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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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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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