Recommendations on Queue Management and Congestion Avoidance in the Internet
draft-irtf-e2e-queue-mgt-00

Internet Draft Bob Braden
Expiration: September 1997 USC/ISI
File: draft-irtf-e2e-queue-mgt-00.txt Dave Clark
 MIT LCS
 Jon Crowcroft
 UCL
 Bruce Davie
 Cisco Systems
 Steve Deering
 Cisco Systems
 Deborah Estrin
 USC
 Sally Floyd
 LBNL
 Van Jacobson
 LBNL
 Greg Minshall
 Ipsilon
 Craig Partridge
 BBN
 Larry Peterson
 University of Arizona
 K. K. Ramakrishnan
 ATT Labs Research
 Scott Shenker
 Xerox PARC
 John Wroclawski
 MIT LCS
 Lixia Zhang
 UCLA
 Recommendations on Queue Management and Congestion Avoidance
 in the Internet
 March 25, 1997
Status of Memo
 This document is an Internet-Draft. Internet-Drafts are working
 documents of the Internet Engineering Task Force (IETF), its areas,
 and its working groups. Note that other groups may also distribute
 working documents as Internet-Drafts.
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 Internet-Drafts are draft documents valid for a maximum of six months
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 material or to cite them other than as "work in progress."
 To learn the current status of any Internet-Draft, please check the
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 Rim).
Abstract
 This memo presents two recommendations to the Internet community
 concerning measures to improve and preserve Internet performance. It
 presents a strong recommendation for testing, standardization, and
 widespread deployment of active queue management in routers, to
 improve the performance of today's Internet. It also urges a
 concerted effort of research, measurement, and ultimate deployment of
 router mechanisms to protect the Internet from flows that are not
 sufficiently responsive to congestion notification.
1. INTRODUCTION
 The Internet protocol architecture is based on a connectionless end-
 to-end packet service using the IP protocol. The advantages of its
 connectionless design, flexibility and robustness, have been amply
 demonstrated. However, these advantages are not without cost:
 careful design is required to provide good service under heavy load.
 In fact, lack of attention to the dynamics of packet forwarding can
 result in severe service degradation or "Internet meltdown". This
 phenomenon was first observed during the early growth phase of the
 Internet of the mid 1980s [Nagle84], and is technically called
 "congestion collapse".
 The original fix for Internet meltdown was provided by Van Jacobson.
 Beginning in 1986, Jacobson developed the congestion avoidance
 mechanisms that are now required in TCP implementations [Jacobson88,
 HostReq89]. These mechanisms operate in the hosts to cause TCP
 connections to "back off" during congestion. We say that TCP flows
 are "responsive" to congestion signals (i.e., dropped packets) from
 the network. It is primarily these TCP congestion avoidance
 algorithms that prevent the congestion collapse of today's Internet.
 However, that is not the end of the story. Considerable research has
 been done on Internet dynamics since 1988, and the Internet has
 grown. It has become clear that the TCP congestion avoidance
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 mechanisms, while necessary and powerful, are not sufficient to
 provide good service in all circumstances. Basically, there is a
 limit to how much control can be accomplished from the edges of the
 network. Some mechanisms are needed in the routers to complement the
 endpoint congestion avoidance mechanisms.
 It is useful to distinguish between two classes of router algorithms
 related to congestion control: "queue management" versus "
 scheduling" algorithms. To a rough approximation, queue management
 algorithms manage the length of packet queues by dropping packets
 when necessary or appropriate, while scheduling algorithms determine
 which packet to send next and are used primarily to manage the
 allocation of bandwidth among flows. While these two router
 mechanisms are closely related, they address rather different
 performance issues.
 This memo highlights two router performance issues. The first issue
 is the need for an advanced form of router queue management that we
 call "active queue management." Section 2 summarizes the benefits
 that active queue management can bring. Section 3 describes a
 recommended active queue management mechanism, called Random Early
 Detection or "RED". We expect that the RED algorithm can be used
 with a wide variety of scheduling algorithms, can be implemented
 relatively efficiently, and will provide significant Internet
 performance improvement.
 The second issue, discussed in Section 4 of this memo, is the
 potential for future congestion collapse of the Internet due to flows
 that are unresponsive, or not sufficiently responsive, to congestion
 indications. Unfortunately, there is no consensus solution to
 controlling congestion caused by such aggressive flows; significant
 research and engineering will be required before any solution will be
 available. It is imperative that this work be energetically pursued,
 to ensure the future stability of the Internet.
 Section 5 concludes the memo with a set of recommendations to the
 IETF concerning these topics.
 The discussion in this memo applies to "best-effort" traffic. The
 Internet integrated services architecture, which provides a mechanism
 for protecting individual flows from congestion, introduces its own
 queue management and scheduling algorithms [Shenker96, Wroclawski96].
 However, we do not expect deployment of integrated services to
 significantly diminish the importance of the best-effort traffic
 issues discussed in this memo.
 Preparation of this memo resulted from past discussions of end-to-end
 performance, Internet congestion, and RED in the End-to-End Research
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 Group of the Internet Research Task Force (IRTF).
2. THE NEED FOR ACTIVE QUEUE MANAGEMENT
 The traditional technique for managing router queue lengths is to set
 a maximum length (in terms of packets) for each queue, accept packets
 for the queue until the maximum length is reached, then reject (drop)
 subsequent incoming packets until the queue decreases because a
 packet from the queue has been transmitted. This technique is known
 as "tail drop", since the packet that arrived most recently (i.e.,
 the one on the tail of the queue) is dropped when the queue is full.
 This method has served the Internet well for years, but it has two
 important drawbacks.
 1. Lock-Out
 In some situations tail drop allows a single connection or a few
 flows to monopolize queue space, preventing other connections
 from getting room in the queue. This "lock-out" phenomenon is
 often the result of synchronization or other timing effects.
 2. Full Queues
 The tail drop discipline allows queues to maintain a full (or,
 almost full) status for long periods of time, since tail drop
 signals congestion (via a packet drop) only when the queue has
 become full. It is important to reduce the steady-state queue
 size, and this is perhaps queue management's most important
 goal.
 The naive assumption might be that there is a simple tradeoff
 between delay and throughput, and that the recommendation that
 queues be maintained in a "non-full" state essentially
 translates to a recommendation that low end-to-end delay is more
 important than high throughput. However, this does not take
 into account the critical role that packet bursts play in
 Internet performance. Even though TCP constrains a flow's
 window size, packets often arrive at routers in bursts
 [Leland94]. If the queue is full or almost full, an arriving
 burst will cause multiple packets to be dropped. This can
 result in a global synchronization of flows throttling back,
 followed by a sustained period of lowered link utilization,
 reducing overall throughput.
 The point of buffering in the network is to absorb data bursts
 and to transmit them during the (hopefully) ensuing bursts of
 silence. This is essential to permit the transmission of bursty
 data. It should be clear why we would like to have normally-
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 small queues in routers: we want to have queue capacity to
 absorb the bursts. The counter-intuitive result is that
 maintaining normally-small queues can result in higher
 throughput as well as lower end-to-end delay. In short, queue
 limits should not reflect the steady state queues we want
 maintained in the network; instead, they should reflect the size
 of bursts we need to absorb.
 Besides tail drop, two alternative queue disciplines that can be
 applied when the queue becomes full are "random drop on full" or
 "drop front on full". Under the random drop on full discipline, a
 router drops a randomly selected packet from the queue (which can be
 an expensive operation, since it naively requires an O(N) walk
 through the packet queue) when the queue is full and a new packet
 arrives. Under the "drop front on full" discipline [Lakshman96], the
 router drops the packet at the front of the queue when the queue is
 full and a new packet arrives. Both of these solve the lock-out
 problem, but neither solves the full-queues problem described above.
 We know in general how to solve the full-queues problem for
 "responsive" flow, i.e., those flows that throttle back in response
 to congestion notification. The solution involves dropping packets
 before a queue becomes full, so that a router can control when and
 how many packets to drop. We call such a proactive approach "active
 queue management". The next section introduces RED, an active queue
 management mechanism that solves both problems listed above (for
 responsive flows).
 In summary, an active queue management mechanism can provide the
 following advantages for responsive flows.
 1. Reduce number of packets dropped in routers
 Packet bursts are just part of the networking business
 [Willinger95]. If all the queue space in a router is already
 committed to "steady state" traffic or if the buffer space is
 inadequate, then the router will have no ability to buffer
 bursts. By keeping the average queue size small, active queue
 management will provide greater capacity to absorb naturally-
 occurring bursts without dropping packets.
 Furthermore, without active queue management, more packets will
 be dropped when a queue does overflow. This is undesirable for
 several reasons. First, with a shared queue and the tail drop
 discipline, an unnecessary global synchronization of flows
 cutting back can result in lowered average link utilization, and
 hence lowered network throughput. Second, TCP recovers with
 more difficulty from a burst of packet drops than from a single
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 packet drop. Third, unnecessary packet drops represent a
 possible waste of bandwidth on the way to the drop point.
 2. Provide lower-delay interactive service
 By keeping the average queue size small, queue management will
 reduce the delays seen by flows. This is particularly important
 for interactive applications such as short Web transfers, Telnet
 traffic, or interactive audio-video sessions, whose subjective
 (and objective) performance is better when the end-to-end delay
 is low.
 3. Avoid lock-out behavior
 Active queue management can prevent lock-out behavior by
 ensuring that there will almost always be a buffer available for
 an incoming packet. For the same reason, active queue
 management can prevent a router bias against low bandwidth but
 highly bursty flows.
 It is clear that lock-out is undesirable because it constitutes
 a gross unfairness among groups of flows. However, we stop
 short of calling this benefit "increased fairness", because
 general fairness among flows requires per-flow state, which is
 not provided by queue management. For example, in a router
 using queue management but only FIFO scheduling, two TCP flows
 may receive very different bandwidths simply because they have
 different round-trip times [Floyd91], and a flow that does not
 use congestion control may receive more bandwidth than a flow
 that does. Per-flow state to achieve general fairness might be
 maintained by a per-flow scheduling algorithm such as Fair
 Queueing (FQ) [Demers90], or a class-based scheduling algorithm
 such as CBQ [Floyd95], for example.
 On the other hand, active queue management is needed even for
 routers that use per-flow scheduling algorithms such as FQ or
 CBQ This is because per-flow scheduling algorithms by
 themselves do nothing to control the overall queue size or the
 size of individual queues. Active queue management is needed to
 control the overall average queue sizes, so that arriving bursts
 can be accommodated without dropping packets. In addition,
 active queue management should be used to control the queue size
 for each individual flow or class, so that they do not
 experience unnecessarily high delays. Therefore, active queue
 management should be applied across the classes or flows as well
 as within each class or flow.
 In short, scheduling algorithms and queue management should be
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 seen as complementary, not as replacements for each other. In
 particular, there have been implementations of queue management
 added to FQ, and work is in progress to add RED queue management
 to CBQ.
3. THE QUEUE MANAGEMENT ALGORITHM "RED"
 Random Early Detection, or RED, is an active queue management
 algorithm for routers that will provide the Internet performance
 advantages cited in the previous section [RED93]. In contrast to
 traditional queue management algorithms, which drop packets only when
 the buffer is full, the RED algorithm drops arriving packets
 probabilistically. The probability of drop increases as the
 estimated average queue size grows. Note that RED responds to a
 time-averaged queue length, not an instantaneous one. Thus, if the
 queue has been mostly empty in the "recent past", RED won't tend to
 drop packets (unless the queue overflows, of course!). On the other
 hand, if the queue has recently been relatively full, indicating
 persistent congestion, newly arriving packets are more likely to be
 dropped.
 The RED algorithm itself consists of two main parts: estimation of
 the average queue size and the decision of whether or not to drop an
 incoming packet.
 (a) Estimation of Average Queue Size
 RED estimates the average queue size, either in the forwarding
 path using a simple exponentially weighted moving average (such
 as presented in Appendix A of [Jacobson88]), or in the
 background (i.e., not in the forwarding path) using a similar
 mechanism.
 Note: when the average queue size is computed in the
 forwarding path, there is a special case when a packet
 arrives and the queue is empty. In this case, the
 computation of the average queue size must take into account
 how much time has passed since the queue went empty. This is
 discussed further in [RED93].
 (b) Packet Drop Decision
 In the second portion of the algorithm, RED decides whether or
 not to drop an incoming packet. It is RED's particular
 algorithm for dropping that results in performance improvement
 for responsive flows. Two RED parameters, minth (minimum
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 threshold) and maxth (maximum threshold), figure prominently in
 this decision process. Minth specifies the average queue size
 *below which* no packets will be dropped, while maxth specifies
 the average queue size *above which* all packets will be
 dropped. As the average queue size varies from minth to maxth,
 packets will be dropped with a probability that varies linearly
 from 0 to maxp.
 Note: a simplistic method of implementing this would be to
 calculate a new random number at each packet arrival, then
 compare that number with the above probability which varies
 from 0 to maxp. A more efficient implementation, described
 in [RED93], computes a random number *once* for each dropped
 packet.
 RED effectively controls the average queue size while still
 accommodating bursts of packets without loss. RED's use of
 randomness breaks up synchronized processes that lead to lock-out
 phenomena.
 There have been several implementations of RED in routers, and papers
 have been published reporting on experience with these
 implementations ([Villamizar94], [Gaynor96]). Additional reports of
 implementation experience would be welcome.
 All available empirical evidence shows that the deployment of active
 queue management mechanisms in the Internet would have substantial
 performance benefits. There are seemingly no disadvantages to using
 the RED algorithm, and numerous advantages. Consequently, we believe
 that the RED active queue management algorithm should be widely
 deployed.
 We should note that there are some extreme scenarios for which RED
 will not be a cure, although it won't hurt and may still help. An
 example of such a scenario would be a very large number of flows,
 each so tiny that its fair share would be less than a single packet
 per RTT.
4. MANAGING AGGRESSIVE FLOWS
 One of the keys to the success of the Internet has been the
 congestion avoidance mechanisms of TCP. Because TCP "backs off"
 during congestion, a large number of TCP connections can share a
 single, congested link in such a way that bandwidth is shared
 reasonably equitably among similarly situated flows. The equitable
 sharing of bandwidth among flows depends on the fact that all flows
 are running basically the same congestion avoidance algorithms,
 conformant with the current TCP specification [HostReq89].
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 We introduce the term "TCP-compatible" for a flow that behaves under
 congestion like a flow produced by a conformant TCP. A TCP-
 compatible flow is responsive to congestion notification, and in
 steady-state it uses no more bandwidth than a conformant TCP running
 under comparable conditions (drop rate, RTT, MTU, etc.)
 It is convenient to divide flows into three classes: (1) TCP-
 compatible flows, (2) unresponsive flows, i.e., flows that do not
 slow down when congestion occurs, and (3) flows that are responsive
 but are not TCP-compatible. The last two classes contain more
 aggressive flows that pose significant threats to Internet
 performance, as we will now discuss.
 o Non-Responsive Flows
 There is a growing set of UDP-based applications whose
 congestion avoidance algorithms are inadequate or nonexistent
 (i.e, the flow does not throttle back upon receipt of congestion
 notification). Such UDP applications include streaming
 applications like packet voice and video, and also multicast
 bulk data transport [SRM96]. If no action is taken, such
 unresponsive flows could lead to a new congestion collapse.
 In general, all UDP-based streaming applications should
 incorporate effective congestion avoidance mechanisms. For
 example, recent research has shown the possibility of
 incorporating congestion avoidance mechanisms such as Receiver-
 driven Layered Multicast (RLM) within UDP-based streaming
 applications such as packet video [McCanne96; Bolot94]. Further
 research and development on ways to accomplish congestion
 avoidance for streaming applications will be very important.
 However, it will also be important for the network to be able to
 protect itself against unresponsive flows, and mechanisms to
 accomplish this must be developed and deployed. Deployment of
 such a mechanism would provide incentive for every streaming
 application to become responsive by incorporating its own
 congestion control.
 o Non-TCP-Compatible Transport Protocols
 The second threat is posed by transport protocol implementations
 that are responsive to congestion notification but, either
 deliberately or through faulty implementations, are not TCP-
 compatible. Such applications can grab an unfair share of the
 network bandwidth.
 For example, the popularity of the Internet has caused a
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 proliferation in the number of TCP implementations. Some of
 these may fail to implement the TCP congestion avoidance
 mechanisms correctly because of poor implementation. Others may
 deliberately be implemented with congestion avoidance algorithms
 that are more aggressive in their use of bandwidth than other
 TCP implementations; this would allow a vendor to claim to have
 a "faster TCP". The logical consequence of such implementations
 would be a spiral of increasingly aggressive TCP
 implementations, leading back to the point where there is
 effectively no congestion avoidance and the Internet is
 chronically congested.
 Note that there is a well-known way to achieve more aggressive
 TCP performance without even changing TCP: open multiple
 connections to the same place, as has been done in some Web
 browsers.
 The projected increase in more aggressive flows of both these
 classes, as a fraction of total Internet traffic, clearly poses a
 threat to the future Internet. There is an urgent need for
 measurements of current conditions and for further research into the
 various ways of managing such flows. There are many difficult issues
 in identifying and isolating unresponsive or non-TCP-compatible flows
 at an acceptable router overhead cost. Finally, there is little
 measurement or simulation evidence available about the rate at which
 these threats are likely to be realized, or about the expected
 benefit of router algorithms for managing such flows.
 There is an issue about the appropriate granularity of a "flow".
 There are a few "natural" answers: 1) a TCP or UDP connection (source
 address/port, destination address/port); 2) a source/destination host
 pair; 3) a given source host or a given destination host. We would
 guess that the source/destination host pair gives the most
 appropriate granularity in many circumstances. However, it is
 possible that different vendors/providers could set different
 granularities for defining a flow (as a way of "distinguishing"
 themselves from one another), or that different granularities could
 be chosen for different places in the network. It may be the case
 that the granularity is less important than the fact that we are
 dealing with more unresponsive flows at *some* granularity. The
 granularity of flows for congestion management is, at least in part,
 a policy question that needs to be addressed in the wider IETF
 community.
5. CONCLUSIONS AND RECOMMENDATIONS
 This discussion leads us to make the following recommendations to the
 IETF and to the Internet community as a whole.
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 o RECOMMENDATION 1:
 Internet routers should implement some active queue management
 mechanism to manage queue lengths, reduce end-to-end latency,
 reduce packet dropping, and avoid lock-out phenomena within the
 Internet.
 The default mechanism for managing queue lengths to meet these
 goals in FIFO queues is Random Early Detection (RED) [RED93].
 Unless a developer has reasons to provide another equivalent
 mechanism, we recommend that RED be used.
 o RECOMMENDATION 2:
 It is urgent to begin or continue research, engineering, and
 measurement efforts contributing to the design of mechanisms to
 deal with flows that are unresponsive to congestion notification
 or are responsive but more aggressive than TCP.
 Widespread implementation and deployment of RED, as recommended
 above, will expose a number of engineering issues. Examples of such
 issues include: implementation questions for Gigabit routers, the
 use of RED in layer 2 switches, and the possible use of additional
 considerations, such as priority, in deciding which packets to drop.
6. References
 [Bolot94] Bolot, J.-C., Turletti, T., and Wakeman, I., Scalable
 Feedback Control for Multicast Video Distribution in the Internet,
 ACM SIGCOMM '94, Sept. 1994.
 [Demers90] Demers, A., Keshav, S., and Shenker, S., Analysis and
 Simulation of a Fair Queueing Algorithm, Internetworking: Research
 and Experience, Vol. 1, 1990, pp. 3-26.
 [Floyd91] Floyd, S., Connections with Multiple Congested Gateways in
 Packet-Switched Networks Part 1: One-way Traffic. Computer
 Communications Review, Vol.21, No.5, October 1991, pp. 30-47. URL
 http://ftp.ee.lbl.gov/floyd/.
 [Floyd95] Floyd, S., and Jacobson, V., Link-sharing and Resource
 Management Models for Packet Networks. IEEE/ACM Transactions on
 Networking, Vol. 3 No. 4, pp. 365-386, August 1995.
 [Gaynor96] Gaynor, M., Proactive Packet Dropping Methods for TCP
 Gateways, October 1996, URL http://www.eecs.harvard.edu/~gaynor/
 final.ps.
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 [HostReq89] R. Braden, Ed., Requirements for Internet Hosts --
 Communication Layers, RFC-1122, October 1989.
 [Jacobson88] V. Jacobson, Congestion Avoidance and Control, ACM
 SIGCOMM '88, August 1988.
 [Lakshman96] T. V. Lakshman, Arnie Neidhardt, Teunis Ott, The Drop
 From Front Strategy in TCP Over ATM and Its Interworking with Other
 Control Features, Infocom 96, MA28.1.
 [Leland94] W. Leland, M. Taqqu, W. Willinger, and D. Wilson, On the
 Self-Similar Nature of Ethernet Traffic (Extended Version), IEEE/ACM
 Transactions on Networking, 2(1), pp. 1-15, February 1994.
 [McCanne96] McCanne, S., Jacobson, V., and M. Vetterli, Receiver-
 driven Layered Multicast, ACM SIGCOMM
 [Nagle84] J. Nagle, Congestion Control in IP/TCP, RFC-896, January
 1984.
 [RED93] Floyd, S., and Jacobson, V., Random Early Detection gateways
 for Congestion Avoidance, IEEE/ACM Transactions on Networking, V.1
 N.4, August 1993, pp. 397-413. Also available from
 http://ftp.ee.lbl.gov/floyd/red.html.
 [Shenker96] Shenker, S., Partridge, C., and Guerin, R., Specification
 of Guaranteed Quality of Service, IETF Integrated Services Working
 Group, Internet draft (work in progress), August 1996.
 [SRM96] Floyd. S., Jacobson, V., McCanne, S., Liu, C., and L. Zhang,
 A Reliable Multicast Framework for Light-weight Sessions and
 Application Level Framing. ACM SIGCOMM '96, pp 342-355.
 [Villamizar94] Villamizar, C., and Song, C., High Performance TCP in
 ANSNET. Computer Communications Review, V. 24 N. 5, October 1994, pp.
 45-60. URL http://ftp.ans.net/pub/papers/tcp-performance.ps.
 [Willinger95] W. Willinger, M. S. Taqqu, R. Sherman, D. V. Wilson,
 Self-Similarity Through High-Variability: Statistical Analysis of
 Ethernet LAN Traffic at the Source Level, ACM SIGCOMM '95, pp. 100-
 113, August 1995.
 [Wroclawski96] J. Wroclawski, Specification of the Controlled-Load
 Network Element Service, IETF Integrated Services Working Group,
 Internet draft (work in progress), August 1996.
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 Security Considerations
 While security is a very important issue, it is largely orthogonal
 to the performance issues discussed in this memo. We note,
 however, that denial-of-service attacks may create unresponsive
 traffic flows that are indistinguishable from flows from normal
 high-bandwidth isochronous applications, and the mechanism
 suggested in Recommendation 2 will be equally applicable to such
 attacks.
 Authors' Addresses
 Bob Braden
 USC Information Sciences Institute
 4676 Admiralty Way
 Marina del Rey, CA 90292
 Phone: 310-822-1511
 Email: Braden@ISI.EDU
 David D. Clark
 MIT Laboratory for Computer Science
 545 Technology Sq.
 Cambridge, MA 02139
 Phone: 617-253-6003
 Email: DDC@lcs.mit.edu
 Jon Crowcroft
 University College London
 Department of Computer Science
 Gower Street
 London, WC1E 6BT
 ENGLAND
 Phone: +44 171 380 7296
 Email: Jon.Crowcroft@cs.ucl.ac.uk
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 Bruce Davie
 Cisco Systems, Inc.
 250 Apollo Drive
 Chelmsford, MA 01824
 Phone:
 E-mail: bdavie@cisco.com
 Steve Deering
 Cisco Systems, Inc.
 170 West Tasman Drive
 San Jose, CA 95134-1706
 Phone: 408-527-8213
 Email: deering@cisco.com
 Deborah Estrin
 USC Information Sciences Institute
 4676 Admiralty Way
 Marina del Rey, CA 90292
 Phone: 310-822-1511
 Email: Estrin@usc.edu
 Sally Floyd
 Lawrence Berkeley National Laboratory,
 MS 50B-2239,
 One Cyclotron Road,
 Berkeley CA 94720
 Phone:
 Email: Floyd@ee.lbl.gov
 Van Jacobson
 Lawrence Berkeley National Laboratory,
 MS 46A,
 One Cyclotron Road,
 Berkeley CA 94720
 Phone: 510-486-7519
 Email: Van@ee.lbl.gov
 Greg Minshall
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 Ipsilon Systems
 232 Java Drive
 Sunnyvale, CA 94089
 Phone:
 Email: Minshall@ipsilon.com
 Craig Partridge
 824 Kipling St
 Palo Alto CA 94301-2831
 Phone: 415-326-4541
 Email: Craig@aland.bbn.com
 Larry Peterson
 Department of Computer Science
 University of Arizona
 Tucson, AZ 85721
 Phone: 520-621-4231
 Email: LLP@cs.arizona.edu
 K. K. Ramakrishnan
 AT&T Labs. Research
 Rm. 2C-454,
 600 Mountain Ave.,
 Murray Hill, N.J. 07974-0636.
 Phone: 908-582-3154
 Email: KKRama@research.att.com
 Scott Shenker
 Xerox PARC
 3333 Coyote Hill Road
 Palo Alto, CA 94304
 Phone: 415-812-4840
 Email: Shenker@parc.xerox.com
 John Wroclawski
 MIT Laboratory for Computer Science
 545 Technology Sq.
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 Cambridge, MA 02139
 Phone: 617-253-7885
 Email: JTW@lcs.mit.edu
 Lixia Zhang
 UCLA
 45316 Boelter Hall
 Los Angeles, CA 90024
 Phone: 310-825-2695
 Email: Lixia@cs.ucla.edu
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