universal Interworking Function. . . . . . . . . . 12
   4.1. Avoid Control Plane Interworking . . . . . . . . . . . . . 13

   5. Packet versus Circuit Switching: Fundamental Differences . . 13
   5.1. Is PS is inherently more efficient than CS?  . . . . . . . 13
   5.2. Is PS simpler than CS? . . . . . . . . . . . . . . . . . . 14
   5.2.1. Software/Firmware Complexity . . . . . . . . . . . . . . 15
   5.2.2. Macro Operation Complexity . . . . . . . . . . . . . . . 15
   5.2.3. Hardware Complexity. . . . . . . . . . . . . . . . . . . 15
   5.2.4. Power. . . . . . . . . . . . . . . . . . . . . . . . . . 16
   5.2.5. Density. . . . . . . . . . . . . . . . . . . . . . . . . 16
   5.2.6. Fixed versus variable costs. . . . . . . . . . . . . . . 16
   5.2.7. QoS. . . . . . . . . . . . . . . . . . . . . . . . . . . 17
   5.2.8. Flexibility. . . . . . . . . . . . . . . . . . . . . . . 17
   5.3. Relative Complexity  . . . . . . . . . . . . . . . . . . . 17
   5.3.1. HBHI and the OPEX Challenge. . . . . . . . . . . . . . . 18
   6. The Myth of Over-Provisioning. . . . . . . . . . . . . . . . 18
   7. The Myth of Five Nines . . . . . . . . . . . . . . . . . . . 19
   8. Architectural Component Proportionality Law. . . . . . . . . 20
   8.1. Service Delivery Paths . . . . . . . . . . . . . . . . . . 21
   9. Conclusions. . . . . . . . . . . . . . . . . . . . . . . . . 21
   10. Security Considerations . . . . . . . . . . . . . . . . . . 22
   11. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . 23
   12. References. . . . . . . . . . . . . . . . . . . . . . . . . 23
   13. Authors' Addresses. . . . . . . . . . . . . . . . . . . . . 27
   14. Full Copyright Statement. . . . . . . . . . . . . . . . . . 28

1.  Introduction

   RFC 1958 [RFC1958] describes the underlying principles of the
   Internet architecture.  This note extends that work by outlining some
   of the philosophical guidelines to which architects and designers of
   Internet backbone networks should adhere.  While many of the areas
   outlined in this document may be controversial, the unifying
   principle described here, controlling complexity as a mechanism to
   control costs and reliability, should not be.  Complexity in carrier
   networks can derive from many sources.  However, as stated in
   [DOYLE2002], "Complexity in most systems is driven by the need for
   robustness to uncertainty in their environments and component parts
   far more than by basic functionality".  The major thrust of this
   document, then, is to raise awareness about the complexity of some of
   our current architectures, and to examine the effect such complexity
   will almost certainly have on the IP carrier industry's ability to
   succeed.

   The rest of this document is organized as follows: The first section
   describes the Simplicity Principle and its implications for the
   design of very large systems.  The remainder of the document outlines
   the high-level consequences of the Simplicity Principle and how it
   should guide large scale network architecture and design approaches.

2.  Large Systems and The Simplicity Principle

   The Simplicity Principle, which was perhaps first articulated by Mike
   O'Dell, former Chief Architect at UUNET, states that complexity is
   the primary mechanism which impedes efficient scaling, and as a
   result is the primary driver of increases in both capital
   expenditures (CAPEX) and operational expenditures (OPEX).  The
   implication for carrier IP networks then, is that to be successful we
   must drive our architectures and designs toward the simplest possible
   solutions.

2.1.  The End-to-End Argument and Simplicity

   The end-to-end argument, which is described in [SALTZER] (as well as
   in RFC 1958 [RFC1958]), contends that "end-to-end protocol design
   should not rely on the maintenance of state (i.e., information about
   the state of the end-to-end communication) inside the network.  Such
   state should be maintained only in the end points, in such a way that
   the state can only be destroyed when the end point itself breaks."
   This property has also been related to Clark's "fate-sharing" concept
   [CLARK].  We can see that the end-to-end principle leads directly to
   the Simplicity Principle by examining the so-called "hourglass"
   formulation of the Internet architecture [WILLINGER2002].  In this
   model, the thin waist of the hourglass is envisioned as the
   (minimalist) IP layer, and any additional complexity is added above
   the IP layer.  In short, the complexity of the Internet belongs at
   the edges, and the IP layer of the Internet should remain as simple
   as possible.

   Finally, note that the End-to-End Argument does not imply that the
   core of the Internet will not contain and maintain state.  In fact, a
   huge amount coarse grained state is maintained in the Internet's core
   (e.g., routing state).  However, the important point here is that
   this (coarse grained) state is almost orthogonal to the state
   maintained by the end-points (e.g., hosts).  It is this minimization
   of interaction that contributes to simplicity.  As a result,
   consideration of "core vs. end-point" state interaction is crucial
   when analyzing protocols such as Network Address Translation (NAT),
   which reduce the transparency between network and hosts.

2.2.  Non-linearity and Network Complexity

   Complex architectures and designs have been (and continue to be)
   among the most significant and challenging barriers to building cost-
   effective large scale IP networks.  Consider, for example, the task
   of building a large scale packet network.  Industry experience has
   shown that building such a network is a different activity (and hence
   requires a different skill set) than building a small to medium scale

   network, and as such doesn't have the same properties.  In
   particular, the largest networks exhibit, both in theory and in
   practice, architecture, design, and engineering non-linearities which
   are not exhibited at smaller scale.  We call this Architecture,
   Design, and Engineering (ADE) non-linearity.  That is, systems such
   as the Internet could be described as highly self-dissimilar, with
   extremely different scales and levels of abstraction [CARLSON].  The
   ADE non-linearity property is based upon two well-known principles
   from non-linear systems theory [THOMPSON]:

2.2.1.  The Amplification Principle

   The Amplification Principle states that there are non-linearities
   which occur at large scale which do not occur at small to medium
   scale.

   COROLLARY: In many large networks, even small things can and do cause
   huge events.  In system-theoretic terms, in large systems such as
   these, even small perturbations on the input to a process can
   destabilize the system's output.

   An important example of the Amplification Principle is non-linear
   resonant amplification, which is a powerful process that can
   transform dynamic systems, such as large networks, in surprising ways
   with seemingly small fluctuations.  These small fluctuations may
   slowly accumulate, and if they are synchronized with other cycles,
   may produce major changes.  Resonant phenomena are examples of non-
   linear behavior where small fluctuations may be amplified and have
   influences far exceeding their initial sizes.  The natural world is
   filled with examples of resonant behavior that can produce system-
   wide changes, such as the destruction of the Tacoma Narrows bridge
   (due to the resonant amplification of small gusts of wind).  Other
   examples include the gaps in the asteroid belts and rings of Saturn
   which are created by non-linear resonant amplification.  Some
   features of human behavior and most pilgrimage systems are influenced
   by resonant phenomena involving the dynamics of the solar system,
   such as solar days, the 27.3 day (sidereal) and 29.5 day (synodic)
   cycles of the moon or the 365.25 day cycle of the sun.

   In the Internet domain, it has been shown that increased inter-
   connectivity results in more complex and often slower BGP routing
   convergence [AHUJA].  A related result is that a small amount of
   inter-connectivity causes the output of a routing mesh to be
   significantly more complex than its input [GRIFFIN].  An important
   method for reducing amplification is ensure that local changes have
   only local effect (this is as opposed to systems in which local
   changes have global effect).  Finally, ATM provides an excellent
   example of an amplification effect: if you lose one cell, you destroy

   the entire packet (and it gets worse, as in the absence of mechanisms
   such as Early Packet Discard [ROMANOV], you will continue to carry
   the already damaged packet).

   Another interesting example of amplification comes from the
   engineering domain, and is described in [CARLSON].  They consider the
   Boeing 777, which is a "fly-by-wire" aircraft, containing as many as
   150,000 subsystems and approximately 1000 CPUs.  What they observe is
   that while the 777 is robust to large-scale atmospheric disturbances,
   turbulence boundaries, and variations in cargo loads (to name a few),
   it could be catastrophically disabled my microscopic alterations in a
   very few large CPUs (as the point out, fortunately this is a very
   rare occurrence).  This example illustrates the issue "that
   complexity can amplify small perturbations, and the design engineer
   must ensure such perturbations are extremely rare." [CARLSON]

2.2.2.  The Coupling Principle

   The Coupling Principle states that as things get larger, they often
   exhibit increased interdependence between components.

   COROLLARY: The more events that simultaneously occur, the larger the
   likelihood that two or more will interact.  This phenomenon has also
   been termed "unforeseen feature interaction" [WILLINGER2002].

   Much of the non-linearity observed large systems is largely due to
   coupling.  This coupling has both  horizontal and vertical
   components.  In the context of networking, horizontal coupling is
   exhibited between the same protocol layer, while vertical coupling
   occurs between layers.

   Coupling is exhibited by a wide variety of natural systems, including
   plasma macro-instabilities (hydro-magnetic, e.g., kink, fire-hose,
   mirror, ballooning, tearing, trapped-particle effects) [NAVE], as
   well as various kinds of electrochemical systems (consider the custom
   fluorescent nucleotide synthesis/nucleic acid labeling problem
   [WARD]).  Coupling of clock physical periodicity has also been
   observed [JACOBSON], as well as coupling of various types of
   biological cycles.

   Several canonical examples also exist in well known network systems.
   Examples include the synchronization of various control loops, such
   as routing update synchronization and TCP Slow Start synchronization
   [FLOYD,JACOBSON].  An important result of these observations is that
   coupling is intimately related to synchronization.  Injecting
   randomness into these systems is one way to reduce coupling.

   Interestingly, in analyzing risk factors for the Public Switched
   Telephone Network (PSTN), Charles Perrow decomposes the complexity
   problem along two related axes, which he terms "interactions" and
   "coupling" [PERROW].  Perrow cites interactions and coupling as
   significant factors in determining the reliability of a complex
   system (and in particular, the PSTN).  In this model, interactions
   refer to the dependencies between components (linear or non-linear),
   while coupling refers to the flexibility in a system.  Systems with
   simple, linear interactions have components  that affect only other
   components that are functionally downstream.  Complex system
   components interact with many other components in different and
   possibly distant parts of the system.  Loosely coupled systems are
   said to have more flexibility in time constraints, sequencing, and
   environmental assumptions than do tightly coupled systems.  In
   addition, systems with complex interactions and tight coupling are
   likely to have unforeseen failure states (of course, complex
   interactions permit more complications to develop and make the system
   hard to understand and predict); this behavior is also described in
   [WILLINGER2002].  Tight coupling also means that the system has less
   flexibility in recovering from failure states.

   The PSTN's SS7 control network provides an interesting example of
   what can go wrong with a tightly coupled complex system.  Outages
   such as the well publicized 1991 outage of AT&T's SS7 demonstrates
   the phenomenon: the outage was caused by software bugs in the
   switches' crash recovery code.  In this case, one switch crashed due
   to a hardware glitch.  When this switch came back up, it (plus a
   reasonably probable timing event) caused its neighbors to crash When
   the neighboring switches came back up, they caused their neighbors to
   crash, and so on [NEUMANN] (the root cause turned out to be a
   misplaced 'break' statement; this is an excellent example of cross-
   layer coupling).  This phenomenon is similar to the phase-locking of
   weakly coupled oscillators, in which random variations in sequence
   times plays an important role in system stability [THOMPSON].

2.3.  Complexity lesson from voice

   In the 1970s and 1980s, the voice carriers competed by adding
   features which drove substantial increases in the complexity of the
   PSTN, especially in the Class 5 switching infrastructure.  This
   complexity was typically software-based, not hardware driven, and
   therefore had cost curves worse than Moore's Law.  In summary, poor
   margins on voice products today are due to OPEX and CAPEX costs not
   dropping as we might expect from simple hardware-bound
   implementations.

2.4.  Upgrade cost of complexity

   Consider the cost of providing new features in a complex network.
   The traditional voice network has little intelligence in its edge
   devices (phone instruments), and a very smart core.  The Internet has
   smart edges, computers with operating systems, applications, etc.,
   and a simple core, which consists of a control plane and packet
   forwarding engines.  Adding an new Internet service is just a matter
   of distributing an application to the a few consenting desktops who
   wish to use it.  Compare this to adding a service to voice, where one
   has to upgrade the entire core.

3.  Layering Considered Harmful

   There are several generic properties of layering, or vertical
   integration as applied to networking.  In general, a layer as defined
   in our context implements one or more of

    Error Control:     The layer makes the "channel" more reliable
                       (e.g., reliable transport layer)

    Flow Control:      The layer avoids flooding slower peer (e.g.,
                       ATM flow control)

    Fragmentation:     Dividing large data chunks into smaller
                       pieces, and subsequent reassembly (e.g., TCP
                       MSS fragmentation/reassembly)

    Multiplexing:      Allow several higher level sessions share
                       single lower level "connection" (e.g., ATM PVC)

    Connection Setup:  Handshaking with peer (e.g., TCP three-way
                       handshake, ATM ILMI)

    Addressing/Naming: Locating, managing identifiers associated
                       with entities (e.g., GOSSIP 2 NSAP Structure
                       [RFC1629])

   Layering of this type does have various conceptual and structuring
   advantages.  However, in the data networking context structured
   layering implies that the functions of each layer are carried out
   completely before the protocol data unit is passed to the next layer.
   This means that the optimization of each layer has to be done
   separately.  Such ordering constraints are in conflict with efficient
   implementation of data manipulation functions.  One could accuse the
   layered model (e.g., TCP/IP and ISO OSI) of causing this conflict.
   In fact, the operations of multiplexing and segmentation both hide
   vital information that lower layers may need to optimize their

   performance.  For example, layer N may duplicate lower level
   functionality, e.g., error recovery hop-hop versus end-to-end error
   recovery.  In addition, different layers may need the same
   information (e.g., time stamp): layer N may need layer N-2
   information (e.g., lower layer packet sizes), and the like [WAKEMAN].
   A related and even more ironic statement comes from Tennenhouse's
   classic paper, "Layered Multiplexing Considered Harmful"
   [TENNENHOUSE]: "The ATM approach to broadband networking is presently
   being pursued within the CCITT (and elsewhere) as the unifying
   mechanism for the support of service integration, rate adaptation,
   and jitter control within the lower layers of the network
   architecture.  This position paper is specifically concerned with the
   jitter arising from the design of the "middle" and "upper" layers
   that operate within the end systems and relays of multi-service
   networks (MSNs)."

   As a result of inter-layer dependencies, increased layering can
   quickly lead to violation of the Simplicity Principle.  Industry
   experience has taught us that increased layering frequently increases
   complexity and hence leads to increases in OPEX, as is predicted by
   the Simplicity Principle.  A corollary is stated in RFC 1925
   [RFC1925], section 2(5):

      "It is always possible to agglutinate multiple separate problems
      into a single complex interdependent solution.  In most cases
      this is a bad idea."

   The first order conclusion then, is that horizontal (as opposed to
   vertical) separation may be more cost-effective and reliable in the
   long term.

3.1.  Optimization Considered Harmful

   A corollary of the layering arguments above is that optimization can
   also be considered harmful.  In particular, optimization introduces
   complexity, and as well as introducing tighter coupling between
   components and layers.

   An important and related effect of optimization is described by the
   Law of Diminishing Returns, which states that if one factor of
   production is increased while the others remain constant, the overall
   returns will relatively decrease after a certain point [SPILLMAN].
   The implication here is that trying to squeeze out efficiency past
   that point only adds complexity, and hence leads to less reliable
   systems.

3.2.  Feature Richness Considered Harmful

   While adding any new feature may be considered a gain (and in fact
   frequently differentiates vendors of various types of equipment), but
   there is a danger.  The danger is in increased system complexity.

3.3.  Evolution of Transport Efficiency for IP

   The evolution of transport infrastructures for IP offers a good
   example of how decreasing vertical integration has lead to various
   efficiencies.  In particular,

    | IP over ATM over SONET  -->
    | IP over SONET over WDM  -->
    | IP over WDM
    |
   \|/
   Decreasing complexity, CAPEX, OPEX

   The key point here is that layers are removed resulting in CAPEX and
   OPEX efficiencies.

3.4.  Convergence Layering

   Convergence is related to the layering concepts described above in
   that convergence is achieved via a "convergence layer".  The end
   state of the convergence argument is the concept of Everything Over
   Some Layer (EOSL).  Conduit, DWDM, fiber, ATM, MPLS, and even IP have
   all been proposed as convergence layers.  It is important to note
   that since layering typically drives OPEX up, we expect convergence
   will as well.  This observation is again consistent with industry
   experience.

   There are many notable examples of convergence layer failure.
   Perhaps the most germane example is IP over ATM.  The immediate and
   most obvious consequence of ATM layering is the so-called cell tax:
   First, note that the complete answer on ATM efficiency is that it
   depends upon packet size distributions.  Let's assume that typical
   Internet type traffic patterns, which tend to have high percentages
   of packets at 40, 44, and 552 bytes.  Recent data [CAIDA] shows that
   about 95% of WAN bytes and 85% of packets are TCP.  Much of this
   traffic is composed of 40/44 byte packets.

   Now, consider the case of a a DS3 backbone with PLCP turned on.  Then
   the maximum cell rate is 96,000 cells/sec.  If you multiply this
   value by the number of bits in the payload, you get: 96000 cells/sec
   * 48 bytes/cell * 8 = 36.864 Mbps.  This, however, is unrealistic
   since it

   assumes perfect payload packing.  There are two other things that
   contribute to the ATM overhead (cell tax): The wasted padding and the
   8 byte SNAP header.

   It is the SNAP header which causes most of the problems (and you
   can't do anything about this), forcing most small packets to consume
   two cells, with the second cell to be mostly empty padding (this
   interacts really poorly with the data quoted above, e.g., that most
   packets are 40-44 byte TCP Ack packets).  This causes a loss of about
   another 16% from the 36.8 Mbps ideal throughput.

   So the total throughput ends up being (for a DS3):

             DS3 Line Rate:              44.736
             PLCP Overhead              - 4.032
             Per Cell Header:           - 3.840
             SNAP Header & Padding:     - 5.900
                                       =========
                                         30.960 Mbps

   Result: With a DS3 line rate of 44.736 Mbps, the total overhead is
   about 31%.

   Another way to look at this is that since a large fraction of WAN
   traffic is comprised of TCP ACKs, one can make a different but
   related calculation.  IP over ATM requires:

             IP data (40 bytes in this case)
             8 bytes SNAP
             8 bytes AAL5 stuff
             5 bytes for each cell
             + as much more as it takes to fill out the last cell

   On ATM, this becomes two cells - 106 bytes to convey 40 bytes of
   information.  The next most common size seems to be one of several
   sizes in the 504-556 byte range - 636 bytes to carry IP, TCP, and a
   512 byte TCP payload - with messages larger than 1000 bytes running
   third.

   One would imagine that 87% payload (556 byte message size) is better
   than 37% payload (TCP Ack size), but it's not the 95-98% that
   customers are used to, and the predominance of TCP Acks skews the
   average.

3.4.1.  Note on Transport Protocol Layering

   Protocol layering models are frequently cast as "X over Y" models.
   In these cases, protocol Y carries protocol X's protocol data units
   (and possibly control data) over Y's data plane, i.e., Y is a
   "convergence layer".  Examples include Frame Relay over ATM, IP over
   ATM, and IP over MPLS.  While X over Y layering has met with only
   marginal success [TENNENHOUSE,WAKEMAN], there have been a few notable
   instances where efficiency can be and is gained.  In particular, "X
   over Y efficiencies" can be realized when there is a kind of
   "isomorphism" between the X and Y (i.e., there is a small convergence
   layer).  In these cases X's data, and possibly control traffic, are
   "encapsulated" and transported over Y.  Examples include Frame Relay
   over ATM, and Frame Relay, AAL5 ATM and Ethernet over L2TPv3
   [L2TPV3]; the simplifying factors here are that there is no
   requirement that a shared clock be recovered by the communicating end
   points, and that control-plane interworking is minimized.  An
   alternative is to interwork the X and Y's control and data planes;
   control-plane interworking is discussed below.

3.5.  Second Order Effects

   IP over ATM provides an excellent example of unanticipated second
   order effects.  In particular, Romanov and Floyd's classic study on
   TCP good-put [ROMANOV] on ATM showed that large UBR buffers (larger
   than one TCP window size) are required to achieve reasonable
   performance, that packet discard mechanisms (such as Early Packet
   Discard, or EPD) improve the effective usage of the bandwidth and
   that more elaborate service and drop strategies than FIFO+EPD, such
   as per VC queuing and accounting, might be required at the bottleneck
   to ensure both high efficiency and fairness.  Though all studies
   clearly indicate that a buffer size not less than one TCP window size
   is required, the amount of extra buffer required naturally depends on
   the packet discard mechanism used and is still an open issue.

   Examples of this kind of problem with layering abound in practical
   networking.  Consider, for example, the effect of IP transport's
   implicit assumptions of lower layers.  In particular:

    o Packet loss: TCP assumes that packet losses are indications of
      congestion, but sometimes losses are from corruption on a wireless
      link [RFC3115].

    o Reordered packets: TCP assumes that significantly reordered
      packets are indications of congestion.  This is not always the
      case [FLOYD2001].

    o Round-trip times: TCP measures round-trip times, and assumes that
      the lack of an acknowledgment within a period of time based on the
      measured round-trip time is a packet loss, and therefore an
      indication of congestion [KARN].

    o Congestion control: TCP congestion control implicitly assumes that
      all the packets in a flow are treated the same by the network, but
      this is not always the case [HANDLEY].

3.6.  Instantiating the EOSL Model with IP

   While IP is being proposed as a transport for almost everything, the
   base assumption, that Everything over IP (EOIP) will result in OPEX
   and CAPEX efficiencies, requires critical examination.  In
   particular, while it is the case that many protocols can be
   efficiently transported over an IP network (specifically, those
   protocols that do not need to recover synchronization between the
   communication end points, such as Frame Relay, Ethernet, and AAL5
   ATM), the Simplicity and Layering Principles suggest that EOIP may
   not represent the most efficient convergence strategy for arbitrary
   services.  Rather, a more CAPEX and OPEX efficient convergence layer
   might be much lower (again, this behavior is predicted by the
   Simplicity Principle).

   An example of where EOIP would not be the most OPEX and CAPEX
   efficient transport would be in those cases where a service or
   protocol needed SONET-like restoration times (e.g., 50ms).  It is not
   hard to imagine that it would cost more to build and operate an IP
   network with this kind of restoration and convergence property (if
   that were even possible) than it would to build the SONET network in
   the first place.

4.  Avoid the Universal Interworking Function

   While there have been many implementations of Universal Interworking
   unction (UIWF), IWF approaches have been problematic at large scale.
   his concern is codified in the Principle of Minimum Intervention
   BRYANT]:

   "To minimise the scope of information, and to improve the efficiency
   of data flow through the Encapsulation Layer, the payload should,
   where possible, be transported as received without modification."

4.1.  Avoid Control Plane Interworking

   This corollary is best understood in the context of the integrated
   solutions space.  In this case, the architecture and design
   frequently achieves the worst of all possible worlds.  This is due to
   the fact that such integrated solutions perform poorly at both ends
   of the performance/CAPEX/OPEX spectrum: the protocols with the least
   switching demand may have to bear the cost of the most expensive,
   while the protocols with the most stringent requirements often must
   make concessions to those with different requirements.  Add to this
   the various control plane interworking issues and you have a large
   opportunity for failure.  In summary, interworking functions should
   be restricted to data plane interworking and encapsulations, and
   these functions should be carried out at the edge of the network.

   As described above, interworking models have been successful in those
   cases where there is a kind of "isomorphism" between the layers being
   interworked.  The trade-off here, frequently described as the
   "Integrated vs.  Ships In the Night trade-off" has been examined at
   various times and  at various protocol layers.  In general, there are
   few cases in which such integrated solutions have proven efficient.
   Multi-protocol BGP [RFC2283] is a subtly different but notable
   exception.  In this case, the control plane is  independent of the
   format of the control data.  That is, no control plane data
   conversion is required, in contrast with control plane interworking
   models such as the ATM/IP interworking envisioned by some soft-switch
   manufacturers, and the so-called "PNNI-MPLS SIN" interworking
   [ATMMPLS].

5.  Packet versus Circuit Switching: Fundamental Differences

   Conventional wisdom holds that packet switching (PS) is inherently
   more efficient than circuit switching (CS), primarily because of the
   efficiencies that can be gained by statistical multiplexing and the
   fact that routing and forwarding decisions are made independently in
   a hop-by-hop fashion [[MOLINERO2002].  Further, it is widely assumed
   that IP is simpler that circuit switching, and hence should be more
   economical to deploy and manage [MCK2002].  However, if one examines
   these and related assumptions, a different picture emerges (see for
   example [ODLYZKO98]).  The following sections discuss these
   assumptions.

5.1.  Is PS is inherently more efficient than CS?

   It is well known that packet switches make efficient use of scarce
   bandwidth [BARAN].  This efficiency is based on the statistical
   multiplexing inherent in packet switching.  However, we continue to
   be puzzled by what is generally believed to be the low utilization of

   Internet backbones.  The first question we might ask is what is the
   current average utilization of Internet backbones, and how does that
   relate to the utilization of long distance voice networks?  Odlyzko
   and Coffman [ODLYZKO,COFFMAN] report that the average utilization of
   links in the IP networks was in the range between 3% and 20%
   (corporate intranets run in the 3% range, while commercial Internet
   backbones run in the 15-20% range).  On the other hand, the average
   utilization of long haul voice lines is about 33%.  In addition, for
   2002, the average utilization of optical networks (all services)
   appears to be hovering at about 11%, while the historical average is
   approximately 15% [ML2002].  The question then becomes why we see
   such utilization levels, especially in light of the assumption that
   PS is inherently more efficient than CS.  The reasons cited by
   Odlyzko and Coffman include:

      (i).   Internet traffic is extremely asymmetric and bursty, but
             links are symmetric and of fixed capacity (i.e., don't know
             the traffic matrix, or required link capacities);

      (ii).  It is difficult to predict traffic growth on a link, so
             operators tend to add bandwidth aggressively;

      (iii).  Falling prices for coarser bandwidth granularity make it
             appear more economical to add capacity in large increments.

   Other static factors include protocol overhead, other kinds of
   equipment granularity, restoration capacity, and provisioning lag
   time all contribute to the need to "over-provision" [MC2001].

5.2.  Is PS simpler than CS?

   The end-to-end principle can be interpreted as stating that the
   complexity of the Internet belongs at the edges.  However, today's
   Internet backbone routers are extremely complex.  Further, this
   complexity scales with line rate.  Since the relative complexity of
   circuit and packet switching seems to have resisted direct analysis,
   we instead examine several artifacts of packet and circuit switching
   as complexity metrics.  Among the metrics we might look at are
   software complexity, macro operation complexity, hardware complexity,
   power consumption, and density.  Each of these metrics is considered
   below.

5.2.1.  Software/Firmware Complexity

   One measure of software/firmware complexity is the number of
   instructions required to program the device.  The typical software
   image for an Internet router requires between eight and ten million
   instructions (including firmware), whereas a typical transport switch
   requires on average about three million instructions [MCK2002].

   This difference in software complexity has tended to make Internet
   routers unreliable, and has notable other second order effects (e.g.,
   it may take a long time to reboot such a router).  As another point
   of comparison, consider that the AT&T (Lucent) 5ESS class 5 switch,
   which has a huge number of calling features, requires only about
   twice the number of lines of code as an Internet core router [EICK].

   Finally, since routers are as much or more software than hardware
   devices, another result of the code complexity is that the cost of
   routers benefits less from Moore's Law than less software-intensive
   devices.  This causes a bandwidth/device trade-off that favors
   bandwidth more than less software-intensive devices.

5.2.2.  Macro Operation Complexity

   An Internet router's line card must perform many complex operations,
   including processing the packet header, longest prefix match,
   generating ICMP error messages, processing IP header options, and
   buffering the packet so that TCP congestion control will be effective
   (this typically requires a buffer of size proportional to the line
   rate times the RTT, so a buffer will hold around 250 ms of packet
   data).  This doesn't include route and packet filtering, or any QoS
   or VPN filtering.

   On the other hand, a transport switch need only to map ingress time-
   slots to egress time-slots and interfaces, and therefore can be
   considerably less complex.

5.2.3.  Hardware Complexity

   One measure of hardware complexity is the number of logic gates on a
   line card [MOLINERO2002].  Consider the case of a high-speed Internet
   router line card: An OC192 POS router line card contains at least 30
   million gates in ASICs, at least one CPU, 300 Mbytes of packet
   buffers, 2 Mbytes of forwarding table, and 10 Mbytes of other

   state memory.  On the other hand, a comparable transport switch line
   card has 7.5 million logic gates, no CPU, no packet buffer, no
   forwarding table, and an on-chip state memory.  Rather, the line-card
   of an electronic transport switch typically contains a SONET framer,
   a chip to map ingress time-slots to egress time-slots, and an
   interface to the switch fabric.

5.2.4.  Power

   Since transport switches have traditionally been built from simpler
   hardware components, they also consume less power [PMC].

5.2.5.  Density

   The highest capacity transport switches have about four times the
   capacity of an IP router [CISCO,CIENA], and sell for about one-third
   as much per Gigabit/sec.  Optical (OOO) technology pushes this
   complexity difference further (e.g., tunable lasers, MEMs switches.
   e.g., [CALIENT]), and DWDM multiplexers provide technology to build
   extremely high capacity, low power transport switches.

   A related metric is physical footprint.  In general, by virtue of
   their higher density, transport switches have a smaller "per-gigabit"
   physical footprint.

5.2.6.  Fixed versus variable costs

   Packet switching would seem to have high variable cost, meaning that
   it costs more to send the n-th piece of information using packet
   switching than it might in a circuit switched network.  Much of this
   advantage is due to the relatively static nature of circuit
   switching, e.g., circuit switching can take advantage of of pre-
   scheduled arrival of information to eliminate operations to be
   performed on incoming information.  For example, in the circuit
   switched case, there is no need to buffer incoming information,
   perform loop detection, resolve next hops, modify fields in the
   packet header, and the like.  Finally, many circuit switched networks
   combine relatively static configuration with out-of-band control
   planes (e.g., SS7), which greatly simplifies data-plane switching.
   The bottom line is that as data rates get large, it becomes more and
   more complex to switch packets, while circuit switching scales more
   or less linearly.

5.2.7.  QoS

   While the components of a complete solution for Internet QoS,
   including call admission control, efficient packet classification,
   and scheduling algorithms, have been the subject of extensive
   research and standardization for more than 10 years, end-to-end
   signaled QoS for the Internet has not become a reality.
   Alternatively, QoS has been part of the circuit switched
   infrastructure almost from its inception.  On the other hand, QoS is
   usually deployed to determine queuing disciplines to be used when
   there is insufficient bandwidth to support traffic.  But unlike voice
   traffic, packet drop or severe delay may have a much more serious
   effect on TCP traffic due to its congestion-aware feedback loop (in
   particular, TCP backoff/slow start).

5.2.8.  Flexibility

   A somewhat harder to quantify metric is the inherent flexibility of
   the Internet.  While the Internet's flexibility has led to its rapid
   growth, this flexibility comes with a relatively high cost at the
   edge: the need for highly trained support personnel.  A standard rule
   of thumb is that in an enterprise setting, a single support person
   suffices to provide telephone service for a group, while you need ten
   computer networking experts to serve the networking requirements of
   the same group [ODLYZKO98A].  This phenomenon is also described in
   [PERROW].

5.3.  Relative Complexity

   The relative computational complexity of circuit switching as
   compared to packet switching has been difficult to describe in formal
   terms [PARK].  As such, the sections above seek to describe the
   complexity in terms of observable artifacts.  With this in mind, it
   is clear that the fundamental driver producing the increased
   complexities outlined above is the hop-by-hop independence (HBHI)
   inherent in the IP architecture.  This is in contrast to the end to
   end architectures such as ATM or Frame Relay.

   [WILLINGER2002] describes this phenomenon in terms of the robustness
   requirement of the original Internet design, and how this requirement
   has the driven complexity of the network.  In particular, they
   describe a "complexity/robustness" spiral, in which increases in
   complexity create further and more serious sensitivities, which then
   requires additional robustness (hence the spiral).

   The important lesson of this section is that the Simplicity
   Principle, while applicable to circuit switching as well as packet
   switching, is crucial in controlling the complexity (and hence OPEX
   and CAPEX properties) of packet networks.  This idea is reinforced by
   the observation that while packet switching is a younger, less mature
   discipline than circuit switching, the trend in packet switches is
   toward more complex line cards, while the complexity of circuit
   switches appears to be scaling linearly with line rates and aggregate
   capacity.

5.3.1.  HBHI and the OPEX Challenge

   As a result of HBHI, we need to approach IP networks in a
   fundamentally different way than we do circuit based networks.  In
   particular, the major OPEX challenge faced by the IP network is that
   debugging of a large-scale IP network still requires a large degree
   of expertise and understanding, again due to the hop-by-hop
   independence inherent in a packet architecture (again, note that this
   hop-by-hop independence is not present in virtual circuit networks
   such as ATM or Frame Relay).  For example, you may have to visit a
   large set of your routers only to discover that the problem is
   external to your own network.  Further, the debugging tools used to
   diagnose problems are also complex and somewhat primitive.  Finally,
   IP has to deal with people having problems with their DNS or their
   mail or news or some new application, whereas this is usually not the
   case for TDM/ATM/etc.  In the case of IP, this can be eased by
   improving automation (note that much of what we mention is customer
   facing).  In general, there are many variables external to the
   network that effect OPEX.

   Finally, it is important to note that the quantitative relationship
   between CAPEX, OPEX, and a network's inherent complexity is not well
   understood.  In fact, there are no agreed upon and quantitative
   metrics for describing a network's complexity, so a precise
   relationship between CAPEX, OPEX, and complexity remains elusive.

6.  The Myth of Over-Provisioning

   As noted in [MC2001] and elsewhere, much of the complexity we observe
   in today's Internet is directed at increasing bandwidth utilization.
   As a result, the desire of network engineers to keep network
   utilization below 50% has been termed "over-provisioning".  However,
   this use of the term over-provisioning is a misnomer.  Rather, in
   modern Internet backbones the unused capacity is actually protection
   capacity.  In particular, one might view this as "1:1 protection at
   the IP layer".  Viewed in this way, we see that an IP network
   provisioned to run at 50% utilization is no more over-provisioned
   than the typical SONET network.  However, the important advantages

   that accrue to an IP network provisioned in this way include close to
   speed of light delay and close to zero packet loss [FRALEIGH].  These
   benefits can been seen as a "side-effect" of 1:1 protection
   provisioning.

   There are also other, system-theoretic reasons for providing 1:1-like
   protection provisioning.  Most notable among these reasons is that
   packet-switched networks with in-band control loops can become
   unstable and can experience oscillations and synchronization when
   congested.  Complex and non-linear dynamic interaction of traffic
   means that congestion in one part of the network will spread to other
   parts of the network.  When routing protocol packets are lost due to
   congestion or route-processor overload, it causes inconsistent
   routing state, and this may result in traffic loops, black holes, and
   lost connectivity.  Thus, while statistical multiplexing can in
   theory yield higher network utilization, in practice, to maintain
   consistent performance and a reasonably stable network, the dynamics
   of the Internet backbones favor 1:1 provisioning and its side effects
   to keep the network stable and delay low.

7.  The Myth of Five Nines

   Paul Baran, in his classic paper, "SOME PERSPECTIVES ON NETWORKS--
   PAST, PRESENT AND FUTURE", stated that "The tradeoff curves between
   cost and system reliability suggest that the most reliable systems
   might be built of relatively unreliable and hence low cost elements,
   if it is system reliability at the lowest overall system cost that is
   at issue" [BARAN77].

   Today we refer to this phenomenon as "the myth of five nines".
   Specifically, so-called five nines reliability in packet network
   elements is consider a myth for the following reasons: First, since
   80% of unscheduled outages are caused by people or process errors
   [SCOTT], there is only a 20% window in which to optimize.  Thus, in
   order to increase component reliability, we add complexity
   (optimization frequently leads to complexity), which is the root
   cause of 80% of the unplanned outages.  This effectively narrows the
   20% window (i.e., you increase the likelihood of people and process
   failure).  This phenomenon is also characterized as a
   "complexity/robustness" spiral [WILLINGER2002], in which increases in
   complexity create further and more serious sensitivities, which then
   requires additional robustness, and so on (hence the spiral).

   The conclusion, then is that while a system like the Internet can
   reach five-nines-like reliability, it is undesirable (and likely
   impossible) to try to make any individual component, especially the
   most complex ones, reach that reliability standard.

8.  Architectural Component Proportionality Law

   As noted in the previous section, the computational complexity of
   packet switched networks such as the Internet has proven difficult to
   describe in formal terms.  However, an intuitive, high level
   definition of architectural complexity might be that the complexity
   of an architecture is proportional to its number of components, and
   that the probability of achieving a stable implementation of an
   architecture is inversely proportional to its number of components.
   As described above, components include discrete elements such as
   hardware elements, space and power requirements, as well as software,
   firmware, and the protocols they implement.

   Stated more abstractly:

       Let

         A   be a representation of architecture A,

         |A| be number of distinct components in the service
             delivery path of architecture A,

         w   be a monotonically increasing function,

         P   be the probability of a stable implementation of an
             architecture, and let

       Then

         Complexity(A) = O(w(|A|))
         P(A)          = O(1/w(|A|))

       where

       O(f) = {g:N->R | there exists c > 0 and n such that g(n)
       < c*f(n)}

       [That is, O(f) comprises the set of functions g for which
       there exists a constant c and a number n, such that g(n) is
       smaller or equal to c*f(n) for all n. That is, O(f) is the
       set of all functions that do not grow faster than f,
       disregarding constant factors]

   Interestingly, the Highly Optimized Tolerance (HOT) model [HOT]
   attempts to characterize complexity in general terms (HOT is one
   recent attempt to develop a general framework for the study of
   complexity, and is a member of a family of abstractions generally
   termed "the new science of complexity" or "complex adaptive

   systems").  Tolerance, in HOT semantics, means that "robustness in
   complex systems is a constrained and limited quantity that must be
   carefully managed and protected." One focus of the HOT model is to
   characterize heavy-tailed distributions such as Complexity(A) in the
   above example (other examples include forest fires, power outages,
   and Internet traffic distributions).  In particular, Complexity(A)
   attempts to map the extreme heterogeneity of the parts of the system
   (Internet), and the effect of their organization into highly
   structured networks, with hierarchies and multiple scales.

8.1.  Service Delivery Paths

   The Architectural Component Proportionality Law (ACPL) states that
   the complexity of an architecture is proportional to its number of
   components.

   COROLLARY: Minimize the number of components in a service delivery
   path, where the service delivery path can be a protocol path, a
   software path, or a physical path.

   This corollary is an important consequence of the ACPL, as the path
   between a customer and the desired service is particularly sensitive
   to the number and complexity of elements in the path.  This is due to
   the fact that the complexity "smoothing" that we find at high levels
   of aggregation [ZHANG] is missing as you move closer to the edge, as
   well as having complex interactions with backoffice and CRM systems.
   Examples of architectures that haven't found a market due to this
   effect include TINA-based CRM systems, CORBA/TINA based service
   architectures.  The basic lesson here was that the only possibilities
   for deploying these systems were "Limited scale deployments (such) as
   in Starvision can avoid coping with major unproven scalability
   issues", or "Otherwise need massive investments (like the carrier-
   grade ORB built almost from scratch)" [TINA].  In other words, these
   systems had complex service delivery paths, and were too complex to
   be feasibly deployed.

9.  Conclusions

   This document attempts to codify long-understood Internet
   architectural principles.  In particular, the unifying principle
   described here is best expressed by the Simplicity Principle, which
   states complexity must be controlled if one hopes to efficiently
   scale a complex object.  The idea that simplicity itself can lead to
   some form of optimality has been a common theme throughout history,
   and has been stated in many other ways and along many dimensions.
   For example, consider the maxim known as Occam's Razor, which was
   formulated by the medieval English philosopher and Franciscan monk
   William of Ockham (ca. 1285-1349), and states "Pluralitas non est

   ponenda sine neccesitate" or "plurality should not be posited without
   necessity." (hence Occam's Razor is sometimes called "the principle
   of unnecessary plurality" and " the principle of simplicity").  A
   perhaps more contemporary formulation of Occam's Razor states that
   the simplest explanation for a phenomenon is the one preferred by
   nature.  Other formulations of the same  idea can be found in the
   KISS (Keep It Simple Stupid) principle and the Principle of Least
   Astonishment (the assertion that the most usable system is the one
   that least often leaves users astonished).  [WILLINGER2002] provides
   a more theoretical discussion of "robustness through simplicity", and
   in discussing the PSTN, [KUHN87] states that in most systems, "a
   trade-off can be made between simplicity of interactions and
   looseness of coupling".

   When applied to packet switched network architectures, the Simplicity
   Principle has implications that some may consider heresy, e.g., that
   highly converged approaches are likely to be less efficient than
   "less converged" solutions.  Otherwise stated, the "optimal"
   convergence layer may be much lower in the protocol stack that is
   conventionally believed.  In addition, the analysis above leads to
   several conclusions that are contrary to the conventional wisdom
   surrounding  packet networking.  Perhaps most significant is the
   belief that packet switching is simpler than circuit switching.  This
   belief has lead to conclusions such as "since packet is simpler than
   circuit, it must cost less to operate".  This study finds to the
   contrary.  In particular, by examining the metrics described above,
   we find that packet switching is more complex than circuit switching.
   Interestingly, this conclusion is borne out by the fact that
   normalized OPEX for data networks is typically significantly greater
   than for voice networks [ML2002].

   Finally, the important conclusion of this work is that for packet
   networks that are of the scale of today's Internet or larger, we must
   strive for the simplest possible solutions if we hope to build cost
   effective infrastructures.  This idea is eloquently stated in
   [DOYLE2002]: "The evolution of protocols can lead to a
   robustness/complexity/fragility spiral where complexity added for
   robustness also adds new fragilities, which in turn leads to new and
   thus spiraling complexities".  This is exactly the phenomenon that
   the Simplicity Principle is designed to avoid.

10.  Security Considerations

   This document does not directly effect the security of any existing
   Internet protocol.  However, adherence to the Simplicity Principle
   does have a direct affect on our ability to implement secure systems.
   In particular, a system's complexity grows, it becomes  more
   difficult to model and analyze, and hence it becomes more difficult

   to find and understand the security implications inherent in its
   architecture, design, and implementation.

11.  Acknowledgments

   Many of the ideas for comparing the complexity of circuit switched
   and packet switched networks were inspired by conversations with Nick
   McKeown.  Scott Bradner, David Banister, Steve Bellovin, Steward
   Bryant, Christophe Diot, Susan Harris, Ananth Nagarajan, Andrew
   Odlyzko, Pete and Natalie Whiting, and Lixia Zhang made many helpful
   comments on early drafts of this document.

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13.  Authors' Addresses

   Randy Bush
   EMail: randy@psg.com

   David Meyer
   EMail: dmm@maoz.com

14.  Full Copyright Statement

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Acknowledgement

   Funding for the RFC Editor function is currently provided by the
   Internet Society.

 

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