path: root/doc/rfc/rfc8289.txt

Internet Engineering Task Force (IETF)                        K. Nichols
Request for Comments: 8289                                 Pollere, Inc.
Category: Experimental                                       V. Jacobson
ISSN: 2070-1721                                         A. McGregor, Ed.
                                                         J. Iyengar, Ed.
                                                                  Google
                                                            January 2018


                Controlled Delay Active Queue Management

Abstract

   This document describes CoDel (Controlled Delay) -- a general
   framework 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.

Status of This Memo

   This document is not an Internet Standards Track specification; it is
   published for examination, experimental implementation, and
   evaluation.

   This document defines an Experimental Protocol for the Internet
   community.  This document is a product of the Internet Engineering
   Task Force (IETF).  It represents the consensus of the IETF
   community.  It has received public review and has been approved for
   publication by the Internet Engineering Steering Group (IESG).  Not
   all documents approved by the IESG are a candidate for any level of
   Internet Standard; see Section 2 of RFC 7841.

   Information about the current status of this document, any errata,
   and how to provide feedback on it may be obtained at
   https://www.rfc-editor.org/info/rfc8289.















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Copyright Notice

   Copyright (c) 2018 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
   (https://trustee.ietf.org/license-info) 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 and Terms Used in This Document . . . . . . . . .   4
   3.  Understanding the Building Blocks of Queue Management . . . .   5
     3.1.  Estimator . . . . . . . . . . . . . . . . . . . . . . . .   6
     3.2.  Target Setpoint . . . . . . . . . . . . . . . . . . . . .   8
     3.3.  Control Loop  . . . . . . . . . . . . . . . . . . . . . .  10
   4.  Overview of the CoDel AQM . . . . . . . . . . . . . . . . . .  13
     4.1.  Non-starvation  . . . . . . . . . . . . . . . . . . . . .  14
     4.2.  Setting INTERVAL  . . . . . . . . . . . . . . . . . . . .  14
     4.3.  Setting TARGET  . . . . . . . . . . . . . . . . . . . . .  14
     4.4.  Use with Multiple Queues  . . . . . . . . . . . . . . . .  15
     4.5.  Setting Up CoDel  . . . . . . . . . . . . . . . . . . . .  16
   5.  Annotated Pseudocode for CoDel AQM  . . . . . . . . . . . . .  16
     5.1.  Data Types  . . . . . . . . . . . . . . . . . . . . . . .  17
     5.2.  Per-Queue State (codel_queue_t Instance Variables)  . . .  17
     5.3.  Constants . . . . . . . . . . . . . . . . . . . . . . . .  17
     5.4.  Enqueue Routine . . . . . . . . . . . . . . . . . . . . .  18
     5.5.  Dequeue Routine . . . . . . . . . . . . . . . . . . . . .  18
     5.6.  Helper Routines . . . . . . . . . . . . . . . . . . . . .  19
     5.7.  Implementation Considerations . . . . . . . . . . . . . .  21
   6.  Further Experimentation . . . . . . . . . . . . . . . . . . .  21
   7.  Security Considerations . . . . . . . . . . . . . . . . . . .  21
   8.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  21
   9.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  22
     9.1.  Normative References  . . . . . . . . . . . . . . . . . .  22
     9.2.  Informative References  . . . . . . . . . . . . . . . . .  22
   Appendix A.  Applying CoDel in the Data Center  . . . . . . . . .  24
   Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . .  25
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  25





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1.  Introduction

   The "persistently full buffer" problem has been discussed in the IETF
   community since the early 80s [RFC896].  The IRTF's End-to-End
   Research 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 growth in memory
   density per Moore's Law 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
   "bufferbloat" problem [BLOAT] has become increasingly important
   throughout the Internet but particularly at the consumer edge.  Queue
   management has become more critical due to increased consumer use of
   the Internet, mixing large video transactions with time-critical VoIP
   and gaming.

   An effective AQM remediates bufferbloat at a bottleneck while "doing
   no harm" at hops where buffers are not bloated.  However, the
   development and deployment of AQM are frequently subject to
   misconceptions about the cause 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 reasonable packet network behavior such
   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" queue.

   Several approaches to AQM have been developed over the past two
   decades, but none have 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.
   Learning from this past history, the CoDel approach is designed to
   meet the following goals:

   o  Make AQM parameterless for normal operation, with no knobs for
      operators, users, or implementers to adjust.

   o  Be able to distinguish "good" queue from "bad" queue and treat
      them differently, that is, keep delay low while permitting
      necessary bursts of traffic.



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   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  Allow simple and efficient implementation (can easily span the
      spectrum from low-end access points and home routers up to high-
      end router hardware).

   CoDel has five major differences from prior AQMs: use of the 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 a mathematically determined setpoint derived
   from maximizing network power [KLEIN81], and a modern state-space
   controller.

   CoDel configures itself based on a round-trip time metric that can be
   set to 100 ms for the normal, terrestrial Internet.  With no changes
   to parameters, CoDel is expected to work across a wide range of
   conditions, with varying links and the full range of terrestrial
   round-trip times.

   CoDel is easily adapted to multiple queue systems as shown by
   [RFC8290].  Implementers and users SHOULD use the fq_codel multiple-
   queue approach as it deals with many problems beyond the reach of an
   AQM on a single queue.

   CoDel was first published in [CODEL2012] and has been implemented in
   the Linux kernel.

   Note that while this document refers to dropping packets when
   indicated by CoDel, it may be reasonable to ECN-mark packets instead.

2.  Conventions and Terms Used in This Document

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "NOT RECOMMENDED", "MAY", and
   "OPTIONAL" in this document are to be interpreted as described in
   BCP 14 [RFC2119] [RFC8174] when, and only when, they appear in all
   capitals, as shown here.








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   The following terms are used in this document and are defined as
   follows:

   sojourn time:  the amount of time a packet has spent in a particular
         buffer, i.e., the time a packet departs the buffer minus the
         time the packet arrived at the buffer.  A packet can depart a
         buffer via transmission or drop.

   standing queue:  a queue (in packets, bytes, or time delay) in a
         buffer that persists for a "long" time, where "long" is on the
         order of the longer round-trip times through the buffer, as
         discussed in Section 4.2.  A standing queue occurs when the
         minimum queue over the "long" time is non-zero and is usually
         tolerable and even desirable as long as it does not exceed some
         target delay.

   bottleneck bandwidth:  the limiting bandwidth along a network path.

3.  Understanding the Building Blocks of Queue Management

   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 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; it 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 5-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 10 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 the sending rate.



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   In the above example, the 5-packet standing queue can be seen to
   contribute nothing but delay to the connection and thus is clearly
   "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), then 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 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 - To figure out what we've got.

   o  Target setpoint - To know 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 parts to 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 Random Early Detection (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 complexity.






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   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
   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, there is no
   persistent queue.

   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



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   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
   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.

   Note that the following key insight makes computation of the local
   minimum efficient: it is sufficient to keep a single state variable
   that indicates how long the minimum has been above or below the
   target value rather than retaining all the local values to compute
   the minimum, which leads to both storage and computational savings.
   We use this insight in the pseudocode for CoDel later in the
   document.

   These two parts, use of sojourn time as the observed value 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
   [TSV84].

3.2.  Target Setpoint

   Now that we have a robust way of detecting standing queue, we need a
   target 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
   target 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].







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

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

   Since the peak queue delay is simply the product of f and 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 trade-off) 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 Additive Increase Multiplicative
   Decrease (AIMD) TCP is monotone decreasing.  But the curve is very
   flat for f < 0.1, followed by an 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 trade-off 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 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 target:
   the ideal range for the permitted standing queue, or the target
   setpoint, is between 5% and 10% of the TCP connection's RTT.

   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 500 ms and link bandwidths of 64 Kbps to 100 Mbps to
   experimentally explore a value for the target that gives consistently





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   high utilization while controlling delay across a range of
   bandwidths, RTTs, and traffic loads.  Our results were notably
   consistent with the mathematics above.

   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 that 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 the 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, as shown in [CODEL2012].

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 target setpoint for this measure that's a
   provably good balance between enhancing throughput and minimizing
   delay.  Section 3.2 also shows that this target 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 the target" state to a state where the persistent queue is
   below the 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
   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 that start and stop
   at arbitrary times and that 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.





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   Since queueing systems match none of the prerequisites for a
   classical controller, a better approach is a modern state-space
   controller with "no persistent queue" and "has persistent queue"
   states.  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 than 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); 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 and 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) [RFC5681], 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 factor of two [RFC5681]; 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.



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   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) and then increasing the drop rate slowly until the persistent
   queue goes below the 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 drop state was entered, using
   the well-known non-linear relationship of drop rate to throughput to
   get a linear change in throughput [REDL1998][MACTCP1997].

   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.  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 (the 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, so the initial drop spacing 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 target.  Dropping
   at dequeue has both implementation and control advantages.









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RFC 8289                          CoDel                     January 2018


4.  Overview of the CoDel AQM

   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, be easily used with multiple
   queues, have excellent utilization with low delay, and have a simple
   and efficient implementation.

   The CoDel algorithm described in the rest of this document uses two
   key variables: TARGET, which is the controller's target setpoint
   described in Section 3.2, and INTERVAL, which is the estimator's
   interval described in Section 3.3.

   The only setting CoDel requires is the INTERVAL value, and as 100 ms
   satisfies that definition for normal Internet usage, CoDel can be
   parameter-free for consumer use.  To ensure that link utilization is
   not adversely affected, CoDel's estimator sets TARGET to one that
   optimizes power.  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
   shows that an ideal TARGET is 5-10% of the connection round-trip time
   (RTT).  In the open terrestrial-based Internet, especially at the
   consumer edge, we expect most unbloated RTTs to have a ceiling of 100
   ms [CHARB2007].  Using this RTT gives a minimum TARGET of 5 ms and
   INTERVAL of 100 ms.  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 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 and therefore SHOULD be set to a value
   equal to INTERVAL.  If the estimator's output falls below 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 value, since an unnecessary drop would occur and lower link
   utilization).  If the 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 drop state too soon after
   exiting it and resumes the drop state at a recent control level, if
   one exists.  This logic is described more precisely in the pseudocode
   below.  Additional work is required to determine the frequency and
   importance of re-entering the drop state.





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

4.1.  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; 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 interface driver.

4.2.  Setting INTERVAL

   The INTERVAL value is chosen to give endpoints time to react to a
   drop without being so long that response times suffer.  CoDel's
   estimator, TARGET, and control loop all use 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 a priori.  The best policy seems to be
   to use an INTERVAL value 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 (however, see Appendix A for an application where
   INTERVAL is measured).  An INTERVAL setting of 100 ms works well
   across a range of RTTs from 10 ms to 1 second (excellent performance
   is achieved in the range from 10 ms to 300 ms).  For devices intended
   for the normal terrestrial Internet, INTERVAL SHOULD have a value of
   100 ms.  This will only cause overdropping where a long-lived TCP has
   an RTT longer than 100 ms and there is little or no mixing with other
   connections through the link.

4.3.  Setting TARGET

   TARGET is the maximum acceptable persistent queue delay above which
   CoDel is dropping or preparing to drop and below which CoDel will not
   drop.  TARGET SHOULD be set to 5 ms for normal Internet traffic.

   The calculations of Section 3.2 show that the best TARGET value is
   5-10% of the RTT, with the low end of 5% preferred.  Extensive
   simulations exploring the impact of different TARGET values when used



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   with mixed traffic flows with different RTTs and different bandwidths
   show that below a TARGET of 5 ms, utilization suffers for some
   conditions and traffic loads; above 5 ms showed very little or no
   improvement in utilization.

   Sojourn times must remain above the TARGET for INTERVAL amount of
   time 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 TARGET.  Since Internet traffic has very dynamic
   characteristics, the actual sojourn delay experienced by packets
   varies greatly and is often less than 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
   10 ms 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].

   Regarding the roles of TARGET and the minimum-tracking INTERVAL, note
   that TARGET SHOULD NOT be increased in response to lower layers that
   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 amount of time, and increasing
   TARGET only leads to longer queue delays.  On the other hand, where a
   significant additional delay is added to the intrinsic RTT of most or
   all packets due to the waiting time for a transmission, it is
   necessary to increase INTERVAL by that extra delay.  TARGET SHOULD
   NOT be adjusted for such short-term bursts, but INTERVAL MAY need to
   be adjusted if the path's intrinsic RTT changes.

4.4.  Use with Multiple Queues

   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 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



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RFC 8289                          CoDel                     January 2018


   fq_codel [RFC8290], which hashes on the packet header fields to
   determine a specific bin, or sub-queue, for the packet 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").

4.5.  Setting Up CoDel

   CoDel is set for use in devices in the open Internet.  An INTERVAL
   setting of 100 ms is used, TARGET is set to 5% of INTERVAL, and the
   initial drop spacing is also set to the 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
   <100 us) but bandwidths are high (1-40 Gbps), 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 and 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
   Explicit Congestion Notification (ECN) marks while keeping throughput
   high and latency low.

   Devices destined for these environments MAY use a different value for
   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
   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 the
   Internet-appropriate values given in this document.

5.  Annotated Pseudocode for CoDel AQM

   What follows is the CoDel algorithm in C++-like pseudocode.  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.







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   Implementors are strongly encouraged to also look at the Linux kernel
   version of CoDel -- a well-written, well-tested, real-world, C-based
   implementation.  As of this writing, it is available at
   https://github.com/torvalds/linux/blob/master/net/sched/sch_codel.c.

5.1.  Data Types

   time_t is an integer time value in units convenient for the system.
   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 the 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.

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 drop 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);     // 5 ms TARGET queue delay
   time_t INTERVAL = MS2TIME(100); // 100 ms sliding-minimum window
   u_int maxpacket = 512;          // Maximum packet size in bytes
                                   // (SHOULD use interface MTU)











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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.  Note that packets arriving at a full buffer will be
   dropped, but these drops are not counted towards CoDel's
   computations.

   void codel_queue_t::enqueue(packet_t* pkt)
   {
       pkt->tstamp = clock();
       queue_t::enqueue(pkt);
   }

5.5.  Dequeue Routine

   This is the heart of CoDel.  There are two branches based on whether
   the controller is in drop state: (i) if the controller is in drop
   state (that is, the minimum packet sojourn time is greater than
   TARGET), then the controller checks if it is time to leave drop state
   or schedules the next drop(s); or (ii) if the controller is not in
   drop state, it determines if it should enter drop 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 drop state
               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_) {
               drop(r.p);
               ++count_;
               r = dodequeue(now);
               if (! r.ok_to_drop) {
                   // leave drop state
                   dropping_ = false;



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               } else {
                   // schedule the next drop.
                   drop_next_ = control_law(drop_next_, count_);
               }
           }
       // If we get here, we're not in drop state.  The 'ok_to_drop'
       // return from dodequeue means that the sojourn time has been
       // above 'TARGET' for 'INTERVAL', so enter drop state.
       } else if (r.ok_to_drop) {
           drop(r.p);
           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_ = 1;
           if ((delta > 1) && (now - drop_next_ < 16*INTERVAL))
               count_ = delta;

           drop_next_ = control_law(now, count_);
           lastcount_ = count_;
       }
       return (r.p);
   }

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);
   }



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   Next is a helper routine that 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
   amount of time.  It returns two values: a Boolean indicating if it is
   OK to drop (sojourn time above TARGET for at least INTERVAL) and the
   packet dequeued.

   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 based on sojourn time
       // 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 based on backlog 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;
   }



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5.7.  Implementation Considerations

   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.

   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 forwarding engines based on
   Application-Specific Integrated Circuits (ASICs) or Network
   Processors (NPUs).  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.  Further Experimentation

   We encourage experimentation with the recommended values of TARGET
   and INTERVAL for Internet settings.  CoDel provides general,
   efficient, parameterless building blocks for queue management that
   can be applied to single or multiple queues in a variety of data
   networking scenarios.  CoDel's settings may be modified for other
   special-purpose networking applications.

7.  Security Considerations

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

8.  IANA Considerations

   This document does not require actions by IANA.



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9.  References

9.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,
              <https://www.rfc-editor.org/info/rfc2119>.

   [RFC8174]  Leiba, B., "Ambiguity of Uppercase vs Lowercase in RFC
              2119 Key Words", BCP 14, RFC 8174, DOI 10.17487/RFC8174,
              May 2017, <https://www.rfc-editor.org/info/rfc8174>.

9.2.  Informative References

   [BLOAT]    Gettys, J. and K. Nichols, "Bufferbloat: Dark Buffers in
              the Internet", Communications of the ACM, Volume 55, Issue
              1, DOI 10.1145/2063176.2063196, January 2012.

   [CHARB2007]
              Dischinger, M., Haeberlen, A., Gummadi, K., and S. Saroiu,
              "Characterizing Residential Broadband Networks",
              Proceedings of the 7th ACM SIGCOMM Conference on Internet
              Measurement, DOI 10.1145/1298306.1298313, October 2007.

   [CODEL2012]
              Nichols, K. and V. Jacobson, "Controlling Queue Delay",
              ACM Queue, Volume 10, Issue 5,
              DOI 10.1145/2208917.2209336, May 2012.

   [KLEIN81]  Kleinrock, L. and R. Gail, "An Invariant Property of
              Computer Network Power", Proceedings of the International
              Conference on Communications, June 1981,
              <http://www.lk.cs.ucla.edu/data/files/Gail/power.pdf>.

   [MACTCP1997]
              Mathis, M., Semke, J., Mahdavi, J., and T. Ott, "The
              Macroscopic Behavior of the TCP Congestion Avoidance
              Algorithm", ACM SIGCOMM Computer Communications
              Review, Volume 27, Issue 3, pp. 67-82,
              DOI 10.1145/263932.264023, July 1997.

   [NETAL2010]
              Kreibich, C., Weaver, N., Paxson, V., and B. Nechaev,
              "Netalyzr: Illuminating the Edge Network", Proceedings of
              the 10th ACM SIGCOMM Conference on Internet Measurement,
              DOI 10.1145/1879141.1879173, November 2010.




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   [REDL1998] Nichols, K., Jacobson, V., and K. Poduri, "RED in a
              Different Light", Technical Report, Cisco Systems,
              September 1999, <http://citeseerx.ist.psu.edu/viewdoc/
              summary?doi=10.1.1.22.9406>.

   [RFC896]   Nagle, J., "Congestion Control in IP/TCP Internetworks",
              RFC 896, DOI 10.17487/RFC0896, January 1984,
              <https://www.rfc-editor.org/info/rfc896>.

   [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, DOI 10.17487/RFC2309, April 1998,
              <https://www.rfc-editor.org/info/rfc2309>.

   [RFC5681]  Allman, M., Paxson, V., and E. Blanton, "TCP Congestion
              Control", RFC 5681, DOI 10.17487/RFC5681, September 2009,
              <https://www.rfc-editor.org/info/rfc5681>.

   [RFC8290]  Hoeiland-Joergensen, T., McKenney, P., Taht, D.,
              Gettys, J., and E. Dumazet, "The Flow Queue CoDel Packet
              Scheduler and Active Queue Management Algorithm",
              RFC 8290, DOI 10.17487/RFC8290, January 2018,
              <https://www.rfc-editor.org/info/rfc8290>.

   [TSV84]    Jacobson, V., "CoDel", IETF 84, Transport Area Open
              Meeting, July 2012,
              <http://www.ietf.org/proceedings/84/slides/
              slides-84-tsvarea-4.pdf>.

   [VANQ2006] Jacobson, V., "A Rant on Queues", Talk at MIT Lincoln
              Labs, Lexington, MA, July 2006,
              <http://www.pollere.net/Pdfdocs/QrantJul06.pdf>.
















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Appendix A.  Applying CoDel in the Data Center

   Nandita Dukkipati and her group at Google realized that the CoDel
   building blocks could be applied to bufferbloat problems in data-
   center 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 to the history entry, and the process is
       repeated.  The use of the dynamic TCP RTT estimate allows the
       interval to 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, which is particularly helpful in controlling
   qdisc buffers that are inherently bursty because of TCP Segmentation
   Offload (TSO).





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RFC 8289                          CoDel                     January 2018


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
   that helped to substantially improve this document.  We thank the AQM
   Working Group and the Transport Area Shepherd, Wes Eddy, for
   patiently prodding this document all the way to publication as an
   RFC.

Authors' Addresses

   Kathleen Nichols
   Pollere, Inc.
   PO Box 370201
   Montara, CA  94037
   United States of America

   Email: nichols@pollere.com


   Van Jacobson
   Google

   Email: vanj@google.com


   Andrew McGregor (editor)
   Google

   Email: andrewmcgr@google.com


   Janardhan Iyengar (editor)
   Google

   Email: jri@google.com












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