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Controlled Delay Active Queue Management

The information below is for an old version of the document.
Document Type
This is an older version of an Internet-Draft that was ultimately published as RFC 8289.
Expired & archived
Authors Kathleen Nichols , Van Jacobson , Andrew McGregor , Jana Iyengar
Last updated 2015-04-27 (Latest revision 2014-10-24)
Replaces draft-aqm-codel, draft-nichols-tsvwg-codel
RFC stream Internet Engineering Task Force (IETF)
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IESG IESG state Became RFC 8289 (Experimental)
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Active Queue Management and Packet Scheduling (aqm)           K. Nichols
Internet-Draft                                             Pollere, Inc.
Intended status: Informational                               V. Jacobson
Expires: April 3, 2015                                       A. McGregor
                                                              J. Iyengar
                                                        October 24, 2014

                Controlled Delay Active Queue Management


   The "persistently full buffer" problem has been discussed in the IETF
   community since the early 80's [RFC896].  The IRTF's End-to-End
   Working Group called for the deployment of active queue management
   (AQM) to solve the problem in 1998 [RFC2309].  Despite the awareness,
   the problem has only gotten worse as Moore's Law growth in memory
   density fueled an exponential increase in buffer pool size.  Efforts
   to deploy AQM have been frustrated by difficult configuration and
   negative impact on network utilization.  This problem, recently
   christened "bufferbloat", [TSVBB2011] [BB2011] has become
   increasingly important throughout the Internet but particularly at
   the consumer edge.

   This document describes a general framework called CoDel (Controlled
   Delay) [CODEL2012] that controls bufferbloat-generated excess delay
   in modern networking environments.  CoDel consists of an estimator, a
   setpoint, and a control loop.  It requires no configuration in normal
   Internet deployments.  CoDel comprises some major technical
   innovations and has been made available as open source so that the
   framework can be applied by the community to a range of problems.  It
   has been implemented in Linux (and available in the Linux
   distribution) and deployed in some networks at the consumer edge.  In
   addition, the framework has been successfully applied in other ways.

   Note: Code Components extracted from this document must include the
   license as included with the code in Section 5.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF).  Note that other groups may also distribute

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   working documents as Internet-Drafts.  The list of current Internet-
   Drafts is at

   Internet-Drafts are draft documents valid for a maximum of six months
   and may be updated, replaced, or obsoleted by other documents at any
   time.  It is inappropriate to use Internet-Drafts as reference
   material or to cite them other than as "work in progress."

   This Internet-Draft will expire on April 3, 2015.

Copyright Notice

   Copyright (c) 2014 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents
   ( in effect on the date of
   publication of this document.  Please review these documents
   carefully, as they describe your rights and restrictions with respect
   to this document.  Code Components extracted from this document must
   include Simplified BSD License text as described in Section 4.e of
   the Trust Legal Provisions and are provided without warranty as
   described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   3
   2.  Conventions used in this document . . . . . . . . . . . . . .   4
   3.  Building Blocks of Queue Management . . . . . . . . . . . . .   5
     3.1.  Estimator . . . . . . . . . . . . . . . . . . . . . . . .   6
     3.2.  Setpoint  . . . . . . . . . . . . . . . . . . . . . . . .   8
     3.3.  Control Loop  . . . . . . . . . . . . . . . . . . . . . .   9
   4.  Putting it together: queue management for the network edge  .  12
     4.1.  Overview of CoDel AQM . . . . . . . . . . . . . . . . . .  12
     4.2.  Non-starvation  . . . . . . . . . . . . . . . . . . . . .  13
     4.3.  Using the interval  . . . . . . . . . . . . . . . . . . .  13
     4.4.  The target Setpoint . . . . . . . . . . . . . . . . . . .  14
     4.5.  Use with multiple queues  . . . . . . . . . . . . . . . .  15
     4.6.  Use of stochastic bins or sub-queues to improve
           performance . . . . . . . . . . . . . . . . . . . . . . .  15
     4.7.  Setting up CoDel AQM  . . . . . . . . . . . . . . . . . .  16
   5.  Annotated Pseudo-code for CoDel AQM . . . . . . . . . . . . .  17
     5.1.  Data Types  . . . . . . . . . . . . . . . . . . . . . . .  18
     5.2.  Per-queue state (codel_queue_t instance variables)  . . .  19
     5.3.  Constants . . . . . . . . . . . . . . . . . . . . . . . .  19
     5.4.  Enque routine . . . . . . . . . . . . . . . . . . . . . .  19
     5.5.  Deque routine . . . . . . . . . . . . . . . . . . . . . .  19

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     5.6.  Helper routines . . . . . . . . . . . . . . . . . . . . .  21
     5.7.  Implementation considerations . . . . . . . . . . . . . .  22
   6.  Adapting and applying CoDel's building blocks . . . . . . . .  23
     6.1.  Validations and available code  . . . . . . . . . . . . .  23
     6.2.  CoDel in the datacenter . . . . . . . . . . . . . . . . .  24
   7.  Security Considerations . . . . . . . . . . . . . . . . . . .  25
   8.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  25
   9.  Conclusions . . . . . . . . . . . . . . . . . . . . . . . . .  25
   10. Acknowledgments . . . . . . . . . . . . . . . . . . . . . . .  25
   11. References  . . . . . . . . . . . . . . . . . . . . . . . . .  25
     11.1.  Normative References . . . . . . . . . . . . . . . . . .  25
     11.2.  Informative References . . . . . . . . . . . . . . . . .  25
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  27

1.  Introduction

   The need for queue management has been evident for decades.  Recently
   the need has become more critical due to the increased consumer use
   of the Internet mixing large video transactions with time-critical
   VoIP and gaming.  Gettys [TSV2011, BB2011] has been instrumental in
   publicizing the problem and the measurement work [CHARB2007,
   NATAL2010] and coining the term bufferbloat.  Large content
   distributors such as Google have observed that bufferbloat is
   ubiquitous and adversely affects performance for many users.  The
   solution is an effective AQM that remediates bufferbloat at a
   bottleneck while "doing no harm" at hops where buffers are not

   The development and deployment of effective active queue management
   has been hampered by persistent misconceptions about the cause and
   meaning of queues.  Network buffers exist to absorb the packet bursts
   that occur naturally in statistically multiplexed networks.  Short-
   term mismatches in traffic arrival and departure rates that arise
   from upstream resource contention, transport conversation startup
   transients and/or changes in the number of conversations sharing a
   link create queues.  Unfortunately, other network behavior can cause
   queues to fill and their effects aren't nearly as benign.  Discussion
   of these issues and why the solution isn't just smaller buffers can
   be found in [RFC2309],[VANQ2006],[REDL1998] and [CODEL2012].  It is
   critical to understand the difference between the necessary, useful
   "good" queue and the counterproductive "bad" queue.

   Many approaches to active queue management (AQM) have been developed
   over the past two decades but none has been widely deployed due to
   performance problems.  When designed with the wrong conceptual model
   for queues, AQMs have limited operational range, require a lot of
   configuration tweaking, and frequently impair rather than improve
   performance.  Today, the demands on an effective AQM are even

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   greater: many network devices must work across a range of bandwidths,
   either due to link variations or due to the mobility of the device.
   The CoDel approach is designed to meet the following goals:

   o  is parameterless for normal operation - has no knobs for
      operators, users, or implementers to adjust

   o  treats "good queue" and "bad queue" differently, that is, keeps
      delay low while permitting necessary bursts of traffic

   o  controls delay while insensitive (or nearly so) to round trip
      delays, link rates and traffic loads; this goal is to "do no harm"
      to network traffic while controlling delay

   o  adapts to dynamically changing link rates with no negative impact
      on utilization

   o  is simple and efficient (can easily span the spectrum from low-
      end, linux-based access points and home routers up to high-end
      commercial router silicon)

   Since April, 2012, when CoDel was published, a number of talented and
   enthusiastic implementers have been using and adapting it with
   promising results.  Much of this work is collected at: . CoDel has five major
   innovations that distinguish it from prior AQMs: use of local queue
   minimum to track congestion ("bad queue"), use of an efficient single
   state variable representation of that tracked statistic, use of
   packet sojourn time as the observed datum, rather than packets,
   bytes, or rates, use of mathematically determined setpoint derived
   from maximizing the network power metric, and a modern state space

   CoDel configures itself based on a round-trip time metric which can
   be set to 100ms for the normal, terrestrial Internet.  With no
   changes to parameters, we have found CoDel to work across a wide
   range of conditions, with varying links and the full range of
   terrestrial round trip times.  CoDel has been implemented in Linux
   very efficiently and should lend itself to silicon implementation.
   CoDel is well-adapted for use in multiple queued devices and has been
   used by Eric Dumazet with multiple queues in sophisticated queue
   management approach, fq_codel (covered in another draft).

2.  Conventions used in this document

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   document are to be interpreted as described in [RFC2119].

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   In this document, these words will appear with that interpretation
   only when in ALL CAPS.  Lower case uses of these words are not to be
   interpreted as carrying [RFC2119] significance.

   In this document, the characters ">>" preceding an indented line(s)
   indicates a compliance requirement statement using the key words
   listed above.  This convention aids reviewers in quickly identifying
   or finding the explicit compliance requirements of this RFC.

3.  Building Blocks of Queue Management

   Two decades of work on queue management failed to yield an approach
   that could be widely deployed in the Internet.  With careful tuning
   for particular usages, queue management techniques have been able to
   "kind of" work, that is decrease queuing delays, but utilization and
   fairness suffer unduly.  At the heart of queue management is the
   notion of "good queue" and "bad queue" and the search for ways to get
   rid of the bad queue (which only adds delay) while preserving the
   good queue (which provides for good utilization).  This section
   explains queuing, both good and bad, and covers the innovative CoDel
   building blocks that can be used to manage packet buffers to keep
   their queues in the "good" range.

   Packet queues form in buffers facing bottleneck links, i.e., where
   the line rate goes from high to low or many links converge.  The
   well-known bandwidth-delay product (sometimes called "pipe size") is
   the bottleneck's bandwidth multiplied by the sender-receiver-sender
   round-trip delay and is the amount of data that has to be in transit
   between two hosts in order to run at 100% utilization.  To explore
   how queues can form, consider a long-lived TCP connection with a 25
   packet window sending through a connection with a bandwidth-delay
   product of 20 packets.  After an initial burst of packets the
   connection will settle into a five packet (+/-1) standing queue, the
   size determined by the window mismatch to the pipe size and unrelated
   to the connection's sending rate.  The connection has 25 packets in
   flight at all times, but only 20 packets arrive at the destination
   over a round trip time.  If the TCP connection has a 30 packet
   window, the queue will be ten packets with no change in sending rate.
   Similarly, if the window is 20 packets, there will be no queue but
   the sending rate is the same.  Nothing can be inferred about the
   sender rate from the queue and the existence of any queue at all
   other than transient bursts can only create delay in the network.
   The sender needs to reduce the number of packets in flight rather
   than sending rate.

   In the above example, the five packet standing queue can be seen to
   contribute nothing but delay to the connection thus is clearly "bad
   queue".  If, in our example, there is a single bottleneck link and it

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   is much slower than the link that feeds it (say, a high-speed
   ethernet link into a limited DSL uplink) a 20 packet buffer at the
   bottleneck might be necessary to temporarily hold the 20 packets in
   flight to keep the utilization high.  The burst of packets should
   drain completely (to 0 or 1 packets) within a round trip time and
   this transient queue is "good queue" because it allows the connection
   to keep the 20 packets in flight and for the bottleneck link to be
   fully utilized.  In terms of the delay experienced We can observe
   that "good queue" goes away in about a round trip time, while "bad
   queue" hangs around causing delays.

   Effective queue management detects "bad queue" while ignoring "good
   queue" and takes action to get rid of the bad queue when it is
   detected.  The goal is a queue controller that accomplishes this
   objective.  To control queue, we need three basic components

   o  Estimator - figure out what we've got

   o  Setpoint - know what what we want

   o  Control loop - if what we've got isn't what we want, we need a way
      to move it there

3.1.  Estimator

   The Estimator both observes the queue and detects when good queue
   turns to bad queue and vice versa.  CoDel has two innovations in its
   Estimator: what is observed as an indicator of queue and how the
   observations are used to detect good/bad queue.

   In the past, queue length has been widely used as an observed
   indicator of congestion and is frequently conflated with sending
   rate.  Use of queue length as a metric is sensitive to how and when
   the length is observed.  A high speed arrival link to a buffer
   serviced at a much lower rate can rapidly build up a queue that might
   disperse completely or down to a single packet before a round trip
   time has elapsed.  If the queue length is monitored at packet arrival
   (as in original RED) or departure time, every packet will see a queue
   with one possible exception.  If the queue length itself is time
   sampled (as recommended in [REDL1998], a truer picture of the queue's
   occupancy can be gained but a separate process is required.

   The use of queue length is further complicated in networks that are
   subject to both short and long term changes in available link rate
   (as in wifi).  Link rate drops can result in a spike in queue length
   that should be ignored unless it persists.  The length metric is
   problematic when what we really want to control is the amount of
   excess delay packets experience due to a persistent or standing

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   queue.  The sojourn time that a packet spends in the buffer is
   exactly what we want to track.  Tracking the packet sojourn times in
   the buffer observes the actual delay experienced by each packet.
   Sojourn time is independent of link rate, gives superior performance
   to use of buffer size, and is directly related to the user-visible
   performance.  It works regardless of line rate changes or whether the
   link is shared by multiple queues (which the individual queues may
   experience as changing rates).

   Consider a link shared by two queues, one priority queue and one of
   lower priority.  Packets that arrive to the high priority queue are
   sent as soon as the link is available while packets of the other
   queue have to wait till the the priority queue is empty (i.e., a
   strict priority scheduler).  The number of packets in the priority
   queue might be large but the queue is emptied quickly and the amount
   of time each packet spends enqueued (the sojourn time) is not large.
   The other queue might have a smaller number of packets, but packet
   sojourn times will include the wait for the high priority packets to
   be sent.  This makes the sojourn times a good sample of the
   congestion that each separate queue is experiencing and shows how
   this metric is independent of the number of queues used or the
   service discipline and instead reflective of the congestion seen by
   the individual queue.

   With sojourn time as the observation, how can it be used to separate
   good queue from bad queue?  In the past, averages, in particular of
   queue length, have been used to determine bad queue.  Consider the
   burst that disperses every round trip time.  The average queue will
   be one-half the burst size, though this might vary depending on when
   the average is computed and the timing of arrivals.  The average then
   would indicate a persistent queue where there is none.  If instead we
   track the minimum observation, if there is one packet that has a zero
   sojourn time then there is no persistent queue.  The value of the
   minimum in detecting persistent queue is apparent when looking at
   graphs of queue delay.

   The standing queue can be detected by tracking the (local) minimum
   queue delay packets experience.  To ensure that this minimum value
   does not become stale, it has to have been experienced recently, i.e.
   during an appropriate past time interval.  This "interval" is the
   maximum amount of time a minimum is considered to be in effect.  It
   is clear that this interval should be at least a round trip time to
   avoid falsely detecting a persistent queue and not a lot more than a
   round trip time to avoid delay in detecting the persistent queue.
   This suggests that the appropriate interval value is the maximum
   round-trip time of all the connections sharing the buffer.  To avoid
   outlier values, the 95-99th percentile value is preferred rather than
   a strict maximum.

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   A key realization makes the local minimum an efficiently computed
   statistic.  Note that it is sufficient to keep a single state
   variable of how long the minimum has been above or below a target
   value rather than retaining all the local values to compute the
   minimum, leading to both storage and computational savings.

   These two innovations, use of sojourn time as observed values and the
   local minimum as the statistic to monitor queue congestion are key to
   CoDel's Estimator building block.  The local minimum sojourn time
   provides an accurate and robust measure of standing queue and has an
   efficient implementation.  In addition, use of the minimum sojourn
   time has important advantages in implementation.  The minimum packet
   sojourn can only be decreased when a packet is dequeued which means
   that all the work of CoDel can take place when packets are dequeued
   for transmission and that no locks are needed in the implementation.
   The minimum is the only statistic with this property.

   A more detailed explanation with many pictures can be found at: and .

3.2.  Setpoint

   Now that we have a robust way of detecting standing queue, we need to
   have a Setpoint that tells us when to act.  If the controller is set
   to take action as soon as the estimator has a non-zero value, the
   average drop rate will be maximized which minimizes TCP goodput
   [MACTCP1997].  Also, since this policy results in no backlog over
   time (no persistent queue), it also maximizes the bottleneck link
   bandwidth lost because of normal stochastic variation in packet
   interarrival time and obliterates much of the value of having a
   buffer.  We want a setpoint that maximizes utilization while
   minimizing delay.  Early in the history of packet networking,
   Kleinrock developed the analytic machinery to do this using a
   quantity he called _'power'_ (the ratio of a normalized throughput to
   a normalized delay) [KLEIN81].

   It's straightforward to derive an analytic expression the average
   goodput of a TCP conversation for a given round-trip time _r_ and
   setpoint _f_ (where _f_ is expressed as a fraction of _r_)
   [VJTARG14].  Reno TCP, for example, yields:

   goodput = _r _(3 + 6_f_ - _f_^2) / (4 (1+_f_))

   Since the peak delay is just _f r_, it's clear that _power_ is solely
   a function of _f_ since the _r_'s in the numerator and denominator

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   power = (1 + 2_f_ - 1/3 _f^_2) / (1 + _f_)^2

   As Kleinrock observed, the best operating point, in terms of
   bandwidth / delay tradeoff, is the peak power point since points off
   the peak represent a higher cost (in delay) per unit of bandwidth.
   The power vs. _f_ curve for any AIMD TCP is monotone decreasing.  But
   the curve is very flat for _f_ < 0.1 followed by a increasing
   curvature with a knee around .2 then a steep, almost linear fall off
   [TSV84] [VJTARG14].  Since the previous equation showed that goodput
   is monotone increasing with _f_, the best operating point is near the
   right edge of the flat top since that represents the highest goodput
   achievable for a negligible increase in delay.  However, since the
   _r_ in the model is a conservative upper bound, a target of .1_r_
   runs the risk of pushing shorter RTT connections over the knee and
   giving them higher delay for no significant goodput increase.
   Generally, a more conservative target of .05_r _offers a good
   utilization vs. delay tradeoff while giving enough headroom to work
   well with a large variation in real RTT.

   As the above analysis shows, a very small standing queue gives close
   to 100% utilization.  While this result was for Reno TCP, the
   derivation uses only properties that must hold for any 'TCP friendly'
   transport.  We have verified by both analysis and simulation that
   this result holds for Reno, Cubic, and Westwood[TSV84].  This results
   in a particularly simple form for the setpoint: the ideal range for
   the permitted standing queue is between 5 and 10% of the TCP
   connection RTT.  Thus _target_ is simply 5% of the _interval_ of
   section 3.1.

3.3.  Control Loop

   Section 3.1 describes a simple, reliable way to measure bad
   (persistent) queue.  Section 3.2 shows that TCP congestion control
   dynamics gives rise to a setpoint for this measure that's a provably
   good balance between enhancing throughput and minimizing delay, and
   that this setpoint is a constant fraction of the same 'largest
   average RTT' interval used to distinguish persistent from transient
   queue.  The only remaining building block needed for a basic AQM is a
   'control loop' algorithm to effectively drive the queuing system from
   any 'persistent queue above target' state to a state where the
   persistent queue is below target.

   Control theory provides a wealth of approaches to the design of
   control loops.  Most of classical control theory deals with the
   control of linear, time-invariant, single-input-single-output (SISO)
   systems.  Control loops for these systems generally come from a (well
   understood) class known as Proportional-Integral-Derivative (PID)
   controllers.  Unfortunately, a queue is not a linear system and an

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   AQM operates at the point of maximum non-linearity (where the output
   link bandwidth saturates so increased demand creates delay rather
   than higher utilization).  Output queues are also not time-invariant
   since traffic is generally a mix of connections which start and stop
   at arbitrary times and which can have radically different behaviors
   ranging from "open loop" UDP audio/video to "closed-loop" congestion-
   avoiding TCP.  Finally, the constantly changing mix of connections
   (which can't be converted to a single 'lumped parameter' model
   because of their transfer function differences) makes the system
   multi-input-multi-output (MIMO), not SISO.

   Since queuing systems match none of the prerequisites for a classical
   controller, a modern state-space controller is a better approach with
   states 'no persistent queue' and 'has persistent queue'.  Since
   Internet traffic mixtures change rapidly and unpredictably, a noise
   and error tolerant adaptation algorithm like Stochastic Gradient is a
   good choice.  Since there's essentially no information in the amount
   of persistent queue [TSV84], the adaptation should be driven by how
   long it has persisted.

   Consider the two extremes of traffic behavior, a single open-loop UDP
   video stream and a single, long-lived TCP bulk data transfer.  If the
   average bandwidth of the UDP video stream is greater that the
   bottleneck link rate, the link's queue will grow and the controller
   will eventually enter 'has persistent queue' state and start dropping
   packets.  Since the video stream is open loop, its arrival rate is
   unaffected by drops so the queue will persist until the average drop
   rate is greater than the output bandwidth deficit (= average arrival
   rate - average departure rate) so the job of the adaptation algorithm
   is to discover this rate.  For this example, the adaptation could
   consist of simply estimating the arrival and departure rates then
   dropping at a rate slightly greater than their difference.  But this
   class of algorithm won't work at all for the bulk data TCP stream.
   TCP runs in closed-loop flow balance [TSV84] so its arrival rate is
   almost always exactly equal to the departure rate - the queue isn't
   the result of a rate imbalance but rather a mismatch between the TCP
   sender's window and the src-dst-src round-trip path capacity (i.e.,
   the connection's bandwidth*delay product).  The sender's TCP
   congestion avoidance algorithm will slowly increase the send window
   (one packet per round-trip-time) [RFC2581] which will eventually
   cause the bottleneck to enter 'has persistent queue' state.  But,
   since the average input rate is the same as the average output rate,
   the rate deficit estimation that gave the correct drop rate for the
   video stream would compute a drop rate of zero for the TCP stream.
   However, if the output link drops one packet as it enters 'has
   persistent queue' state, when the sender discovers this (via TCP's
   normal packet loss repair mechanisms) it will reduce its window by a

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   factor of two [RFC2581] so, one round-trip-time after the drop, the
   persistent queue will go away.

   If there were N TCP conversations sharing the bottleneck, the
   controller would have to drop O(N) packets, one from each
   conversation, to make all the conversations reduce their window to
   get rid of the persistent queue.  If the traffic mix consists of
   short (<= bandwidth*delay product) conversations, the aggregate
   behavior becomes more like the open-loop video example since each
   conversation is likely to have already sent all its packets by the
   time it learns about a drop so each drop has negligible effect on
   subsequent traffic.

   The controller doesn't know what type, how many or how long are the
   conversations creating its queue so it has to learn that.  Since
   single drops can have a large effect if the degree of multiplexing
   (the number of active conversations) is small, dropping at too high a
   rate is likely to have a catastrophic effect on throughput.  Dropping
   at a low rate (< 1 packet per round-trip-time) then increasing the
   drop rate slowly until the persistent queue goes below target is
   unlikely to overdrop yet is guaranteed to eventually dissipate the
   persistent queue.  This stochastic gradient learning procedure is the
   core of CoDel's control loop (the gradient exists because a drop
   always reduces the (instantaneous) queue so an increasing drop rate
   always moves the system "down" toward no persistent queue, regardless
   of traffic mix).

   The next drop time is decreased in inverse proportion to the square
   root of the number of drops since the dropping state was entered,
   using the well-known nonlinear relationship of drop rate to
   throughput to get a linear change in throughput.  [REDL1998,

   Since the best rate to start dropping is at slightly more than one
   packet per RTT, the controller's initial drop rate can be directly
   derived from the Estimator's interval, defined in section 3.1.  Where
   the interval is likely to be very close to the usual round trip time,
   the initial drop spacing SHOULD be set to the Estimator's interval
   plus twice the target (i.e., initial drop spacing = 1.1 * interval)
   to ensure that acceptable congestion delays are covered.

   Use of the minimum statistic lets the Controller be placed in the
   dequeue routine with the Estimator.  This means that the control
   signal (the drop) can be sent at the first sign of bad queue (as
   indicated by the sojourn time) and that the Controller can stop
   acting as soon as the sojourn time falls below the Setpoint.
   Dropping at dequeue has both implementation and control advantages.

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4.  Putting it together: queue management for the network edge

   The CoDel building blocks are able to adapt to different or time-
   varying link rates, to be easily used with multiple queues, to have
   excellent utilization with low delay and to have a simple and
   efficient implementation.  The only setting CoDel requires is its
   interval value, and as 100ms satisfies that definition for normal
   internet usage, CoDel can be parameter-free for consumer use.  CoDel
   was released to the open source community where it has been widely
   promulgated and adapted to many problems.  We can see how well these
   building blocks work in a simple CoDel queue management
   implementation.  This AQM was designed as a bufferbloat solution and
   is focused on the consumer network edge.

4.1.  Overview of CoDel AQM

   To ensure that link utilization is not adversely affected, CoDel's
   Estimator sets its target to the Setpoint that optimizes power and
   CoDel's Controller does not drop packets when the drop would leave
   the queue empty or with fewer than a maximum transmission unit (MTU)
   worth of bytes in the buffer.  Section 3.2 showed that the ideal
   Setpoint is 5-10% of the connection RTT.  In the open Internet, in
   particular the consumer edge, we can use the "usual maximum"
   terrestrial RTT of 100 ms to calculate a minimum target of 5ms.
   Under the same assumptions, we compute the interval for tracking the
   minimum to be the nominal RTT of 100ms.  In practice, uncongested
   links will see sojourn times under the target more often than once
   per RTT, so the Estimator is not overly sensitive to the value of the

   When the Estimator finds a persistent delay above target, the
   Controller enters the drop state where a packet is dropped and the
   next drop time is set.  As discussed in section 3.3, the initial next
   drop spacing is intended to be long enough to give the endpoints time
   to react to the single drop so SHOULD be set to a value of 1.0 to 1.1
   times the interval.  If the Estimator's output falls below the
   target, the Controller cancels the next drop and exits the drop
   state.  (The Controller is more sensitive than the Estimator to an
   overly short interval, since an unnecessary drop could occur and
   lower utilization.)  If next drop time is reached while the
   Controller is still in drop state, the packet being dequeued is
   dropped and the next drop time is recalculated.  Additional logic
   prevents re-entering the dropping state too soon after exiting it and
   resumes the dropping state at a recent control level, if one exists.

   Note that CoDel AQM only enters its dropping state when the local
   minimum sojourn delay has exceeded an acceptable standing queue
   target for a time interval long enough for normal bursts to dissipate

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   ensuring that a burst of packets that fits in the pipe will not be

   CoDel's efficient implementation and lack of configuration are unique
   features and make it suitable to manage modern packet buffers.  For
   more background and results on CoDel, see [CODEL2012] and .

4.2.  Non-starvation

   CoDel's goals are to control delay with little or no impact on link
   utilization and to be deployed on a wide range of link bandwidth,
   including varying rate links, without reconfiguration.  To keep from
   making drops when it would starve the output link, CoDel makes
   another check before dropping to see if at least an MTU worth of
   bytes remains in the buffer.  If not, the packet SHOULD NOT be
   dropped and, currently, CoDel exits the drop state.  The MTU size can
   be set adaptively to the largest packet seen so far or can be read
   from the driver.

4.3.  Using the interval

   The interval is chosen to give endpoints time to react to a drop
   without being so long that response times suffer.  CoDel's Estimator,
   Setpoint, and Control Loop all use the interval.  Understanding their
   derivation shows that CoDel is the most sensitive to the value of
   interval for single long-lived TCPs with a decreased sensitivity for
   traffic mixes.  This is fortunate as RTTs vary across connections and
   are not known apriori and it's difficult to obtain a definitive
   histogram of RTTs seen on the normal consumer edge link.  The best
   policy is to use an interval slightly larger than the RTT seen by
   most of the connections using a link, a value that can be determined
   as the largest RTT seen if the value is not an outlier (as in section
   3.1, use of a 95-99th percentile value should work).  In practice,
   this value is not known or measured (though see Section 6.2 for an
   application where interval is measured.  Work-in-progress at Pollere
   may lead to a method of doing this in an Internet buffer).  A setting
   of 100ms works well across a range of RTTs from 10ms to 1 second
   (excellent performance is achieved in the range from 10 ms to 300ms).
   For devices intended for the normal terrestrial Internet interval
   SHOULD have the value of 100ms.  This will only cause overdropping
   where a long-lived TCP has an RTT longer than 100ms and there is
   little or no mixing with other connections through the link.

   Some confusion concerns the roles of the target Setpoint and the
   minimum-tracking interval.  In particular, some experimenters believe
   the value of target needs to be increased when the lower layers have
   a bursty nature where packets are transmitted for short periods

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   interspersed with idle periods where the link is waiting for
   permission to send.  CoDel's Estimator will "see" the effective
   transmission rate over an interval and increasing target will just
   lead to longer queue delays.  On the other hand, where a significant
   additional delay is added to the intrinsic round trip time of most or
   all packets due to the waiting time for a transmission, it is
   necessary to increase interval by that extra delay.  That is, target
   SHOULD NOT be adjusted but interval MAY need to be adjusted.  For
   more on this (and pictures) see

4.4.  The target Setpoint

   The target is the maximum acceptable standing queue delay above which
   CoDel is dropping or preparing to drop and below which CoDel will not
   drop.  The calculations of section 3.2 showed that the best setpoint
   is 5-10% of the RTT, with the low end of 5% preferred.  We used
   simulation to explore the impact when TCPs are mixed with other
   traffic and with connections of different RTTs.  Accordingly, we
   experimented extensively with values in the 5-10% of RTT range and,
   overall, used target values between 1 and 20 milliseconds for RTTs
   from 30 to 500ms and link bandwidths of 64Kbps to 100Mbps to
   experimentally explore the Setpoint that gives consistently high
   utilization while controlling delay across a range of bandwidths,
   RTTs, and traffic loads.  Our results were notably consistent with
   the mathematics of section 3.2.  Below a target of 5ms, utilization
   suffers for some conditions and traffic loads, above 5ms we saw very
   little or no improvement in utilization.  Thus target SHOULD be set
   to 5ms for normal Internet traffic.

   If a CoDel link has only or primarily long-lived TCP flows sharing a
   link to congestion but not overload, the median delay through the
   link will tend to the target.  For bursty traffic loads and for
   overloaded conditions (where it is difficult or impossible for all
   the arriving flows to be accommodated) the median queues will be
   longer than target.

   The non-starvation drop inhibit feature dominates where the link rate
   becomes very small.  By inhibiting drops when there is less than an
   (outbound link) MTU worth of bytes in the buffer, CoDel adapts to
   very low bandwidth links.  This is shown in [CODEL2012] and
   interested parties should see the discussion of results there.
   Unpublished studies were carried out down to 64Kbps.  The drop
   inhibit condition can be expanded to include a test to retain
   sufficient bytes or packets to fill an allocation in a request-and-
   grant MAC.

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   Sojourn times must remain above target for an entire interval in
   order to enter the drop state.  Any packet with a sojourn time less
   than target will reset the time that the queue was last below the
   target.  Since Internet traffic has very dynamic characteristics, the
   actual sojourn delays experienced by packets varies greatly and is
   often less than the target unless the overload is excessive.  When a
   link is not overloaded, it is not a bottleneck and packet sojourn
   times will be small or nonexistent.  In the usual case, there are
   only one or two places along a path where packets will encounter a
   bottleneck (usually at the edge), so the amount of queuing delay
   experienced by a packet should be less than 10 ms even under
   extremely congested conditions.  Contrast this to the queuing delays
   that grow to orders of seconds that have led to the "bufferbloat"
   term [NETAL2010, CHARRB2007].

4.5.  Use with multiple queues

   Unlike other AQMs, CoDel is easily adapted to multiple queue systems.
   With other approaches there is always a question of how to account
   for the fact that each queue receives less than the full link rate
   over time and usually sees a varying rate over time.  This is exactly
   what CoDel excels at: using a packet's sojourn time in the buffer
   completely bypasses this problem.  A separate CoDel algorithm runs on
   each queue, but each CoDel uses the packet sojourn time the same way
   a single queue CoDel does.  Just as a single queue CoDel adapts to
   changing link bandwidths[CODEL2012], so do the multiple queue CoDels.
   When testing for queue occupancy before dropping, the total occupancy
   of all bins should be used.  This property of CoDel has been
   exploited in fq_codel, briefly discussed in the next section and the
   subject of another Internet Draft.

4.6.  Use of stochastic bins or sub-queues to improve performance

   Shortly after the release of the CoDel pseudocode, Eric Dumazet
   created fq_codel, applying CoDel to each bin, or queue, used with
   stochastic fair queuing.  (To understand further, see [SFQ1990] or
   the linux sfq at .) Fq_codel hashes
   on the packet header fields to determine a specific bin, or sub-
   queue, for each five-tuple flow, and runs CoDel on each bin or sub-
   queue thus creating a well-mixed output flow and obviating issues of
   reverse path flows (including "ack compression").  Dumazet's code is
   part of the CeroWrt project code at the's web site
   and an Internet Draft has been submitted describing fq_codel, draft-

   We've experimented with a similar approach by creating an ns-2
   simulator code module, sfqcodel.  This has provided excellent results
   thus far: median queues remain small across a range of traffic

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   patterns that includes bidirectional file transfers (that is, the
   same traffic sent in both directions on a link), constant bit-rate
   VoIP-like flows, and emulated web traffic and utilizations are
   consistently better than single queue CoDel, generally very close to
   100%. Our version differs from Dumazet's by preferring a packet-based
   round robin of the bins rather than byte-based DRR and there may be
   other minor differences in implementation.  Our code, intended for
   simulation experiments, is available at
   and being integrated into the ns-2 distribution.  Andrew McGregor has
   an ns-3 version of fq_codel.

   Stochastic flow queuing provides better traffic mixing on the link
   and tends to isolate a larger flow or flows.  For real priority
   treatment, use of DiffServ isolation is encouraged.  We've
   experimented in simulation with creating a queue to isolate all the
   UDP traffic (which is all simulated VoIP thus low bandwidth) but this
   approach has to be applied with caution in the real world.  Some
   experimenters are trying rounding with a small quantum (on the order
   of a voice packet size) but this also needs thorough study.

   A number of open issues should be studied.  In particular, if the
   number of different queues or bins is too large, the scheduling will
   be the dominant factor, not the AQM; it is NOT the case that more
   bins are always better.  In our simulations, we have found good
   behavior across mixed traffic types with smaller numbers of queues,
   8-16 for a 5Mbps link.  This configuration appears to give the best
   behavior for voice, web browsing and file transfers where increased
   numbers of bins seems to favor file transfers at the expense of the
   other traffic.  Our work has been very preliminary and we encourage
   others to take this up and to explore analytic modeling.  It would be
   instructive to see the effects of different numbers of bins on a
   range of traffic models, something like an updated version of

   Implementers SHOULD use the fq_codel multiple queue approach if
   possible as it deals with many problems beyond the reach of an AQM on
   a single queue.

4.7.  Setting up CoDel AQM

   CoDel's is set for use in devices in the open Internet.  An interval
   of 100ms is used, target is set to 5% of interval, and the initial
   drop spacing is also set to interval.  These settings have been
   chosen so that a device, such as a small WiFi router, can be sold
   without the need for any values to be made adjustable, yielding a
   parameterless implementation.  In addition, CoDel is useful in
   environments with significantly different characteristics from the
   normal Internet, for example, in switches used as a cluster

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   interconnect within a data center.  Since cluster traffic is entirely
   internal to the data center, round trip latencies are low (typically
   <100us) but bandwidths are high (1-40Gbps) so it's relatively easy
   for the aggregation phase of a distributed computation (e.g., the
   Reduce part of a Map/Reduce) to persistently fill then overflow the
   modest per-port buffering available in most high speed switches.  A
   CoDel configured for this environment (target and interval in the
   microsecond rather than millisecond range) can minimize drops (or ECN
   marks) while keeping throughput high and latency low.

   Devices destined for these environments MAY use a different interval,
   where suitable.  If appropriate analysis indicates, the target MAY be
   set to some other value in the 5-10% of interval and the initial drop
   spacing MAY be set to a value of 1.0 to 1.2 times the interval.  But
   these settings will cause problems such as over dropping and low
   throughput if used on the open Internet so devices that allow CoDel
   to be configured MUST default to Internet appropriate values given in
   this document.

5.  Annotated Pseudo-code for CoDel AQM

   What follows is the CoDel algorithm in C++-like pseudo-code.  Since
   CoDel adds relatively little new code to a basic tail-drop fifo-
   queue, we've tried to highlight just these additions by presenting
   CoDel as a sub-class of a basic fifo-queue base class.  There have
   been a number of minor variants in the code and our reference pseudo-
   code has not yet been completely updated.  The reference code is
   included to aid implementers who wish to apply CoDel to queue
   management as described here or to adapt its principles to other

   Implementors are strongly encouraged to also look at Eric Dumazet's
   Linux kernel version of CoDel - a well-written, well tested, real-
   world, C-based implementation.  As of this writing, it is at:

   This code is open-source with a dual BSD/GPL license:

   Codel - The Controlled-Delay Active Queue Management algorithm

   Copyright (C) 2011-2014 Kathleen Nichols <>

   Redistribution and use in source and binary forms, with or without
   modification, are

   permitted provided that the following conditions are met:

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   o  Redistributions of source code must retain the above copyright
      notice, this list of conditions, and the following disclaimer,
      without modification.

   o  Redistributions in binary form must reproduce the above copyright
      notice, this list of conditions and the following disclaimer in
      the documentation and/or other materials provided with the

   o  The names of the authors may not be used to endorse or promote
      products derived from this software without specific prior written

   Alternatively, provided that this notice is retained in full, this
   software may be distributed under the terms of the GNU General Public
   License ("GPL") version 2, in which case the provisions of the GPL
   apply INSTEAD OF those given above.


5.1.  Data Types

   "time_t " is an integer time value in units convenient for the
   system.  Resolution to at least a millisecond is required and better
   resolution is useful up to the minimum possible packet time on the
   output link; 64- or 32-bit widths are acceptable but with 32 bits the
   resolution should be no finer than 2^{-16} to leave enough dynamic
   range to represent a wide range of queue waiting times.  Narrower
   widths also have implementation issues due to overflow (wrapping) and
   underflow (limit cycles because of truncation to zero) that are not
   addressed in this pseudocode.  The code presented here uses 0 as a
   flag value to indicate "no time set."

   "packet_t*" is a pointer to a packet descriptor.  We assume it has a
   tstamp field capable of holding a time_t and that field is available
   for use by CoDel (it will be set by the enque routine and used by the
   deque routine).

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   "queue_t" is a base class for queue objects (the parent class for
   codel_queue_t objects).  We assume it has enque() and deque() methods
   that can be implemented in child classes.  We assume it has a bytes()
   method that returns the current queue size in bytes.  This can be an
   approximate value.  The method is invoked in the deque() method but
   shouldn't require a lock with the enque() method.

   "flag_t " is a Boolean.

5.2.  Per-queue state (codel_queue_t instance variables)

   time_t first_above_time; // Time to declare sojourn time above target
   time_t drop_next;        // Time to drop next packet
   uint32_t count;      // Packets dropped since entering drop state
   flag_t dropping;         // Equal to 1 if in drop state

5.3.  Constants

   time_t target = MS2TIME(5); // 5ms target queue delay
   time_t interval = MS2TIME(100); // 100ms sliding-minimum window
   u_int maxpacket = 512; // Maximum packet size in bytes
                    // (should use interface MTU)

5.4.  Enque routine

   All the work of CoDel is done in the deque routine.  The only CoDel
   addition to enque is putting the current time in the packet's tstamp
   field so that the deque routine can compute the packet's sojourn

   void codel_queue_t::enque(packet_t* pkt)
       pkt->timestamp() = clock();

5.5.  Deque routine

   This is the heart of CoDel.  There are two branches: In packet-
   dropping state (meaning that the queue-sojourn time has gone above
   target and hasn't come down yet), then we need to check if it's time
   to leave or if it's time for the next drop(s); if we're not in
   dropping state, then we need to decide if it's time to enter and do
   the initial drop.

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   Packet* CoDelQueue::deque()
       double now = clock();;
       dodequeResult r = dodeque(now);

       if (dropping_) {
           if (! r.ok_to_drop) {
               // sojourn time below target - leave dropping state
               dropping_ = 0;
           // Time for the next drop. Drop current packet and dequeue
           // next.  If the dequeue doesn't take us out of dropping
           // state, schedule the next drop. A large backlog might
           // result in drop rates so high that the next drop should
           // happen now, hence the 'while' loop.
           while (now >= drop_next_ && dropping_) {
               r = dodeque(now);
               if (! r.ok_to_drop) {
                   // leave dropping state
                   dropping_ = 0;
               } else {
                   // schedule the next drop.
                   drop_next_ = control_law(drop_next_);
       // If we get here we're not in dropping state. The 'ok_to_drop'
       // return from dodeque means that the sojourn time has been
       // above 'target' for 'interval' so enter dropping state.
       } else if (r.ok_to_drop) {
           r = dodeque(now);
           dropping_ = 1;

           // If min went above target close to when it last went
           // below, assume that the drop rate that controlled the
           // queue on the last cycle is a good starting point to
           // control it now. ('drop_next' will be at most 'interval'
           // later than the time of the last drop so 'now - drop_next'
           // is a good approximation of the time from the last drop
           // until now.)
           count_ = (count_ > 2 && now - drop_next_ < 8*interval_)?
                       count_ - 2 : 1;
           drop_next_ = control_law(now);
       return (r.p);

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5.6.  Helper routines

   Since the degree of multiplexing and nature of the traffic sources is
   unknown, CoDel acts as a closed-loop servo system that gradually
   increases the frequency of dropping until the queue is controlled
   (sojourn time goes below target).  This is the control law that
   governs the servo.  It has this form because of the sqrt(p)
   dependence of TCP throughput on drop probability.  Note that for
   embedded systems or kernel implementation, the inverse sqrt can be
   computed efficiently using only integer multiplication.  See Eric
   Dumazet's excellent Linux CoDel implementation for example code (in
   file net/sched/sch_codel.c of the kernel source for 3.5 or newer

   time_t codel_queue_t::control_law(time_t t)
       return t + interval / sqrt(count);

   Next is a helper routine the does the actual packet dequeue and
   tracks whether the sojourn time is above or below target and, if
   above, if it has remained above continuously for at least interval.
   It returns two values, a Boolean indicating if it is OK to drop
   (sojourn time above target for at least interval) and the packet

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   typedef struct {
       packet_t* p;
       flag_t ok_to_drop;
   } dodeque_result;

   dodeque_result codel_queue_t::dodeque(time_t now)
       dodequeResult r = { NULL, queue_t::deque() };
       if (r.p == NULL) {
           // queue is empty - we can't be above target
           first_above_time_ = 0;
           return r;

       // To span a large range of bandwidths, CoDel runs two
       // different AQMs in parallel. One is sojourn-time-based
       // and takes effect when the time to send an MTU-sized
       // packet is less than target.  The 1st term of the "if"
       // below does this.  The other is backlog-based and takes
       // effect when the time to send an MTU-sized packet is >=
       // target. The goal here is to keep the output link
       // utilization high by never allowing the queue to get
       // smaller than the amount that arrives in a typical
       // interarrival time (MTU-sized packets arriving spaced
       // by the amount of time it takes to send such a packet on
       // the bottleneck). The 2nd term of the "if" does this.
       time_t sojourn_time = now - r.p->tstamp;
       if (sojourn_time_ < target_ || bytes() <= maxpacket_) {
           // went below - stay below for at least interval
           first_above_time_ = 0;
       } else {
           if (first_above_time_ == 0) {
               // just went above from below. if still above at
               // first_above_time, will say it's ok to drop.
               first_above_time_ = now + interval_;
           } else if (now >= first_above_time_) {
               r.ok_to_drop = 1;
       return r;

5.7.  Implementation considerations

   Since CoDel requires relatively little per-queue state and no direct
   communication or state sharing between the enqueue and dequeue
   routines, it's relatively simple to add it to almost any packet
   processing pipeline, including ASIC- or NPU-based forwarding engines.

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   One issue to think about is dedeque's use of a 'bytes()' function to
   find out about how many bytes are currently in the queue.  This value
   does not need to be exact.  If the enqueue part of the pipeline keeps
   a running count of the total number of bytes it has put into the
   queue and the dequeue routine keeps a running count of the total
   bytes it has removed from the queue, 'bytes()' is just the difference
   between these two counters. 32 bit counters are more than adequate.
   Enqueue has to update its counter once per packet queued but it
   doesn't matter when (before, during or after the packet has been
   added to the queue).  The worst that can happen is a slight,
   transient, underestimate of the queue size which might cause a drop
   to be briefly deferred.

6.  Adapting and applying CoDel's building blocks

   CoDel is being implemented and tested in a range of environments.
   Dave Taht has been instrumental in the integration and distribution
   of bufferbloat solutions, including CoDel, and has set up a website
   and a mailing list for CeroWRT implementers.
   ( This is an active area of work
   and an excellent place to track developments.

6.1.  Validations and available code

   An experiment by Stanford graduate students successfully used the
   linux CoDel to duplicate our published simulation work on CoDel's
   ability to following drastic link rate changes which can be found at:
   bufferbloat-the-codel-way/ .

   Our ns-2 simulations are available at .
   Cable Labs has funded some additions to the simulator sfqcodel code
   which have been made public.  The basic algorithm of CoDel remains
   unchanged, but we continue to experiment with drop interval setting
   when resuming the drop state, inhibiting or canceling drop state when
   bytes in the queue small, and other minor details.  Our approach to
   changes is to only make them if we are convinced they do more good
   than harm, both operationally and in the implementation.  With this
   in mind, some of these issues may continue to evolve as we get more
   deployment and as the building blocks are applied to a wider range of

   CoDel is being made available with the ns-2 distribution.

   Andrew McGregor has an ns-3 implementation of both CoDel and FQ_CoDel
   ( ).

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   CoDel is available in Linux.  Eric Dumazet has put CoDel into the
   Linux distribution.

6.2.  CoDel in the datacenter

   Nandita Dukkipati's team at Google was quick to realize that the
   CoDel building blocks could be applied to bufferbloat problems in
   datacenter servers, not just to Internet routers.  The Linux CoDel
   queueing discipline (Qdisc) was adapted in three ways to tackle this
   bufferbloat problem.

   1.  The default CoDel action was modified to be a direct feedback
       from Qdisc to the TCP layer at dequeue.  The direct feedback
       simply reduces TCP's congestion window just as congestion control
       would do in the event of drop.  The scheme falls back to ECN
       marking or packet drop if the TCP socket lock could not be
       acquired at dequeue.

   2.  Being located in the server makes it possible to monitor the
       actual RTT to use as CoDel's interval rather than making a "best
       guess" of RTT.  The CoDel interval is dynamically adjusted by
       using the maximum TCP round-trip time (RTT) of those connections
       sharing the same Qdisc/bucket.  In particular, there is a history
       entry of the maximum RTT experienced over the last second.  As a
       packet is dequeued, the RTT estimate is accessed from its TCP
       socket.  If the estimate is larger than the current CoDel
       interval, the CoDel interval is immediately refreshed to the new
       value.  If the CoDel interval is not refreshed for over a second,
       it is decreased it to the history entry and the process repeated.
       The use of the dynamic TCP RTT estimate lets interval adapt to
       the actual maximum value currently seen and thus lets the
       controller space its drop intervals appropriately.

   3.  Since the mathematics of computing the set point are invariant, a
       target of 5% of the RTT or CoDel interval was used here.

   Non-data packets were not dropped as these are typically small and
   sometimes critical control packets.  Being located on the server,
   there is no concern with misbehaving users scamming such a policy as
   there would be in an Internet router.

   In several data center workload benchmarks, which are typically
   bursty, CoDel reduced the queueing latencies at the Qdisc, and
   thereby improved the mean and 99 percentile latencies from several
   tens of milliseconds to less than one millisecond.  The minimum
   tracking part of the CoDel framework proved useful in disambiguating
   "good" queue versus "bad" queue, particularly helpful in controlling

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   Qdisc buffers that are inherently bursty because of TCP Segmentation

7.  Security Considerations

   This document describes an active queue management algorithm for
   implementation in networked devices.  There are no specific security
   exposures associated with CoDel.

8.  IANA Considerations

   This document does not require actions by IANA.

9.  Conclusions

   CoDel provides very general, efficient, parameterless building blocks
   for queue management that can be applied to single or multiple queues
   in a variety of data networking scenarios.  It is a critical tool in
   solving bufferbloat.  CoDel's settings MAY be modified for other
   special-purpose networking applications.

   On-going projects are creating a deployable CoDel in Linux routers
   and experimenting with applying CoDel to stochastic queuing with very
   promising results.

10.  Acknowledgments

   The authors wish to thank Jim Gettys for the constructive nagging
   that made us get the work "out there" before we thought it was ready.
   We also want to thank Dave Taht, Eric Dumazet, and the open source
   community for showing the value of getting it "out there" and for
   making it real.  We also wish to thank Nandita Dukkipati for
   contribution to section 6 and for comments which helped to
   substantially improve this draft.

11.  References

11.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119, March 1997.

11.2.  Informative References

   [BB2011]   Gettys, J. and K. Nichols, "Bufferbloat: Dark Buffers in
              the Internet", Communications of the ACM 9(11) pp. 57-65,

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   [BMPFQ]    Suter, B., "Buffer Management Schemes for Supporting TCP
              in Gigabit Routers with Per-flow Queueing", IEEE Journal
              on Selected Areas in Communications Vol. 17 Issue 6, June,
              1999, pp. 1159-1169, .

              Dischinger, M., et. al, "Characterizing Residential
              Broadband Networks", Proceedings of the Internet
              Measurement Conference San Diego, CA, 2007, .

   [CMNTS]    Allman, M., "Comments on Bufferbloat", Computer
              Communication Review Vol. 43 No. 1, January, 2013, pp.
              31-37, .

              Nichols, K. and V. Jacobson, "Controlling Queue Delay",
              Communications of the ACM Vol. 55 No. 11, July, 2012, pp.
              42-50, .

   [KLEIN81]  Kleinrock, L. and R. Gail, "An Invariant Property of
              Computer Network Power", International Conference on
              Communications June, 1981,

              Mathis, M., Semke, J., and J. Mahdavi, "The Macroscopic
              Behavior of the TCP Congestion Avoidance Algorithm", ACM
              SIGCOMM Computer Communications Review Vol. 27 no. 1, Jan.
              2007, .

              Kreibich, C., et. al., "Netalyzr: Illuminating the Edge
              Network", Proceedings of the Internet Measurement
              Conference Melbourne, Australia, 2010, .

              Nichols, K., Jacobson, V., and K. Poduri, "RED in a
              Different Light", Tech report, September, 1999,

   [RFC0896]  Nagle, J., "Congestion control in IP/TCP internetworks",
              RFC 896, January 1984.

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   [RFC2309]  Braden, B., Clark, D., Crowcroft, J., Davie, B., Deering,
              S., Estrin, D., Floyd, S., Jacobson, V., Minshall, G.,
              Partridge, C., Peterson, L., Ramakrishnan, K., Shenker,
              S., Wroclawski, J., and L. Zhang, "Recommendations on
              Queue Management and Congestion Avoidance in the
              Internet", RFC 2309, April 1998.

   [RFC2581]  Allman, M., Paxson, V., and W. Stevens, "TCP Congestion
              Control", RFC 2581, April 1999.

   [SFQ1990]  McKenney, P., "Stochastic Fairness Queuing", Proceedings
              of IEEE INFOCOMM 90 San Francisco, 1990, .

   [TSV2011]  Gettys, J., "Bufferbloat: Dark Buffers in the Internet",
              IETF 80 presentation to Transport Area Open Meeting,
              March, 2011,

   [TSV84]    Jacobson, V., "CoDel talk at TSV meeting IETF 84",

              Jacobson, V., "A Rant on Queues", talk at MIT Lincoln
              Labs, Lexington, MA July, 2006,

Authors' Addresses

   Kathleen Nichols
   Pollere, Inc.
   PO Box 370201
   Montara, CA  94037


   Van Jacobson


   Andrew McGregor


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   Jana Iyengar


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