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RFC3439 - Some Internet Architectural Guidelines and Philosophy

王朝other·作者佚名  2008-05-31
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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 RFC1958 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

RFC1958 [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 RFC1958 [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 RFC1925

[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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[BARAN77] "SOME PERSPECTIVES ON NETWORKS--PAST, PRESENT AND

FUTURE", Paul Baran, Information Processing 77,

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[BRYANT] "Protocol Layering in PWE3", Bryant et al, Work in

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[CAIDA] http://www.caida.org

[CALLIENT] http://www.calient.net/home.Html

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Doyle, Proc. Natl. Acad. Sci. USA, Vol. 99, Suppl. 1,

2538-2545, February 19, 2002.

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[CIENA] "CIENA Multiwave CoreDiretor",

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[CLARK] "The Design Philosophy of the DARPA Internet

Protocols", D. Clark, Proc. of the ACM SIGCOMM, 1988.

[COFFMAN] "Internet Growth: Is there a 'Moores Law' for Data

Traffic", K.G. Coffman and A.M. Odlyzko, pp. 47-93,

Handbook of Massive Data Stes, J. Elli, P. M.

Pardalos, and M. G. C. Resende, Editors. Kluwer,

2002.

[DOYLE2002] "Robustness and the Internet: Theoretical

Foundations", John C. Doyle, et. al. Work in

Progress.

[EICK] "Visualizing Software Changes", S.G. Eick, et al,

National Institute of Statistical Sciences, Technical

Report 113, December 2000.

[MOLINERO2002] "TCP Switching: Exposing Circuits to IP", Pablo

Molinero-Fernandez and Nick McKeown, IEEE January,

2002.

[FLOYD] "The Synchronization of Periodic Routing Messages",

Sally Floyd and Van Jacobson, IEEE ACM Transactions

on Networking, 1994.

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Congestion Control, IEEE Communications Magazine, S.

Floyd, April 2001.

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Based Service Level Agreements", Chuck Fraleigh,

Fouad Tobagi, and Christophe Diot, 2002.

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Griffin, IPAM Workshop on Large-Scale Communication

Networks: Topology, Routing, Traffic, and Control,

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IAB workshop on Wireless Internetworking", M.

Handley, March 2000.

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[JACOBSON] "Congestion Avoidance and Control", Van Jacobson,

Proceedings of ACM Sigcomm 1988, pp. 273-288.

[KARN] "TCP vs Link Layer Retransmission" in P. Karn et al.,

Advice for Internet Subnetwork Designers, Work in

Progress.

[KUHN87] "Sources of Failure in the Public Switched Telephone

Network", D. Richard Kuhn, EEE Computer, Vol. 30, No.

4, April, 1997.

[L2TPV3] Lan, J., et. al., "Layer Two Tunneling Protocol

(Version 3) -- L2TPv3", Work in Progress.

[MC2001] "U.S Communications Infrastructure at A Crossroads:

Opportunities Amid the Gloom", McKinsey&Company for

Goldman-Sachs, August 2001.

[MCK2002] Nick McKeown, personal communication, April, 2002.

[ML2002] "Optical Systems", Merril Lynch Technical Report,

April, 2002.

[NAVE] "The influence of mode coupling on the non-linear

evolution of tearing modes", M.F.F. Nave, et al, Eur.

Phys. J. D 8, 287-297.

[NEUMANN] "Cause of AT&T network failure", Peter G. Neumann,

http://catless.ncl.ac.uk/Risks/9.62.html#subj2

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A.M. Odlyzko, IT Professional 1 (no. 2), pp. 67-69,

Mar/Apr 1999.

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Microsoft". A. M. Odlyzko, ACM Networker, 2(5),

December, 1998.

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pricing, and Quality of Service", A.M. Odlyzko, July,

1998.

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Technologies", Basic Books, C. Perrow, New York,

1984.

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Architecture", PMC-Sierra, http://www.pmc-

sierra.com/products/diagrams/CoreRouter_lg.html

[RFC1629] Colella, R., Callon, R., Gardner, E. and Y. Rekhter,

"Guidelines for OSI NSAP Allocation in the Internet",

RFC1629, May 1994.

[RFC1925] Callon, R., "The Twelve Networking Truths", RFC1925,

1 April 1996.

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Internet", RFC1958, June 1996.

[RFC2283] Bates, T., Chandra, R., Katz, D. and Y. Rekhter,

"Multiprotocol Extensions for BGP4", RFC2283,

February 1998.

[RFC3155] Dawkins, S., Montenegro, G., Kojo, M. and N. Vaidya,

"End-to-end Performance Implications of Links with

Errors", BCP 50, RFC3155, May 2001.

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Number 4, November 1984, pp 277-288.

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Downtime", D. Scott, Tactical Guidelines, TG-07-4033,

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[SPILLMAN] "The Law of Diminishing Returns:, W. J. Spillman and

E. Lang, 1924.

[STALLINGS] "Data and Computer Communications (2nd Ed)", William

Stallings, Maxwell Macmillan, 1989.

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Tennenhouse, Proceedings of the IFIP Workshop on

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H.B. Stewart, John Wiley and Sons, 1994, ISBN

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Crowcroft, Zheng Wang, and Dejan Sirovica, IEEE

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alternative method for nucleic acid labeling",

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[WILLINGER2002] "Robustness and the Internet: Design and evolution",

Walter Willinger and John Doyle, 2002.

[ZHANG] "Impact of Aggregation on Scaling Behavior of

Internet Backbone Traffic", Sprint ATL Technical

Report TR02-ATL-020157 Zhi-Li Zhang, Vinay Ribeiroj,

Sue Moon, Christophe Diot, February, 2002.

13. Authors' Addresses

Randy Bush

EMail: randy@psg.com

David Meyer

EMail: dmm@maoz.com

14. Full Copyright Statement

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

This document and translations of it may be copied and furnished to

others, and derivative works that comment on or otherwise explain it

or assist in its implementation may be prepared, copied, published

and distributed, in whole or in part, without restriction of any

kind, provided that the above copyright notice and this paragraph are

included on all such copies and derivative works. However, this

document itself may not be modified in any way, such as by removing

the copyright notice or references to the Internet Society or other

Internet organizations, except as needed for the purpose of

developing Internet standards in which case the procedures for

copyrights defined in the Internet Standards process must be

followed, or as required to translate it into languages other than

English.

The limited permissions granted above are perpetual and will not be

revoked by the Internet Society or its successors or assigns.

This document and the information contained herein is provided on an

"AS IS" basis and THE INTERNET SOCIETY AND THE INTERNET ENGINEERING

TASK FORCE DISCLAIMS ALL WARRANTIES, EXPRESS OR IMPLIED, INCLUDING

BUT NOT LIMITED TO ANY WARRANTY THAT THE USE OF THE INFORMATION

HEREIN WILL NOT INFRINGE ANY RIGHTS OR ANY IMPLIED WARRANTIES OF

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Acknowledgement

Funding for the RFCEditor function is currently provided by the

Internet Society.

 
 
 
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