rfc2309.txt

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Network Working Group                                 B. Braden, USC/ISI
Request for Comments: 2309                             D. Clark, MIT LCS
Category: Informational                                J. Crowcroft, UCL
                                                 B. Davie, Cisco Systems
                                               S. Deering, Cisco Systems
                                                          D. Estrin, USC
                                                          S. Floyd, LBNL
                                                       V. Jacobson, LBNL
                                                  G. Minshall, Fiberlane
                                                       C. Partridge, BBN
                                      L. Peterson, University of Arizona
                                      K. Ramakrishnan, ATT Labs Research
                                                  S. Shenker, Xerox PARC
                                                  J. Wroclawski, MIT LCS
                                                          L. Zhang, UCLA
                                                              April 1998



     Recommendations on Queue Management and Congestion Avoidance
                            in the Internet



Status of Memo

      This memo provides information for the Internet community.  It
      does not specify an Internet standard of any kind.  Distribution
      of this memo is unlimited.

Copyright Notice

      Copyright (C) The Internet Society (1998).  All Rights Reserved.

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.







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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
   mechanisms [RFC2001], 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



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   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].
   Similarly, the discussion of queue management and congestion control
   requirements for differential services is a separate issue.  However,
   we do not expect the deployment of integrated services and
   differential 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
   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.





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   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-
        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.






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   We know in general how to solve the full-queues problem for
   "responsive" flows, i.e., those flows that throttle back in response
   to congestion notification.  In the current Internet, dropped packets
   serve as a critical mechanism of congestion notification to end
   nodes.  The solution to the full-queues problem is for routers to
   drop packets before a queue becomes full, so that end nodes can
   respond to congestion before buffers overflow.  We call such a
   proactive approach "active queue management".  By dropping packets
   before buffers overflow, active queue management allows routers to
   control when and how many packets to drop.  The next section
   introduces RED, an active queue management mechanism that solves both
   problems listed above (given 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 an unavoidable aspect of packet networks
        [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
        packet drop.  Third, unnecessary packet drops represent a
        possible waste of bandwidth on the way to the drop point.

        We note that while RED can manage queue lengths and reduce end-
        to-end latency even in the absence of end-to-end congestion
        control, RED will be able to reduce packet dropping only in an
        environment that continues to be dominated by end-to-end
        congestion control.

   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



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        (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
        class-based scheduling algorithms such as 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