Network Working Group                                          A. Mankin
Request for Comments: 1254                                         MITRE
                                                         K. Ramakrishnan
                                           Digital Equipment Corporation
                                                             August 1991

                   Gateway Congestion Control Survey

Status of this Memo

   This memo provides information for the Internet community.  It is a
   survey of some of the major directions and issues.  It does not
   specify an Internet standard.  Distribution of this memo is


   The growth of network intensive Internet applications has made
   gateway congestion control a high priority.  The IETF Performance and
   Congestion Control Working Group surveyed and reviewed gateway
   congestion control and avoidance approaches.  The purpose of this
   paper is to present our review of the congestion control approaches,
   as a way of encouraging new discussion and experimentation.  Included
   in the survey are Source Quench, Random Drop, Congestion Indication
   (DEC Bit), and Fair Queueing.  The task remains for Internet
   implementors to determine and agree on the most effective mechanisms
   for controlling gateway congestion.

1.  Introduction

   Internet users regularly encounter congestion, often in mild forms.
   However, severe congestion episodes have been reported also; and
   gateway congestion remains an obstacle for Internet applications such
   as scientific supercomputing data transfer.  The need for Internet
   congestion control originally became apparent during several periods
   of 1986 and 1987, when the Internet experienced the "congestion
   collapse" condition predicted by Nagle [Nag84].  A large number of
   widely dispersed Internet sites experienced simultaneous slowdown or
   cessation of networking services for prolonged periods.  BBN, the
   firm responsible for maintaining the then backbone of the Internet,
   the ARPANET, responded to the collapse by adding link capacity

   Much of the Internet now uses as a transmission backbone the National
   Science Foundation Network (NSFNET). Extensive monitoring and
   capacity planning are being done for the NSFNET backbone; still, as

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   the demand for this capacity grows, and as resource-intensive
   applications such as wide-area file system management [Sp89]
   increasingly use the backbone, effective congestion control policies
   will be a critical requirement.

   Only a few mechanisms currently exist in Internet hosts and gateways
   to avoid or control congestion.  The mechanisms for handling
   congestion set forth in the specifications for the DoD Internet
   protocols are limited to:

      Window flow control in TCP [Pos81b], intended primarily for
      controlling the demand on the receiver's capacity, both in terms
      of processing and buffers.

      Source quench in ICMP, the message sent by IP to request that a
      sender throttle back [Pos81a].

   One approach to enhancing Internet congestion control has been to
   overlay the simple existing mechanisms in TCP and ICMP with more
   powerful ones.  Since 1987, the TCP congestion control policy, Slow-
   start, a collection of several algorithms developed by Van Jacobson
   and Mike Karels [Jac88], has been widely adopted. Successful Internet
   experiences with Slow-start led to the Host Requirements RFC [HREQ89]
   classifying the algorithms as mandatory for TCP.  Slow-start modifies
   the user's demand when congestion reaches such a point that packets
   are dropped at the gateway.  By the time such overflows occur, the
   gateway is congested.  Jacobson writes that the Slow-start policy is
   intended to function best with a complementary gateway policy

1.1  Definitions

   The characteristics of the Internet that we are interested in include
   that it is, in general, an arbitrary mesh-connected network.  The
   internetwork protocol is connectionless.  The number of users that
   place demands on the network is not limited by any explicit
   mechanism; no reservation of resources occurs and transport layer
   set-ups are not disallowed due to lack of resources.  A path from a
   source to destination host may have multiple hops, through several
   gateways and links.  Paths through the Internet may be heterogeneous
   (though homogeneous paths also exist and experience congestion).
   That is, links may be of different speeds.  Also, the gateways and
   hosts may be of different speeds or may be providing only a part of
   their processing power to communication-related activity.  The
   buffers for storing information flowing through Internet gateways are
   finite.  The nature of the internet protocol is to drop packets when
   these buffers overflow.

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   Gateway congestion arises when the demand for one or more of the
   resources of the gateway exceeds the capacity of that resource.  The
   resources include transmission links, processing, and space used for
   buffering.  Operationally, uncongested gateways operate with little
   queueing on average, where the queue is the waiting line for a
   particular resource of the gateway.  One commonly used quantitative
   definition [Kle79] for when a resource is congested is when the
   operating point is greater than the point at which resource power is
   maximum, where resource power is defined as the ratio of throughput
   to delay (See Section 2.2).  At this operating point, the average
   queue size is close to one, including the packet in service.  Note
   that this is a long-term average queue size.  Several definitions
   exist for the timescale of averaging for congestion detection and
   control, such as dominant round-trip time and queue regeneration
   cycle (see Section 2.1).

   Mechanisms for handling congestion may be divided into two
   categories, congestion recovery and congestion avoidance.  Congestion
   recovery tries to restore an operating state, when demand has already
   exceeded capacity.  Congestion avoidance is preventive in nature.  It
   tries to keep the demand on the network at or near the point of
   maximum power, so that congestion never occurs.  Without congestion
   recovery, the network may cease to operate entirely (zero
   throughput), whereas the Internet has been operating without
   congestion avoidance for a long time.  Overall performance may
   improve with an effective congestion avoidance mechanism.  Even if
   effective congestion avoidance was in place, congestion recovery
   schemes would still be required, to retain throughput in the face of
   sudden changes (increase of demand, loss of resources) that can lead
   to congestion.

   In this paper, the term "user" refers to each individual transport
   (TCP or another transport protocol) entity.  For example, a TCP
   connection is a "user" in this terminology.  The terms "flow" and
   "stream" are used by some authors in the same sense.  Some of the
   congestion control policies discussed in this paper, such as
   Selective Feedback Congestion Indication and Fair Queueing aggregate
   multiple TCP connections from a single host (or between a source
   host-destination host pair) as a virtual user.

   The term "cooperating transport entities" will be defined as a set of
   TCP connections (for example) which follow an effective method of
   adjusting their demand on the Internet in response to congestion.
   The most restrictive interpretation of this term is that the
   transport entities follow identical algorithms for congestion control
   and avoidance.  However, there may be some variation in these
   algorithms.  The extent to which heterogeneous end-system congestion
   control and avoidance may be accommodated by gateway policies should

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   be a subject of future research. The role played in Internet
   performance of non-cooperating transport entities is discussed in
   Section 5.

1.2  Goals and Scope of This Paper

   The task remains for Internet implementors to determine effective
   mechanisms for controlling gateway congestion.  There has been
   minimal common practice on which to base recommendations for Internet
   gateway congestion control.  In this survey, we describe the
   characteristics of one experimental gateway congestion management
   policy, Random Drop, and several that are better-known:  Source
   Quench, Congestion Indication, Selective Feedback Congestion
   Indication, and Fair Queueing, both Bit-Round and Stochastic.  A
   motivation for documenting Random Drop is that it has as primary
   goals low overhead and suitability for scaling up for Internets with
   higher speed links.  Both of these are important goals for future
   gateway implementations that will have fast links, fast processors,
   and will have to serve large numbers of interconnected hosts.

   The structure of this paper is as follows.  First, we discuss
   performance goals, including timescale and fairness considerations.
   Second, we discuss the gateway congestion control policies.  Random
   Drop is sketched out, with a recommendation for using it for
   congestion recovery and a separate section on its use as congestion
   avoidance.  Third, since gateway congestion control in itself does
   not change the end-systems' demand, we briefly present the effective
   responses to these policies by two end-system congestion control
   schemes, Slow-start and End-System Congestion Indication.  Among our
   conclusions, we address the issues of transport entities that do not
   cooperate with gateway congestion control.  As an appendix, because
   of the potential interactions with gateway congestion policies, we
   report on a scheme to help in controlling the performance of Internet
   gateways to connection-oriented subnets (in particular, X.25).

   Resources in the current Internet are not charged to users of them.
   Congestion avoidance techniques cannot be expected to help when users
   increase beyond the capacity of the underlying facilities.  There are
   two possible solutions for this, increase the facilities and
   available bandwidth, or forcibly reduce the demand.  When congestion
   is persistent despite implemented congestion control mechanisms,
   administrative responses are needed.  These are naturally not within
   the scope of this paper.  Also outside the scope of this paper are
   routing techniques that may be used to relocate demand away from
   congested individual resources (e.g., path-splitting and load-

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2.  Performance Goals

   To be able to discuss design and use of various mechanisms for
   improving Internetwork performance, we need to have clear performance
   goals for the operation of gateways and sets of end-systems.
   Internet experience shows that congestion control should be based on
   adaptive principles; this requires efficient computation of metrics
   by algorithms for congestion control.  The first issue is that of the
   interval over which these metrics are estimated and/or measured.

2.1  Interval for Measurement/Estimation of Performance Metrics

   Network performance metrics may be distorted if they are computed
   over intervals that are too short or too long relative to the dynamic
   characteristics of the network.  For instance, within a small
   interval, two FTP users with equal paths may appear to have sharply
   different demands, as an effect of brief, transient fluctuations in
   their respective processing.  An overly long averaging interval
   results in distortions because of the changing number of users
   sharing the resource measured during the time.  It is similarly
   important for congestion control mechanisms exerted at end systems to
   find an appropriate interval for control.

   The first approach to the monitoring, or averaging, interval for
   congestion control is one based on round-trip times.  The rationale
   for it is as follows:  control mechanisms must adapt to changes in
   Internet congestion as quickly as possible.  Even on an uncongested
   path, changed conditions will not be detected by the sender faster
   than a round-trip time.  The effect of a sending end-system's control
   will also not be seen in less than a round-trip time in the entire
   path as well as at the end systems.  For the control mechanism to be
   adaptive, new information on the path is needed before making a
   modification to the control exerted.  The statistics and metrics used
   in congestion control must be able to provide information to the
   control mechanism so that it can make an informed decision.
   Transient information which may be obsolete before a change is made
   by the end-system should be avoided.  This implies the
   monitoring/estimating interval is one lasting one or more round
   trips.  The requirements described here give bounds on:

      How short an interval:  not small enough that obsolete information
      is used for control;

      How long:  not more than the period at which the end-system makes

   But, from the point of view of the gateway congestion control policy,
   what is a round-trip time?  If all the users of a given gateway have

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   the same path through the Internet, they also have the same round-
   trip time through the gateway.  But this is rarely the case.

   A meaningful interval must be found for users with both short and
   long paths. Two approaches have been suggested for estimating this
   dynamically, queue regeneration cycle and frequency analysis.

   Use of the queue regeneration cycle has been described as part of the
   Congestion Indication policy.  The time period used for averaging
   here begins when a resource goes from the idle to busy state.  The
   basic interval for averaging is a "regeneration cycle" which is in
   the form of busy and idle intervals.  Because an average based on a
   single previous regeneration may become old information, the
   recommendation in [JRC87] is to average over the sum of two
   intervals, that is, the previous (busy and idle) period, and the time
   since the beginning of the current busy period.

   If the gateway users are window-based transport entities, it is
   possible to see how the regeneration interval responds to their
   round-trip times.  If a user with a long round-trip time has the
   dominant traffic, the queue length may be zero only when that user is
   awaiting acknowledgements.  Then the users with short paths will
   receive gateway congestion information that is averaged over several
   of their round-trip times.  If the short path traffic dominates the
   activity in the gateway, i.e., the connections with shorter round-
   trip times are the dominant users of the gateway resources, then the
   regeneration interval is shorter and the information communicated to
   them can be more timely. In this case, users with longer paths
   receive, in one of their round-trip times, multiple samples of the
   dominant traffic; the end system averaging is based on individual
   user's intervals, so that these multiple samples are integrated
   appropriately for these connections with longer paths.

   The use of frequency analysis has been described by [Jac89]. In this
   approach, the gateway congestion control is done at intervals based
   on spectral analysis of the traffic arrivals.  It is possible for
   users to have round-trip times close to each other, but be out of
   phase from each other. A spectral analysis algorithm detects this.
   Otherwise, if multiple round-trip times are significant, multiple
   intervals will be identified.  Either one of these will be
   predominant, or several will be comparable. An as yet difficult
   problem for the design of algorithms accomplishing this technique is
   the likelihood of "locking" to the frequency of periodic traffic of
   low intensity, such as routing updates.

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2.2  Power and its Relationship to the Operating Point

   Performance goals for a congestion control/avoidance strategy embody
   a conflict in that they call for as high a throughput as possible,
   with as little delay as possible.  A measure that is often used to
   reflect the tradeoff between these goals is power, the ratio of
   throughput to delay.  We would like to maximize the value of power
   for a given resource.  In the standard expression for power,

     Power = (Throughput^alpha)/Delay

   the exponent alpha is chosen for throughput, based on the relative
   emphasis placed on throughput versus delay: if throughput is more
   important, then a value of A alpha greater than one is chosen.  If
   throughput and delay are equally important (e.g., both bulk transfer
   traffic and interactive traffic are equally important), then alpha
   equal to one is chosen. The operating point where power is maximized
   is the "knee" in the throughput and delay curves.  It is desirable
   that the operating point of the resource be driven towards the knee,
   where power is maximized.  A useful property of power is that it is
   decreasing whether the resource is under- or over-utilized relative
   to the knee.

   In an internetwork comprising nodes and links of diverse speeds and
   utilization, bottlenecks or concentrations of demand may form.  Any
   particular user can see a single bottleneck, which is the slowest or
   busiest link or gateway in the path (or possibly identical "balanced"
   bottlenecks).  The throughput that the path can sustain is limited by
   the bottleneck.  The delay for packets through a particular path is
   determined by the service times and queueing at each individual hop.
   The queueing delay is dominated by the queueing at the bottleneck
   resource(s).  The contribution to the delay over other hops is
   primarily the service time, although the propagation delay over
   certain hops, such as a satellite link, can be significant.  We would
   like to operate all shared resources at their knee and maximize the
   power of every user's bottleneck.

   The above goal underscores the significance of gateway congestion
   control.  If techniques can be found to operate gateways at their
   resource knee, it can improve Internet performance broadly.

2.3  Fairness

   We would like gateways to allocate resources fairly to users.  A
   concept of fairness is only relevant when multiple users share a
   gateway and their total demand is greater than its capacity.  If
   demands were equal, a fair allocation of the resource would be to
   provide an equal share to each user.  But even over short intervals,

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   demands are not equal.  Identifying the fair share of the resource
   for the user becomes hard.  Having identified it, it is desirable to
   allocate at least this fair share to each user.  However, not all
   users may take advantage of this allocation.  The unused capacity can
   be given to other users.  The resulting final allocation is termed a
   maximally fair allocation.  [RJC87] gives a quantitative method for
   comparing the allocation by a given policy to the maximally fair

   It is known that the Internet environment has heterogeneous transport
   entities, which do not follow the same congestion control policies
   (our definition of cooperating transports). Then, the controls given
   by a gateway may not affect all users and the congestion control
   policy may have unequal effects.  Is "fairness" obtainable in such a
   heterogeneous community?  In Fair Queueing, transport entities with
   differing congestion control policies can be insulated from each
   other and each given a set share of gateway bandwidth.

   It is important to realize that since Internet gateways cannot refuse
   new users, fairness in gateway congestion control can lead to all
   users receiving small (sub-divided) amounts of the gateway resources
   inadequate to meet their performance requirements.  None of the
   policies described in this paper currently addresses this.  Then,
   there may be policy reasons for unequal allocation of the gateway
   resources.  This has been addressed by Bit-Round Fair Queueing.

2.4  Network Management

   Network performance goals may be assessed by measurements in either
   the end-system or gateway frame of reference.  Performance goals are
   often resource-centered and the measurement of the global performance
   of "the network," is not only difficult to measure but is also
   difficult to define.  Resource-centered metrics are more easily
   obtained, and do not require synchronization.  That resource-centered
   metrics are appropriate ones for use in optimization of power is
   shown by [Jaf81].

   It would be valuable for the goal of developing effective gateway
   congestion handling if Management Information Base (MIB) objects
   useful for evaluating gateway congestion were developed.  The
   reflections on the control interval described above should be applied
   when network management applications are designed for this purpose.
   In particular, obtaining an instantaneous queue length from the
   managed gateway is not meaningful for the purposes of congestion

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3.  Gateway Congestion Control Policies

   There have been proposed a handful of approaches to dealing with
   congestion in the gateway. Some of these are Source Quench, Random
   Drop, Congestion Indication, Selective Feedback Congestion
   Indication, Fair Queueing, and Bit-Round Fair Queueing.  They differ
   in whether they use a control message, and indeed, whether they view
   control of the end-systems as necessary, but none of them in itself
   lowers the demand of users and consequent load on the network.  End-
   system policies that reduce demand in conjunction with gateway
   congestion control are described in Section 4.

3.1  Source Quench

   The method of gateway congestion control currently used in the
   Internet is the Source Quench message of the RFC-792 [Pos81a]
   Internet Control Message Protocol (ICMP). When a gateway responds to
   congestion by dropping datagrams, it may send an ICMP Source Quench
   message to the source of the dropped datagram.  This is a congestion
   recovery policy.

   The Gateway Requirements RFC, RFC-1009 [GREQ87], specifies that
   gateways should only send Source Quench messages with a limited
   frequency, to conserve CPU resources during the time of heavy load.
   We note that operating the gateway for long periods under such loaded
   conditions should be averted by a gateway congestion control policy.
   A revised Gateway Requirements RFC is being prepared by the IETF.

   Another significant drawback of the Source Quench policy is that its
   details are discretionary, or, alternatively, that the policy is
   really a family of varied policies.  Major Internet gateway
   manufacturers have implemented a variety of source quench
   frequencies.  It is impossible for the end-system user on receiving a
   Source Quench to be certain of the circumstances in which it was
   issued.  This makes the needed end-system response problematic:  is
   the Source Quench an indication of heavy congestion, approaching
   congestion, a burst causing massive overload, or a burst slightly
   exceeding reasonable load?

   To the extent that gateways drop the last arrived datagram on
   overload, Source Quench messages may be distributed unfairly.  This
   is because the position at the end of the queue may be unfairly often
   occupied by the packets of low demand, intermittent users, since
   these do not send regular bursts of packets that can preempt most of
   the queue space.

   [Fin89] developed algorithms for when to issue Source Quench and for
   responding to it with a rate-reduction in the IP layer on the sending

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   host.  The system controls end-to-end performance of connections in a
   manner similar to the congestion avoidance portion of Slow-start TCP

3.2  Random Drop

   Random Drop is a gateway congestion control policy intended to give
   feedback to users whose traffic congests the gateway by dropping
   packets on a statistical basis.  The key to this policy is the
   hypothesis that a packet randomly selected from all incoming traffic
   will belong to a particular user with a probability proportional to
   the average rate of transmission of that user.  Dropping a randomly
   selected  packet results in users which generate much traffic having
   a greater number of packets dropped compared with those generating
   little traffic.  The selection of packets to be dropped is completely
   uniform.  Therefore, a user who generates traffic of an amount below
   the "fair share" (as defined in Section 2.3) may also experience a
   small amount of packet loss at a congested gateway. This character of
   uniformity is in fact a primary goal that Random Drop attempts to

   The other primary goal that Random Drop attempts to achieve is a
   theoretical overhead which is scaled to the number of shared
   resources in the gateway rather than the number of its users.  If a
   gateway congestion algorithm has more computation the more users
   there are, this can lead to processing demands that are higher as
   congestion increases.  Also the low-overhead goal of Random Drop
   addresses concerns about the scale of gateway processing that will be
   required in the mid-term Internet as gateways with fast processors
   and links are shared by very large active sets of users.

3.2.1  For Congestion Recovery

   Random Drop has been proposed as an improvement to packet dropping at
   the operating point where the gateway's packet buffers overflow.
   This is using Random Drop strictly as a congestion recovery

   In Random Drop congestion recovery, instead of dropping the last
   packet to arrive at the queue, a packet is selected randomly from the
   queue.  Measurements of an implementation of Random Drop Congestion
   Recovery [Man90] showed that a user with a low demand, due to a
   longer round-trip time path than other users of the gateway, had a
   higher drop rate with RDCR than without.  The throughput accorded to
   users with the same round-trip time paths was nearly equal with RDCR
   as compared to without it.  These results suggest that RDCR should be
   avoided unless it is used within a scheme that groups traffic more or
   less by round-trip time.

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3.2.2  For Congestion Avoidance

   Random Drop is also proposed as a congestion avoidance policy
   [Jac89].  The intent is to initiate dropping packets when the gateway
   is anticipated to become congested and remain so unless there is some
   control exercised.  This  implies  selection  of  incoming packets to
   be randomly dropped at a rate derived from identifying the level of
   congestion at the gateway.  The rate is the number of arrivals
   allowed between drops. It depends on the current operating point and
   the prediction of congestion.

   A part of the policy is to determine that congestion will soon occur
   and that the gateway is beginning to operate beyond the knee of the
   power curve.  With a suitably chosen interval (Section 2.1), the
   number of packets from each individual user in a sample over that
   interval is proportional to each user's demand on the gateway.  Then,
   dropping one or more random packets indicates to some user(s) the
   need to reduce the level of demand that is driving the gateway beyond
   the desired operating point.  This is the goal that a policy of
   Random Drop for congestion avoidance attempts to achieve.

   There are several parameters to be determined for a Random Drop
   congestion avoidance policy. The first is an interval, in terms of
   number of packet arrivals, over which packets are dropped with
   uniform probability.  For instance, in a sample implementation, if
   this interval spanned 2000 packet arrivals, and a suitable
   probability of drop was 0.001, then two random variables would be
   drawn in a uniform distribution in the range of 1 to 2,000.  The
   values drawn would be used by counting to select the packets dropped
   at arrival.  The second parameter is the value for the probability of
   drop.  This parameter would be a function of an estimate of the
   number of users, their appropriate control intervals, and possibly
   the length of time that congestion has persisted.  [Jac89] has
   suggested successively increasing the probability of drop when
   congestion persists over multiple control intervals.  The motivation
   for increasing the packet drop probability is that the implicit
   estimate of the number of users and random selection of their packets
   to drop does not guarantee that all users have received enough
   signals to decrease demand.  Increasing the probability of drop
   increases the probability that enough feedback is provided.
   Congestion detection is also needed in Random Drop congestion
   avoidance, and could be implemented in a variety of ways.  The
   simplest is a static threshold, but dynamically averaged measures of
   demand or utilization are suggested.

   The packets dropped in Random Drop congestion avoidance would not be
   from the initial inputs to the gateway.  We suggest that they would
   be selected only from packets destined for the resource which is

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   predicted to be approaching congestion.  For example, in the case of
   a gateway with multiple outbound links, access to each individual
   link is treated as a separate resource, the Random Drop policy is
   applied at each link independently.  Random Drop congestion avoidance
   would provide uniform treatment of all cooperating transport users,
   even over individual patterns of traffic multiplexed within one
   user's stream.  There is no aggregation of users.

   Simulation studies [Zha89], [Has90] have presented evidence that
   Random Drop is not fair across cooperating and non-cooperating
   transport users.  A transport user whose sending policies included
   Go-Back-N retransmissions and did not include Slow-start received an
   excessive share of bandwidth from a simple implementation of Random
   Drop.  The simultaneously active Slow-start users received unfairly
   low shares.  Considering this, it can be seen that when users do not
   respond to control, over a prolonged period, the Random Drop
   congestion avoidance mechanism would have an increased probability of
   penalizing users with lower demand.  Their packets dropped, these
   users exert the controls leading to their giving up bandwidth.

   Another problem can be seen to arise in Random Drop [She89] across
   users whose communication paths are of different lengths.  If the
   path spans congested resources at multiple gateways, then the user's
   probability of receiving an unfair drop and subsequent control (if
   cooperating) is exponentially increased.  This is a significant
   scaling problem.

   Unequal paths cause problems for other congestion avoidance policies
   as well.  Selective Feedback Congestion Indication was devised to
   enhance Congestion Indication specifically because of the problem of
   unequal paths.  In Fair Queueing by source-destination pairs, each
   path gets its own queue in all the gateways.

3.3  Congestion Indication

   The Congestion Indication policy is often referred to as the DEC Bit
   policy. It was developed at DEC [JRC87], originally for the Digital
   Network Architecture (DNA).  It has also been specified for the
   congestion avoidance of the ISO protocols TP4 and CLNP [NIST88].
   Like Source Quench, it uses explicit communications from the
   congested gateway to the user.  However, to use the lowest possible
   network resources for indicating congestion, the information is
   communicated in a single bit, the Congestion Experienced Bit, set in
   the network header of the packets already being forwarded by a
   gateway.  Based on the condition of this bit, the end-system user
   makes an adjustment to the sending window. In the NSP transport
   protocol of DECNET, the source makes an adjustment to its window; in
   the ISO transport protocol, TP4, the destination makes this

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   adjustment in the window offered to the sender.

   This policy attempts to avoid congestion by setting the bit whenever
   the average queue length over the previous queue regeneration cycle
   plus part of the current cycle is one or more.  The feedback is
   determined independently at each resource.

3.4  Selective Feedback Congestion Indication

   The simple Congestion Indication policy works based upon the total
   demand on the gateway.  The total number of users or the fact that
   only a few of the users might be causing congestion is not
   considered.  For fairness, only those users who are sending more than
   their fair share should be asked to reduce their load, while others
   could attempt to increase where possible.  In Selective Feedback
   Congestion Indication, the Congestion Experienced Bit is used to
   carry out this goal.

   Selective Feedback works by keeping a count of the number of packets
   sent by different users since the beginning of the queue averaging
   interval.  This is equivalent to monitoring their throughputs. Based
   on the total throughput, a fair share for each user is determined and
   the congestion bit is set, when congestion approaches, for the users
   whose demand is higher than their fair share.  If the gateway is
   operating below the throughput-delay knee, congestion indications are
   not set.

   A min-max algorithm used to determine the fair share of capacity and
   other details of this policy are described in [RJC87].  One parameter
   to be computed is the capacity of each resource to be divided among
   the users.  This metric depends on the distribution of inter-arrival
   times and packet sizes of the users.  Attempting to determine these
   in real time in the gateway is unacceptable.  The capacity is instead
   estimated from on the throughput seen when the gateway is operating
   in congestion, as indicated by the average queue length.  In
   congestion (above the knee), the service rate of the gateway limits
   its throughput.  Multiplying the throughput obtained at this
   operating point by a capacity factor (between 0.5 and 0.9) to adjust
   for the distributions yields an acceptable capacity estimate in

   Selective Feedback Congestion Indication takes as input a vector of
   the number of packets sent by each source-destination pair of end-
   systems.  Other alternatives include 1) destination address, 2)
   input/output link, and 3) transport connection (source/destination
   addresses and ports).

   These alternatives give different granularities for fairness.  In the

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   case where paths are the same or round-trip times of users are close
   together, throughputs are equalized similarly by basing the selective
   feedback on source or destination address.  In fact, if the RTTs are
   close enough, the simple congestion indication policy would result in
   a fair allocation.  Counts based on source/destination pairs ensure
   that paths with different lengths and network conditions get a fair
   throughput at the individual gateways.  Counting packets based on
   link pairs has a low overhead, but may result in unfairness to users
   whose demand is below the fair share and are using a long path.
   Counts based on transport layer connection identifiers, if this
   information was available to Internet gateways, would make good
   distinctions, since the differences of demand of different
   applications and instances of applications would be separately

   Problems with Selective Feedback Congestion Indication include that
   the gateway has significant processing to do.  With the feasible
   choice of aggregation at the gateway, unfairness across users within
   the group is likely.  For example, an interactive connection
   aggregated with one or more bulk transfer connections will receive
   congestion indications though its own use of the gateway resources is
   very low.

3.5  Fair Queueing

   Fair Queueing is the policy of maintaining separate gateway output
   queues for individual end-systems by source-destination pair.  In the
   policy as proposed by [Nag85], the gateway's processing and link
   resources are distributed to the end-systems on a round-robin basis.
   On congestion, packets are dropped from the longest queue.  This
   policy leads to equal allocations of resources to each source-
   destination pair.  A source-destination pair that demands more than a
   fair share simply increases its own queueing delay and congestion

3.5.1  Bit-Round Fair Queueing

   An enhancement of Nagle Fair Queueing, the Bit-Round Fair Queuing
   algorithm described and simulated by [DKS89] addresses several
   shortcomings of Nagle's scheme. It computes the order of service to
   packets using their lengths, with a technique that emulates a bit-
   by-bit round-robin discipline, so that long packets do not get an
   advantage over short ones.  Otherwise the round-robin would be
   unfair, for example, giving more bandwidth to hosts whose traffic is
   mainly long packets than to hosts sourcing short packets.

   The aggregation of users of a source-destination pair by Fair
   Queueing has the property of grouping the users whose round-trips are

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   similar. This may be one reason that the combination of Bit-Round
   Fair Queueing with Congestion Indication had particularly good
   simulated performance in [DKS89].

   The aggregation of users has the expected drawbacks, as well.  A
   TELNET user in a queue with an FTP user does not get delay benefits;
   and host pairs with high volume of connections get treated the same
   as a host pair with small number of connections and as a result gets
   unfair services.

   A problem can be mentioned about Fair Queueing, though it is related
   to implementation, and perhaps not properly part of a policy
   discussion.  This is a concern that the resources (processing) used
   for determining where to queue incoming packets would themselves be
   subject to congestion, but not to the benefits of the Fair Queuing
   discipline.  In a situation where the gateway processor was not
   adequate to the demands on it, the gateway would need an alternative
   policy for congestion control of the queue awaiting processing.
   Clever implementation can probably find an efficient way to route
   packets to the queues they belong in before other input processing is
   done, so that processing resources can be controlled, too.  There is
   in addition, the concern that bit-by-bit round FQ requires non-FCFS
   queueing even within the same source destination pairs to allow for
   fairness to different connections between these end systems.

   Another potential concern about Fair Queueing is whether it can scale
   up to gateways with very large source-destination populations.  For
   example, the state in a Fair Queueing implementation scales with the
   number of active end-to-end paths, which will be high in backbone

3.5.2  Stochastic Fairness Queuing

   Stochastic Fairness Queueing (SFQ) has been suggested as a technique
   [McK90] to address the implementation issues relating to Fair
   Queueing.  The first overhead that is reduced is that of looking up
   the source-destination address pair in an incoming packet and
   determining which queue that packet will have to be placed in.  SFQ
   does not require as many memory accesses as Fair Queueing to place
   the packet in the appropriate queue.  SFQ is thus claimed to be more
   amenable to implementation for high-speed networks [McK90].

   SFQ uses a simple hash function to map from the source-destination
   address pair to a fixed set of queues.  Since the assignment of an
   address pair to a queue is probabilistic, there is the likelihood of
   multiple address pairs colliding and mapping to the same queue.  This
   would potentially degrade the additional fairness that is gained with
   Fairness Queueing.  If two or more address pairs collide, they would

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   continue to do so.  To deal with the situation when such a collision
   occurs, SFQ periodically perturbs the hash function so that these
   address pairs will be unlikely to collide subsequently.

   The price paid for achieving this implementation efficiency is that
   SFQ requires a potentially large number of queues (we must note
   however, that these queues may be organized orthogonally from the
   buffers in which packets are stored. The buffers themselves may be
   drawn from a common pool, and buffers in each queue organized as a
   linked list pointed to from each queue header).  For example, [McK90]
   indicates that to get good, consistent performance, we may need to
   have up to 5 to 10 times the number of active source-destination
   pairs. In a typical gateway, this may require around 1000 to 2000

   [McK90] reports simulation results with SFQ. The particular hash
   function that is studied is using the HDLC's cyclic redundancy check
   (CRC).  The hash function is perturbed by multiplying each byte by a
   sequence number in the range 1 to 255 before applying the CRC.  The
   metric considered is the standard deviation of the number of packets
   output per source-destination pair.  A perfectly fair policy would
   have a standard deviation of zero and an unfair policy would have a
   large standard deviation.  In the example studied (which has up to 20
   source-destination (s-d) pairs going through a single overloaded
   gateway), SFQ with 1280 queues (i.e., 64 times the number of source-
   destination pairs), approaches about 3 times the standard deviation
   of Fairness Queueing.  This must be compared to a FCFS queueing
   discipline having a standard deviation which is almost 175 times the
   standard deviation of Fairness Queueing.

   It is conjectured in [McK90] that a good value for the interval in
   between perturbations of the hash function appears to be in the area
   between twice the queue flush time of the stochastic fairness queue
   and one-tenth the average conversation time between a source-
   destination pair.

   SFQ also may alleviate the anticipated scaling problems that may be
   an issue with Fair Queueing by not strictly requiring the number of
   queues equal to the possible source-destination pairs that may be
   presenting a load on a particular gateway. However, SFQ achieves this
   property by trading off some of the fairness for implementation

   [McK90] examines alternative strategies for implementation of Fair
   Queueing and SFQ and estimates their complexity on common hardware
   platforms (e.g., a Motorola 68020).  It is suggested that mapping an
   IP address pair may require around 24 instructions per packet for
   Fair Queueing in the best case; in contrast SFQ requires 10

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   instructions in the worst case.  The primary source of this gain is
   that SFQ avoids a comparison of the s-d address pair on the packet to
   the identity of the queue header.  The relative benefit of SFQ over
   Fair Queueing is anticipated to be greater when the addresses are

   SFQ offers promising implemenatation benefits.  However, the price to
   be paid in terms of having a significantly larger number of queues
   (and the consequent data structures and their management) than the
   number of s-d pairs placing a load on the gateway is a concern.  SFQ
   is also attractive in that it may be used in concert with the DEC-bit
   scheme for Selective Feedback Congestion Indication to provide
   fairness as well as congestion avoidance.


   Ideally in gateway congestion control, the end-system transport
   entities adjust (decrease) their demand in response to control
   exerted by the gateway.  Schemes have been put in practice for
   transport entities to adjust their demand dynamically in response to
   congestion feedback.  To accomplish this, in general, they call for
   the user to gradually increase demand until information is received
   that the load on the gateway is too high.  In response to this
   information, the user decreases load, then begins an exploratory
   increases again.  This cycle is repeated continuously, with the goal
   that the total demand will oscillate around the optimal level.

   We have already noted that a Slow-start connection may give up
   considerable bandwidth when sharing a gateway with aggressive
   transport entities.  There is currently no way to enforce that end-
   systems use a congestion avoidance policy, though the Host
   Requirements RFC [HR89] has defined Slow-start as mandatory for TCP.
   This adverse effect on Slow-start connections is mitigated by the
   Fair Queueing policy.  Our conclusions discuss further the
   coexistence of different end-system strategies.

   This section briefly presents two fielded transport congestion
   control and avoidance schemes, Slow-start and End-System Congestion
   Indication, and the responses by means of which they cooperate with
   gateway policies.  They both use the control paradigm described
   above.  Slow-start, as mentioned in Section 1, was developed by
   [Jac88], and widely fielded in the BSD TCP implementation.  End-
   system Congestion Indication was developed by [JRC87].  It is fielded
   in DEC's Digital Network Architecture, and has been specified as well
   for ISO TP4 [NIST88].

   Both Slow-start and End-system Congestion Indication view the
   relationship between users and gateways as a control system. They

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   have feedback and control components, the latter further broken down
   into a procedure bringing a new connection to equilibrium, and a
   procedure to maintain demand at the proper level.  They make use of
   policies for increasing and decreasing the transport sender's window
   size.  These require the sender to follow a set of self-restraining
   rules which dynamically adjust the send window whenever congestion is

   A predecessor of these, CUTE, developed by [Jai86], introduced the
   use of retransmission timeouts as congestion feedback.  The Slow-
   start scheme was also designed to use timeouts in the same way.  The
   End-System policies for Congestion Indication use the Congestion
   Experienced Bit delivered in the network header as the primary
   feedback, but retransmission timeouts also provoke an end-system
   congestion response.

   In reliable transport protocols like TCP and TP4, the retransmission
   timer must do its best to satisfy two conflicting goals. On one hand,
   the timer must rapidly detect lost packets. And, on the other hand,
   the timer must minimize false alarms.  Since the retransmit timer is
   used as a congestion signal in these end-system policies, it is all
   the more important that timeouts reliably correspond to packet drops.
   One important rule for retransmission is to avoid bad sampling in the
   measurements that will be used in estimating the round-trip delay.
   [KP87] describes techniques to ensure accurate sampling.  The Host
   Requirements RFC [HR89] makes these techniques mandatory for TCP.

   The utilization of a resource can be defined as the ratio of its
   average arrival rate to its mean service rate. This metric varies
   between 0 and 1.0. In a state of congestion, one or more resources
   (link, gateway buffer, gateway CPU) has a utilization approaching
   1.0.  The average delay (round trip time) and its variance approach
   infinity. Therefore, as the utilization of a network increases, it
   becomes increasingly important to take into account the variance of
   the round trip time in estimating it for the retransmission timeout.

   The TCP retransmission timer is based on an estimate of the round
   trip time.  [Jac88] calls the round trip time estimator the single
   most important feature of any protocol implementation that expects to
   survive a heavy load. The retransmit timeout procedure in RFC-793
   [Pos81b] includes a fixed parameter, beta, to account for the
   variance in the delay. An estimate of round trip time using the
   suggested values for beta is valid for a network which is at most 30%
   utilized.  Thus, the RFC-793 retransmission timeout estimator will
   fail under heavy congestion, causing unnecessary retransmissions that
   add to the load, and making congestive loss detection impossible.

   Slow-start TCP uses the mean deviation as an estimate of the variance

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   to improve the estimate. As a rough view of what happens with the
   Slow-start retransmission calculation, delays can change by
   approximately two standard deviations without the timer going off in
   a false alarm.  The same method of estimation may be applicable to
   TP4.  The procedure is:

           Error     = Measured - Estimated
           Estimated = Estimated + Gain_1 * Error
           Deviation = Deviation + Gain_2 * (|Error| - Deviation)
           Timeout   = Estimated + 2 * Deviation

           Where:  Gain_1, Gain_2 are gain factors.

4.1  Response to No Policy in Gateway

   Since packets must be dropped during congestion because of the finite
   buffer space, feedback of congestion to the users exists even when
   there is no gateway congestion policy.  Dropping the packets is an
   attempt to recover from congestion, though it needs to be noted that
   congestion collapse is not prevented by packet drops if end-systems
   accelerate their sending rate in these conditions.  The accurate
   detection of congestive loss by all retransmission timers in the
   end-systems is extremely important for gateway congestion recovery.

4.2  Response to Source Quench

   Given that a Source Quench message has ambiguous meaning, but usually
   is issued for congestion recovery, the response of Slow-start to a
   Source Quench is to return to the beginning of the cycle of increase.
   This is an early response, since the Source Quench on overflow will
   also lead to a retransmission timeout later.

4.3 Response to Random Drop

   The end-system's view of Random Drop is the same as its view of loss
   caused by overflow at the gateway. This is a drawback of the use of
   packet drops as congestion feedback for congestion avoidance: the
   decrease policy on congestion feedback cannot be made more drastic
   for overflows than for the drops the gateway does for congestion
   avoidance.  Slow-start responds rapidly to gateway feedback.  In a
   case where the users are cooperating (all Slow-start), a desired
   outcome would be that this responsiveness would lead quickly to a
   decreased probability of drop.  There would be, as an ideal, a self-
   adjusting overall amount of control.

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4.4  Response to Congestion Indication

   Since the Congestion Indication mechanism attempts to keep gateways
   uncongested, cooperating end-system congestion control policies need
   not reduce demand as much as with other gateway policies.  The
   difference between the Slow-start response to packet drops and the
   End-System Congestion Indication response to Congestion Experienced
   Bits is primarily in the decrease policy.  Slow-start decreases the
   window to one packet on a retransmission timeout.  End-System
   Congestion Indication decreases the window by a fraction (for
   instance, to 7/8 of the original value), when the Congestion
   Experienced Bit is set in half of the packets in a sample spanning
   two round-trip times (two windows full).

4.5  Response to Fair Queuing

   A Fair Queueing policy may issue control indications, as in the
   simulated Bit-Round Fair Queueing with DEC Bit, or it may depend only
   on the passive effects of the queueing.  When the passive control is
   the main effect (perhaps because most users are not responsive to
   control indications), the behavior of retransmission timers will be
   very important.  If retransmitting users send more packets in
   response to increases in their delay and drops, Fair Queueing will be
   prone to degraded performance, though collapse (zero throughput for
   all users) may be prevented for a longer period of time.

5.  Conclusions

   The impact of users with excessive demand is a driving force as
   proposed gateway policies come closer to implementation.  Random Drop
   and Congestion Indication can be fair only if the transport entities
   sharing the gateway are all cooperative and reduce demand as needed.
   Within some portions of the Internet, good behavior of end-systems
   eventually may be enforced through administration.  But for various
   reasons, we can expect non-cooperating transports to be a persistent
   population in the Internet.  Congestion avoidance mechanisms will not
   be deployed all at once, even if they are adopted as standards.
   Without enforcement, or even with penalties for excessive demand,
   some end-systems will never implement congestion avoidance

   Since it is outside the context of any of the gateway policies, we
   will mention here a suggestion for a relatively small-scale response
   to users which implement especially aggressive policies. This has
   been made experimentally by [Jac89].  It would implement a low
   priority queue, to which the majority of traffic is not routed.  The
   candidate traffic to be queued there would be identified by a cache
   of recent recipients of whatever control indications the gateway

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   policy makes.  Remaining in the cache over multiple control intervals
   is the criterion for being routed to the low priority queue.  In
   approaching or established congestion, the bandwidth given to the
   low-service queue is decreased.

   The goal of end-system cooperation itself has been questioned.  As
   [She89] points out, it is difficult to define.  A TCP implementation
   that retransmits before it determines that has been loss indicated
   and in a Go-Back-N manner is clearly non-cooperating.  On the other
   hand, a transport entity with selective acknowledgement may demand
   more from the gateways than TCP, even while responding to congestion
   in a cooperative way.

   Fair Queueing maintains its control of allocations regardless of the
   end-system congestion avoidance policies.  [Nag85] and [DKS89] argue
   that the extra delays and congestion drops that result from
   misbehavior could work to enforce good end-system policies.  Are the
   rewards and penalties less sharply defined when one or more
   misbehaving systems cause the whole gateway's performance to be less?
   While the tax on all users imposed by the "over-users" is much less
   than in gateways with other policies, how can it be made sufficiently

   In the sections on Selective Feedback Congestion Indication and Bit-
   Round Fair Queueing we have pointed out that more needs to be done on
   two particular fronts:

      How can increased computational overhead be avoided?

      The allocation of resources to source-destination pairs is, in
      many scenarios, unfair to individual users. Bit-Round Fair
      Queueing offers a potential administrative remedy, but even if it
      is applied, how should the unequal allocations be propagated
      through multiple gateways?

   The first question has been taken up by [McK90].

   Since Selective Feedback Congestion Indication (or Congestion
   Indication used with Fair Queueing) uses a network bit, its use in
   the Internet requires protocol support; the transition of some
   portions of the Internet to OSI protocols may make such a change
   attractive in the future.  The interactions between heterogeneous
   congestion control policies in the Internet will need to be explored.

   The goals of Random Drop Congestion Avoidance are presented in this
   survey, but without any claim that the problems of this policy can be
   solved.  These goals themselves, of uniform, dynamic treatment of
   users (streams/flows), of low overhead, and of good scaling

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   characteristics in large and loaded networks, are significant.

Appendix:  Congestion and Connection-oriented Subnets

   This section presents a recommendation for gateway implementation in
   an areas that unavoidably interacts with gateway congestion control,
   the impact of connection-oriented subnets, such as those based on

   The need to manage a connection oriented service (X.25) in order to
   transport datagram traffic (IP) can cause problems for gateway
   congestion control.  Being a pure datagram protocol, IP provides no
   information delimiting when a pair of IP entities need to establish a
   session between themselves.  The solution involves compromise among
   delay, cost, and resources.  Delay is introduced by call
   establishment when a new X.25 SVC (Switched Virtual Circuit) needs to
   be established, and also by queueing delays for the physical line.
   Cost includes any charges by the X.25 network service provider.
   Besides the resources most gateways have (CPU, memory, links), a
   maximum supported or permitted number of virtual circuits may be in

   SVCs are established on demand when an IP packet needs to be sent and
   there is no SVC established or pending establishment to the
   destination IP entity.  Optionally, when cost considerations, and
   sufficient numbers of unused virtual circuits allow, redundant SVCs
   may be established between the same pair of IP entities.  Redundant
   SVCs can have the effect of improving performance when coping with
   large end-to-end delay, small maximum packet sizes and small maximum
   packet windows.  It is generally preferred though, to negotiate large
   packet sizes and packet windows on a single SVC.  Redundant SVCs must
   especially be discouraged when virtual circuit resources are small
   compared with the number of destination IP entities among the active
   users of the gateway.

   SVCs are closed after some period of inactivity indicates that
   communication may have been suspended between the IP entities.  This
   timeout is significant in the operation of the interface.  Setting
   the value too low can result in closing of the SVC even though the
   traffic has not stopped.  This results in potentially large delays
   for the packets which reopen the SVC and may also incur charges
   associated with SVC calls.  Also, clearing of SVCs is, by definition,
   nongraceful.  If an SVC is closed before the last packets are
   acknowledged, there is no guarantee of delivery.  Packet losses are
   introduced by this destructive close independent of gateway traffic
   and congestion.

   When a new circuit is needed and all available circuits are currently

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   in use, there is a temptation to pick one to close (possibly using
   some Least Recently Used criterion).  But if connectivity demands are
   larger than available circuit resources, this strategy can lead to a
   type of thrashing where circuits are constantly being closed and
   reopened.  In the worst case, a circuit is opened, a single packet
   sent and the circuit closed (without a guarantee of packet delivery).
   To counteract this, each circuit should be allowed to remain open a
   minimum amount of time.

   One possible SVC strategy is to dynamically change the time a circuit
   will be allowed to remain open based on the number of circuits in
   use.  Three administratively controlled variables are used, a minimum
   time, a maximum time and an adaptation factor in seconds per
   available circuit.  In this scheme, a circuit is closed after it has
   been idle for a time period equal to the minimum plus the adaptation
   factor times the number of available circuits, limited by the maximum
   time.  By administratively adjusting these variables, one has
   considerable flexibility in adjusting the SVC utilization to meet the
   needs of a specific gateway.


   This paper is the outcome of discussions in the Performance and
   Congestion Control Working Group between April 1988 and July 1989.
   Both PCC WG and plenary IETF members gave us helpful reviews of
   earlier drafts.  Several of the ideas described here were contributed
   by the members of the PCC WG.  The Appendix was written by Art
   Berggreen.  We are particularly thankful also to Van Jacobson, Scott
   Shenker, Bruce Schofield, Paul McKenney, Matt Mathis, Geof Stone, and
   Lixia Zhang for participation and reviews.


   [DKS89] Demers, A., Keshav, S., and S. Shenker, "Analysis and
   Simulation of a Fair Queueing Algorithm", Proceedings of SIGCOMM '89.

   [Fin89] Finn, G., "A Connectionless Congestion Control Algorithm",
   Computer Communications Review, Vol. 19, No. 5, October 1989.

   [Gar87] Gardner, M., "BBN Report on the ARPANET", Proceedings of the
   McLean IETF, SRI-NIC IETF-87/3P, July 1987.

   [GREQ87] Braden R., and J. Postel, "Requirements for Internet
   Gateways", RFC 1009, USC/Information Sciences Institute, June 1987.

   [HREQ89] Braden R., Editor, "Requirements for Internet Hosts --
   Communications Layers", RFC 1122, Internet Engineering Task Force,
   October 1989.

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   [Has90] Hashem, E., "Random Drop Congestion Control", M.S. Thesis,
   Massachusetts Institute of Technology, Department of Computer
   Science, 1990.

   [Jac88] Jacobson, V., "Congestion Avoidance and Control", Proceedings
   of SIGCOMM '88.

   [Jac89] Jacobson, V., "Presentations to the IETF Performance and
   Congestion Control Working Group".

   [Jaf81] Jaffe, J., "Bottleneck Flow Control", IEEE Transactions on
   Communications, COM-29(7), July, 1981.

   [Jai86] Jain, R., "A Timeout-based Congestion Control Scheme for
   Window Flow-controlled Networks", IEEE Journal on Selected Areas in
   Communications, SAC-4(7), October 1986.

   [JRC87] Jain, R., Ramakrishnan, K., and D. Chiu, "Congestion
   Avoidance in Computer Networks With a Connectionless Network Layer",
   Technical Report DEC-TR-506, Digital Equipment Corporation.

   [Kle79] Kleinrock, L., "Power and Deterministic Rules of Thumb for
   Probabilistic Problems in Computer Communications",  Proceedings of
   the ICC '79.

   [KP87] Karn, P., and C. Partridge, "Improving Round Trip Estimates in
   Reliable Transport Protocols", Proceedings of SIGCOMM '87.

   [Man90] Mankin, A., "Random Drop Congestion Control", Proceedings of
   SIGCOMM '90.

   [McK90] McKenney, P., "Stochastic Fairness Queueing", Proceedings of
   INFOCOM '90.

   [Nag84] Nagle, J., "Congestion Control in IP/TCP Internetworks", RFC
   896, FACC Palo Alto, 6 January 1984.

   [Nag85] Nagle, J., "On Packet Switches With Infinite Storage", RFC
   970, FACC Palo Alto, December 1985.

   [NIST88] NIST, "Stable Implementation Agreements for OSI Protocols,
   Version 2, Edition 1", National Institute of Standards and Technology
   Special Publication 500-162, December 1988.

   [Pos81a] Postel, J., "Internet Control Message Protocol - DARPA
   Internet Program Protocol Specification", RFC-792, USC/Information
   Sciences Institute, September 1981.

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   [Pos81b] Postel, J., "Transmission Control Protocol - DARPA Internet
   Program Protocol Specification", RFC-793, DARPA, September 1981.

   [RJC87] Ramakrishnan, K., Jain, R., and D. Chiu, "A Selective Binary
   Feedback Scheme for General Topologies", Technical Report DEC-TR-510,
   Digital Equipment Corporation.

   [She89] Shenker, S., "Correspondence with the IETF Performance and
   Congestion Control Working Group".

   [Sp89] Spector, A., and M. Kazar, "Uniting File Systems", Unix
   Review, Vol.  7, No. 3, March 1989.

   [Zha89] Zhang, L., "A New Architecture for Packet Switching Network
   Protocols", Ph.D Thesis, Massachusetts Institute of Technology,
   Department of Computer Science, 1989.

Security Considerations

   Security issues are not discussed in this memo.

Authors' Addresses

   Allison Mankin
   The MITRE Corporation
   M/S W425
   7525 Colshire Drive
   McLean, VA  22102


   K.K. Ramakrishnan
   Digital Equipment Corporation
   M/S LKG1-2/A19
   550 King Street
   Littleton, MA  01754


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