AQM                                                           K. Nichols
Internet-Draft                                             Pollere, Inc.
Intended status: Experimental                                V. Jacobson
Expires: May 4, 2017                                    A. McGregor, ed.
                                                         J. Iyengar, ed.
                                                        October 31, 2016

                Controlled Delay Active Queue Management


   This document describes a general framework called CoDel (Controlled
   Delay) 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
   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 May 4, 2017.

Nichols, et al.            Expires May 4, 2017                  [Page 1]

Internet-Draft                    CoDel                     October 2016

Copyright Notice

   Copyright (c) 2016 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 . . . . . . . . . . . . . .   5
   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.  Enqueue routine . . . . . . . . . . . . . . . . . . . . .  19
     5.5.  Dequeue routine . . . . . . . . . . . . . . . . . . . . .  19
     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

Nichols, et al.            Expires May 4, 2017                  [Page 2]

Internet-Draft                    CoDel                     October 2016

     11.1.  Normative References . . . . . . . . . . . . . . . . . .  25
     11.2.  Informative References . . . . . . . . . . . . . . . . .  26
     11.3.  URIs . . . . . . . . . . . . . . . . . . . . . . . . . .  27
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  27

1.  Introduction

   The need for queue management has been evident for decades.  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 this
   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", [TSV2011] [BB2011] has become increasingly
   important throughout the Internet but particularly at the consumer
   edge.  Recently, queue management has become more critical due to
   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, NETAL2010] 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 bloated.

   The development and deployment of effective active queue management
   has been hampered by persistent misconceptions about the cause and
   meaning of packet queues in network buffers.  Network buffers exist
   to absorb the packet bursts that occur naturally in statistically
   multiplexed networks.  Buffers helpfully absorb the queues created by
   such reasonable packet network behavior as 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.  Unfortunately, other
   less useful network behaviors can cause queues to fill and their
   effects are not nearly as benign.  Discussion of these issues and the
   reason why the solution is not simply smaller buffers can be found in
   [RFC2309], [VANQ2006], [REDL1998], and [CODEL2012].  To understand
   queue management, it is critical to understand the difference between
   the necessary, useful "good" queue, and the counterproductive "bad"

   Many approaches to active queue management (AQM) have been developed
   over the past two decades but none has been widely deployed due to

Nichols, et al.            Expires May 4, 2017                  [Page 3]

Internet-Draft                    CoDel                     October 2016

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

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

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

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

   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 controller.

   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.

   Since CoDel was first published [CODEL2012], a number of implementers
   have been using and adapting it with promising results.  Much of this
   work is collected at .
   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 a sophisticated queue management approach,
   fq_codel [FQ-CODEL-ID].

Nichols, et al.            Expires May 4, 2017                  [Page 4]

Internet-Draft                    CoDel                     October 2016

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].

   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.

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.  Careful tuning for
   particular usages has enabled queue management techniques to "kind
   of" work; that is, they have been able to decrease queueing delays,
   but only at the undue cost of link utilization and/or fairness.  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 queueing, 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 where 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 the bottleneck link 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; this standing queue size is determined by the
   mismatch between the window size and the pipe size, and is 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
   sending rate from the queue size, and any queue other than transient
   bursts only creates delays 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, and thus is clearly

Nichols, et al.            Expires May 4, 2017                  [Page 5]

Internet-Draft                    CoDel                     October 2016

   "bad queue".  If, in our example, there is a single bottleneck link
   and it 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 bottleneck link's 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, the "good queue" goes away in about a round trip time,
   while "bad queue" hangs around for longer, 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 a 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.

   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 at the expense of considerable implementation

   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.  It is not the queue

Nichols, et al.            Expires May 4, 2017                  [Page 6]

Internet-Draft                    CoDel                     October 2016

   length that should be controlled but the amount of excess delay
   packets experience due to a persistent or standing queue, which means
   that the packet sojourn time 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 allows queue
   management to be independent of link rate, gives superior performance
   to use of buffer size, and is directly related to user-visible
   performance.  It works regardless of line rate changes or link
   sharing by multiple queues (which the individual queues may
   experience as changing rates).

   Consider a link shared by two queues with different priorities.
   Packets that arrive at the high priority queue are sent as soon as
   the link is available while packets in the other queue have to wait
   until the high priority queue is empty (i.e., a strict priority
   scheduler).  The number of packets in the high 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 waiting time for the high priority packets to
   be sent.  This makes the sojourn time a good sample of the congestion
   that each separate queue is experiencing.  This example also shows
   how the metric of sojourn time is independent of the number of queues
   or the service discipline used, and is instead indicative of
   congestion seen by the individual queues.

   How can observed sojourn time be used to separate good queue from bad
   queue?  Although averages, especially of queue length, have
   previously been widely used as an indicator of bad queue, their
   efficacy is questionable.  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 queue sojourn time would be one-
   half the time it takes to clear the burst.  The average then would
   indicate a persistent queue where there is none.  Instead of averages
   we recommend tracking the minimum sojourn time, then, 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.

   A persistent 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 value is considered to be in effect,
   and is related to the amount of time it takes for the largest
   expected burst to drain.  Conservatively, this interval should be at
   least a round trip time to avoid falsely detecting a persistent queue

Nichols, et al.            Expires May 4, 2017                  [Page 7]

Internet-Draft                    CoDel                     October 2016

   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.

   (The following key insight makes computation of the local minimum
   efficient: 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.  We use this insight in the
   pseudo-code for CoDel later in the draft.)

   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 in and .

3.2.  Setpoint

   Now that we have a robust way of detecting standing queue, we need 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, this policy results in no backlog over time (no
   persistent queue), which negates much of the value of having a
   buffer, since it maximizes the bottleneck link bandwidth lost due to
   normal stochastic variation in packet interarrival time.  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', which is the
   ratio of a normalized throughput to a normalized delay [KLEIN81].

   It is straightforward to derive an analytic expression for the
   average goodput of a TCP conversation at a given round-trip time r
   and setpoint f (where f is expressed as a fraction of r).  Reno TCP,
   for example, yields:

   goodput = r (3 + 6f - f^2) / (4 (1+f))

Nichols, et al.            Expires May 4, 2017                  [Page 8]

Internet-Draft                    CoDel                     October 2016

   Since the peak queue delay is simply f r, power is solely a function
   of f since the r's in the numerator and denominator cancel:

   power is proportional to (1 + 2f - 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 f = 0.2, then a steep, almost linear fall off
   [TSV84].  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 0.1r 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 0.05r offers a good utilization vs. delay tradeoff while
   giving enough headroom to work well with a large variation in real

   As the above analysis shows, a very small standing queue gives close
   to 100% utilization of the bottleneck link.  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's 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 queueing 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)

Nichols, et al.            Expires May 4, 2017                  [Page 9]

Internet-Draft                    CoDel                     October 2016

   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
   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 queueing 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 source-destination-source 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

Nichols, et al.            Expires May 4, 2017                 [Page 10]

Internet-Draft                    CoDel                     October 2016

   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 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 does not know the number, duration, or kind of
   conversations creating its queue, so it has to learn the appropriate
   response.  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 and 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.  When
   the minimum sojourn time first crosses the target and CoDel drops a
   packet, the earliest the controller could see the effect of the drop
   is the round trip time (interval) + the local queue wait time
   (target).  If the next drop happens any earlier than this time
   (interval + target), CoDel will overdrop.  In practice, the local
   queue waiting time tends to vary, so making the initial drop spacing
   (i.e., the time to the second drop) be exactly the minimum possible
   also leads to overdropping.  Analysis of simulation and real-world
   measured data shows that the 75th percentile magnitude of this
   variation is less than the target, and so the initial drop spacing

Nichols, et al.            Expires May 4, 2017                 [Page 11]

Internet-Draft                    CoDel                     October 2016

   SHOULD be set to the estimator's interval (i.e., initial drop spacing
   = interval) to ensure that the controller has accounted for
   acceptable congestion delays.

   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.

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

   CoDel was initially designed as a bufferbloat solution for the
   consumer 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.  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 .

4.1.  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 terrestrial-
   based Internet, especially at the consumer edge, we expect most
   unbloated RTTs to have a ceiling of 100ms [CHARB2007].  Using this
   RTT gives a minimum target of 5ms and the interval for tracking the
   minimum is 100ms.  In practice, uncongested links will see sojourn
   times below target more often than once per RTT, so the estimator is
   not overly sensitive to the value of the interval.

   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 equal to the
   interval.  If the estimator's output falls below target, the

Nichols, et al.            Expires May 4, 2017                 [Page 12]

Internet-Draft                    CoDel                     October 2016

   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 would occur and lower link
   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.  This logic is described more precisely in the
   pseudo-code below.  Additional work is required to determine the
   frequency and importance of re-entering the dropping state.

   Note that CoDel AQM only enters its dropping state when the local
   minimum sojourn delay has exceeded target for a time interval long
   enough for normal bursts to dissipate, ensuring that a burst of
   packets that fits in the pipe will not be dropped.

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 bandwidths,
   including variable-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, therefore, 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.  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 (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.  An
   interval 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 a value of 100ms.  This will only

Nichols, et al.            Expires May 4, 2017                 [Page 13]

Internet-Draft                    CoDel                     October 2016

   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
   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 persistent 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, and above 5ms we saw
   very little or no improvement in utilization.  Thus target SHOULD be
   set to 5ms for normal Internet traffic.

   A congested (but not overloaded) CoDel link with traffic composed
   solely or primarily of long-lived TCP flows will have a 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

Nichols, et al.            Expires May 4, 2017                 [Page 14]

Internet-Draft                    CoDel                     October 2016

   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.

   Sojourn times must remain above the target for an entire interval in
   order to enter the drop state.  Any packet with a sojourn time less
   than the target will reset the time that the queue was last below the
   target.  Since Internet traffic has very dynamic characteristics, the
   actual sojourn delay 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 total amount of queueing
   delay experienced by a packet should be less than 10ms even under
   extremely congested conditions.  This net delay is substantially
   lower than common current queueing delays on the Internet that grow
   to orders of seconds [NETAL2010, CHARB2007].

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 circumvents this problem.  In a multiple-queue setting, a
   separate CoDel algorithm runs on each queue, but each CoDel instance
   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 does a multiple-queue CoDel system.  As an
   optimization to avoid queueing more than necessary, when testing for
   queue occupancy before dropping, the total occupancy of all queues
   sharing the same output link should be used.  This property of CoDel
   has been exploited in fq_codel, briefly discussed in the next section
   and in more detail in [FQ-CODEL-ID].

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 queueing.  (To understand further, see [SFQ1990] or
   the linux sfq documentation.) 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-

Nichols, et al.            Expires May 4, 2017                 [Page 15]

Internet-Draft                    CoDel                     October 2016

   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 described in [FQ-
   CODEL-ID].  Andrew McGregor has implemented a version of fq_codel for
   the network simulator ns-3 (

   We have also experimented with a similar multi-queue approach by
   creating an ns-2 simulator code module, sfqcodel, which uses
   Stochastic Fair Queueing (SFQ) to isolate flows into bins, each
   running CoDel.  This setup has provided excellent results: median
   queues remain small across a range of traffic 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 code, intended
   for simulation experiments, is available at
   [6]del.html [7] and being integrated into the ns-2 distribution.

   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 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 interconnect within a data
   center.  Since cluster traffic is entirely internal to the data
   center, round trip latencies are low (typically <100us) but

Nichols, et al.            Expires May 4, 2017                 [Page 16]

Internet-Draft                    CoDel                     October 2016

   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 overdropping and low
   throughput if used on the open Internet, so devices that allow CoDel
   to be configured SHOULD 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 have attempted to highlight just these additions by
   presenting CoDel as a sub-class of a basic fifo-queue base class.
   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 applications.

   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

   The following pseudo-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:

   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

Nichols, et al.            Expires May 4, 2017                 [Page 17]

Internet-Draft                    CoDel                     October 2016

   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 enqueue routine and used by
   the dequeue routine).

   queue_t is a base class for queue objects (the parent class for
   codel_queue_t objects).  We assume it has enqueue() and dequeue()
   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 dequeue()
   method but shouldn't require a lock with the enqueue() method.

   flag_t is a Boolean.

Nichols, et al.            Expires May 4, 2017                 [Page 18]

Internet-Draft                    CoDel                     October 2016

5.2.  Per-queue state (codel_queue_t instance variables)

   time_t first_above_time_ = 0; // Time to declare sojourn time above
                                 // target
   time_t drop_next_ = 0;        // Time to drop next packet
   uint32_t count_ = 0;          // Packets dropped in dropping state
   uint32_t lastcount_ = 0;      // Count from previous iteration
   flag_t dropping_ = false;     // Set to true 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.  Enqueue routine

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

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

5.5.  Dequeue routine

   This is the heart of CoDel.  There are two branches based on whether
   the controller is in dropping state: (i) if the controller is in
   dropping state (that is, the minimum packet sojourn time is greater
   than target) then the controller checks if it is time to leave
   dropping state or schedules the next drop(s); or (ii) if the
   controller is not in dropping state, it determines if it should enter
   dropping state and do the initial drop.

   packet_t* CoDelQueue::dequeue()
       time_t now = clock();
       dodequeue_result r = dodequeue(now);
       uint32_t delta;

       if (dropping_) {
           if (! r.ok_to_drop) {
               // sojourn time below target - leave dropping state

Nichols, et al.            Expires May 4, 2017                 [Page 19]

Internet-Draft                    CoDel                     October 2016

               dropping_ = false;
           // 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 = dodequeue(now);
               if (! r.ok_to_drop) {
                   // leave dropping state
                   dropping_ = false;
               } else {
                   // schedule the next drop.
                   drop_next_ = control_law(drop_next_, count_);
       // If we get here we're not in dropping state. The 'ok_to_drop'
       // return from dodequeue means that the sojourn time has been
       // above 'target' for 'interval' so enter dropping state.
       } else if (r.ok_to_drop) {
           r = dodequeue(now);
           dropping_ = true;

           // 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.) Implementations vary slightly here; this is
           // the Linux version, which is more widely deployed and
           // tested.
           delta = count_ - lastcount_;
           count_ = (delta > 1 && now - drop_next_ < 16*interval_)?
                        delta : 1;
           drop_next_ = control_law(now, count_);
           lastcount_ = count_;
       return (r.p);

Nichols, et al.            Expires May 4, 2017                 [Page 20]

Internet-Draft                    CoDel                     October 2016

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.

   time_t codel_queue_t::control_law(time_t t, uint32_t count)
       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

Nichols, et al.            Expires May 4, 2017                 [Page 21]

Internet-Draft                    CoDel                     October 2016

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

   dodequeue_result codel_queue_t::dodequeue(time_t now)
       dodequeue_result r = { queue_t::dequeue(), false };
       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 = true;
       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 is relatively simple to add CoDel to almost any packet
   processing pipeline, including ASIC- or NPU-based forwarding engines.

Nichols, et al.            Expires May 4, 2017                 [Page 22]

Internet-Draft                    CoDel                     October 2016

   One issue to consider is dodequeue()'s use of a 'bytes()' function to
   determine the current queue size in bytes.  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 simply the difference between
   these two counters (32-bit counters should be adequate.)  Enqueue has
   to update its counter once per packet queued but it does not 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 has been implemented and tested in a range of environments.

6.1.  Validations and available code

   An experiment by Stanford graduate students successfully used Linux
   CoDel to duplicate our published simulation work on CoDel's ability
   to adapt to drastic link rate changes.  This experiment can be found
   solving-bufferbloat-the-codel-way/ .

   Our ns-2 simulations are available at .
   CableLabs 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
   few bytes are in the queue, and other 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 available in ns-2 version 2.35 and later.

   Andrew McGregor has an ns-3 implementation of both CoDel and
   fq_codel.  CoDel is available in ns-3 version 3.21 and later at . At the time of this writing, the ns-3
   implementation of fq_codel is available at .

   CoDel is available in Linux.  Eric Dumazet implemented CoDel in the
   Linux kernel.

Nichols, et al.            Expires May 4, 2017                 [Page 23]

Internet-Draft                    CoDel                     October 2016

   Dave Taht integrated and distributed CoDel as a bufferbloat solution
   ( ).

6.2.  CoDel in the datacenter

   Nandita Dukkipati and her group at Google realized 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 is
       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 setpoint 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 as there would be on the
   public Internet.

   In several data center workload benchmarks, which are typically
   bursty, CoDel reduced the queueing latencies at the qdisc, and
   thereby improved the mean and 99th-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

Nichols, et al.            Expires May 4, 2017                 [Page 24]

Internet-Draft                    CoDel                     October 2016

   qdisc buffers that are inherently bursty because of TCP Segmentation
   Offload (TSO).

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.  We encourage
   experimentation, improvement, and rigorous testing.

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

10.  Acknowledgments

   The authors thank Jim Gettys for the constructive nagging that made
   us get the work "out there" before we thought it was ready.  We 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 thank
   Nandita Dukkipati for contributions to Section 6 and for comments
   which helped to substantially improve this draft.  We thank the AQM
   working group and the Transport Area shepherd, Wes Eddy, for
   patiently prodding this draft all the way to a standard.

11.  References

11.1.  Normative References

   [RFC2119]  Bradner, S., "Key Words for use in RFCs to Indicate
              Requirement Levels", March 1997.

Nichols, et al.            Expires May 4, 2017                 [Page 25]

Internet-Draft                    CoDel                     October 2016

11.2.  Informative References

              Hoeiland-Joergensen, T., McKenney, P.,
    , d., Gettys, J., and E. Dumazet,
              "FlowQueue-Codel", draft-ietf-aqm-fq-codel-03 (work in
              progress), November 2015.

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

   [RFC896]   Nagle, J., "Congestion control in IP/TCP internetworks",
              RFC 896, January 1984.

   [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.

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

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

   [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.

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

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

Nichols, et al.            Expires May 4, 2017                 [Page 26]

Internet-Draft                    CoDel                     October 2016

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

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

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

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

              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.

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

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

11.3.  URIs



Authors' Addresses

Nichols, et al.            Expires May 4, 2017                 [Page 27]

Internet-Draft                    CoDel                     October 2016

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


   Van Jacobson


   Andrew McGregor


   Janardhan Iyengar


Nichols, et al.            Expires May 4, 2017                 [Page 28]