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Versions: 00 RFC 2309

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
   and may be updated, replaced, or obsoleted by other documents at any
   time.  It is inappropriate to use Internet-Drafts as reference
   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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   (Europe), ftp.isi.edu (US West Coast), or munnari.oz.au (Pacific
   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.



IRTF                   Expiration: September 1997              [Page 15]


Internet Draft    Internet Performance Recommendations     February 1997


      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





































IRTF                   Expiration: September 1997              [Page 16]


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