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Versions: 00 draft-ietf-aqm-fq-implementation

Active Queue Management                                         F. Baker
Internet-Draft                                                    R. Pan
Intended status: Informational                             Cisco Systems
Expires: December 15, 2014                                 June 13, 2014


                   On Queuing, Marking, and Dropping
                 draft-baker-aqm-sfq-implementation-00

Abstract

   This note discusses implementation strategies for coupled queuing and
   mark/drop algorithms.

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
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   This Internet-Draft will expire on December 15, 2014.

Copyright Notice

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

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   described in the Simplified BSD License.






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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Fair Queuing: Algorithms and History  . . . . . . . . . . . .   2
     2.1.  Generalized Processor Sharing . . . . . . . . . . . . . .   3
       2.1.1.  GPS Comparisons: transmission quanta  . . . . . . . .   3
       2.1.2.  GPS Comparisons: flow definition  . . . . . . . . . .   4
       2.1.3.  GPS Comparisons: unit of measurement  . . . . . . . .   5
     2.2.  GPS Approximations  . . . . . . . . . . . . . . . . . . .   5
       2.2.1.  Definition of a queuing algorithm . . . . . . . . . .   5
       2.2.2.  Round Robin Models  . . . . . . . . . . . . . . . . .   6
       2.2.3.  Calendar Queue Models . . . . . . . . . . . . . . . .   7
       2.2.4.  Work Conserving Models and Stochastic Fairness
               Queuing . . . . . . . . . . . . . . . . . . . . . . .   8
       2.2.5.  Non Work Conserving Models and Virtual Clock  . . . .   9
   3.  Queuing, Marking, and Dropping  . . . . . . . . . . . . . . .  10
     3.1.  Queuing with Tail Mark/Drop . . . . . . . . . . . . . . .  10
     3.2.  Queuing with CoDel Mark/Drop  . . . . . . . . . . . . . .  10
     3.3.  Queuing with PIE Mark/Drop  . . . . . . . . . . . . . . .  11
   4.  Conclusion  . . . . . . . . . . . . . . . . . . . . . . . . .  12
   5.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  12
   6.  Security Considerations . . . . . . . . . . . . . . . . . . .  12
   7.  Acknowledgements  . . . . . . . . . . . . . . . . . . . . . .  12
   8.  References  . . . . . . . . . . . . . . . . . . . . . . . . .  13
     8.1.  Normative References  . . . . . . . . . . . . . . . . . .  13
     8.2.  Informative References  . . . . . . . . . . . . . . . . .  13
   Appendix A.  Change Log . . . . . . . . . . . . . . . . . . . . .  14
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  14

1.  Introduction

   In the discussion of Active Queue Management, there has been
   discussion of the coupling of queue management algorithms such as
   Stochastic Fairness Queuing [SFQ] with mark/drop algorithms such as
   CoDel [I-D.nichols-tsvwg-codel] or PIE [I-D.pan-tsvwg-pie].  In the
   interest of clarifying the discussion, we document possible
   implementation approaches to that, and analyze the possible effects
   and side-effects.  The language and model derive from the
   Architecture for Differentiated Services [RFC2475].

2.  Fair Queuing: Algorithms and History

   There is extensive history in the set of algorithms collectively
   referred to as "Fair Queuing".  The model was initially discussed in
   [RFC0970], which proposed it hypothetically as a solution to the TCP
   Silly Window Syndrome issue in BSD 4.1.  The problem was that, due to
   a TCP implementation bug, some senders would settle into sending a
   long stream of very short segments, which unnecessarily consumed



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   bandwidth on TCP and IP headers and occupied short packet buffers,
   thereby disrupting competing sessions.  Nagle suggested that if
   packet streams were sorted by their source address and the sources
   treated in a round robin fashion, a sender's effect on end-to-end
   latency and increased loss rate would primarily affect only itself.
   This touched off perhaps a decade of work by various researchers on
   what was and is termed "Fair Queuing," philosophical discussions of
   the meaning of the word "fair," operational reasons that one might
   want a "weighted" or "predictably unfair" queuing algorithm, and so
   on.

2.1.  Generalized Processor Sharing

   Conceptually, any Fair Queuing algorithm attempts to implement some
   approximation to the Generalized Processor Sharing [GPS] model.

   The GPS model, in its essence, presumes that a set of identified data
   streams, called "flows", pass through an interface.  Each flow has a
   rate when measured over a period of time; A voice session might, for
   example, require 64 KBPS plus whatever overhead is necessary to
   deliver it, and a TCP session might have variable throughput
   depending on where it is in its evolution.  The premise of
   Generalized Processor Sharing is that on all time scales, the flow
   occupies a predictable bit rate, so that if there is enough bandwidth
   for the flow in the long term, it also lacks nothing in the short
   term.  "All time scales" is obviously untenable in a packet network -
   and even in a traditional TDM circuit switch network - because a
   timescale shorter than he duration of a packet will only see one
   packet at a time.  But it provides an ideal for other models to be
   compared against.

   There are a number of attributes of approximations to the GPS model
   that bear operational consideration, including at least the
   transmission quanta, the definition of a "flow", the unit of
   measurement.  Implementation algorithms have different practical
   impacts as well.

2.1.1.  GPS Comparisons: transmission quanta

   The most obvious comparison between the GPS model and common
   approximations to it is that real world data is not delivered
   uniformly, but in some quantum.  The smallest quantum, in a packet
   network, is a packet.  But quanta can be larger; for example, in
   video applications it is common to describe data flow in frames per
   second, where a frame describes a picture on a screen or the changes
   made from a previous one.  A single video frame is commonly on the
   order of tens of packets.  If a codec is delivering thirty frames per
   second, it is conceivable that the packets comprising a frame might



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   be sent as thirty bursts per second, with each burst sent at the
   interface rate of the camera or other sender.  Similarly, TCP
   exchanges have an initial window, which might be any number of
   packets; common values are 1, 2, 3, 4, and 10, and there are also
   reports of bursts of 65K bytes at the relevant MSS, which is to say
   about 45 packets in one burst, presumably coming from TCP offload
   engines.  After that initial burst, TCP senders commonly send pairs
   of packets, but may send either smaller or larger bursts, and the
   rate at which they send is governed by the arrival rate of
   acknowledgements from the receiver.

2.1.2.  GPS Comparisons: flow definition

   An important engineering trade-off relevant to GPS is the definition
   of a "flow".  A flow is, by definition, a defined data stream.
   Common definitions include:

   o  Packets in a single transport layer session ("microflow"),
      identified by a five-tuple [RFC2990],

   o  Packets between a single pair of addresses, identified by a source
      and destination address or prefix,

   o  Packets from a single source address or prefix [RFC0970],

   o  Packets to a single destination address or prefix,

   o  Packets to or from a single subscriber, customer, or peer
      [RFC6057].  In Service Provider operations, this might be a
      neighboring Autonomous System; in broadband, a residential
      customer.

   The difference should be apparent.  Consider a comparison between
   sorting by source address or destination address, to pick two
   examples, in the case that a given router interface has N application
   sessions going through it between N/2 local destinations and N remote
   sources.  Sorting by source, or in this case by source/destination
   pair, would give each remote peer an upper bound guarantee of 1/N of
   the available capacity, which might be distributed very unevenly
   among the local destinations.  Sorting by destination would give each
   local destination an upper bound guarantee of 2/N of the available
   capacity, which might be distributed very unevenly among the remote
   systems and correlated sessions.  Who is one fair to?  In both cases,
   they deliver equal service by their definition, but that might not be
   someone else's definition.






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2.1.3.  GPS Comparisons: unit of measurement

   And finally, there is the question of what is measured for rate.  If
   the sole objective is to force packet streams to not dominate each
   other, it is sufficient to count packets.  However, if the issue is
   the bit rate of an SLA, one must consider the sizes of the packets
   (the aggregate throughput of a flow, measured in bits or bytes).  And
   if predictable unfairness is a consideration, the value must be
   weighted accordingly.

2.2.  GPS Approximations

   Carrying the matter further, a queuing algorithm may also be termed
   "Work Conserving" or "Non Work Conserving".  A "work conserving"
   algorithm, by definition, is either empty, in which case no attempt
   is being made to dequeue data from it, or contains something, in
   which case it continuously tries to empty the queue.  A work
   conserving queue that contains queued data, at an interface with a
   given rate, will deliver data at that rate until it empties.  A non-
   work-conserving queue might stop delivering even through it still
   contains data.  A common reason for doing this is to impose an
   artificial upper bound on a class of traffic that is lower than the
   rate of the underlying physical interface.

2.2.1.  Definition of a queuing algorithm

   In the discussion following, we assume a basic definition of a
   queuing algorithm.  A queuing algorithm has, at minimum:

   o  Some form of internal storage for the elements kept in the queue,

   o  If it has multiple internal classifications,

      *  a method for classifying elements,

      *  additional storage for the classifier and implied classes,

   o  a method for creating the queue,

   o  a method for destroying the queue,

   o  a method, called "enqueue", for placing packets into the queue or
      queuing system

   o  a method, called "dequeue", for removing packets from the queue or
      queuing system





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   There may also be other information or methods, such as the ability
   to inspect the queue.  It also often has inspectable external
   attributes, such as the total volume of packets or bytes in queue,
   and may have limit thresholds, such as a maximum number of packets or
   bytes the queue might hold.

   For example, a simple FIFO queue has a linear data structure,
   enqueues packets at the tail, and dequeues packets from the head.  It
   might have a maximum queue depth and a current queue depth,
   maintained in packets or bytes.

2.2.2.  Round Robin Models

   One class of implementation approaches, generically referred to as
   "Weighted Round Robin", implements the structure of the queue as an
   array or ring of queues associated with flows, for whatever
   definition of a flow is important.

   On enqueue, the enqueue function classifies a packet and places it
   into a simple FIFO sub-queue.

   On dequeue, the sub-queues are searched in round-robin order, and
   when a sub-queue is identified that contains data, removes a
   specified quantum of data from it.  That quantum is at minimum a
   packet, but it may be more.  If the system is intended to maintain a
   byte rate, there will be memory between searches of the excess
   previously dequeued.

                            +-+
                          +>|1|
                          | +-+
                          |  |
                          | +-+               +-+
                          | |1|             +>|3|
                          | +-+             | +-+
                          |  |              |  |
                          | +-+      +-+    | +-+
                          | |1|    +>|2|    | |3|
                          | +-+    | +-+    | +-+
                          |  A     |  A     |  A
                          |  |     |  |     |  |
                         ++--++   ++--++   ++--++
                      +->| Q  |-->| Q  |-->| Q  |--+
                      |  +----+   +----+   +----+  |
                      +----------------------------+

                       Figure 1: Round Robin Queues




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   If a hash is used as a classifier, the modulus of the hash might be
   used as an array index, selecting the sub-queue that the packet will
   go into.  One can imagine other classifiers, such as using a DSCP
   value as an index into an array containing the queue number for a
   flow, or more complex access list implementations.

   In any event, a sub-queue contains the traffic for a flow, and data
   is sent from each sub-queue in succession.

2.2.3.  Calendar Queue Models

   Another class of implementation approaches, generically referred to
   as "Weighted Fair Queues" or "Calendar Queue Implementations",
   implements the structure of the queue as an array or ring of queues
   (often called "buckets") associated with time or sequence; Each
   bucket contains the set of packets, which may be null, intended to be
   sent at a certain time or following the emptying of the previous
   bucket.  The queue structure includes a look-aside table that
   indicates the current depth (which is to say, the next bucket) of any
   given class of traffic, which might similarly be identified using a
   hash, a DSCP, an access list, or any other classifier.  Conceptually,
   the queues each contain zero or more packets from each class of
   traffic.  One is the queue being emptied "now"; the rest are
   associated with some time or sequence in the future.

   On enqueue, the enqueue function classifies a packet and determines
   the current depth of that class, with a view to scheduling it for
   transmission at some time or sequence in the future.  If the unit of
   scheduling is a packet and the queuing quantum is one packet per sub-
   queue, a burst of packets arrives in a given flow, and at the start
   the flow has no queued data, the first packet goes into the "next"
   queue, the second into its successor, and so on; if there was some
   data in the class, the first packet in the burst would go into the
   bucket pointed to by the look-aside table.  If the unit of scheduling
   is time, the explanation in Section 2.2.5 might be simplest to
   follow, but the bucket selected will be the bucket corresponding to a
   given transmission time in the future.  A necessary side-effect,
   memory being finite, is that there exist a finite number of "future"
   buckets.  If enough traffic arrives to cause a class to wrap, one is
   forced to drop something (tail-drop).

   On dequeue, the buckets are searched at their stated times or in
   their stated sequence, and when a bucket is identified that contains
   data, removes a specified quantum of data from it and, by extension,
   from the associated traffic classes.  A single bucket might contain
   data from a number of classes simultaneously.





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                             +-+
                           +>|1|
                           | +-+
                           |  |
                           | +-+      +-+
                           | |2|    +>|2|
                           | +-+    | +-+
                           |  |     |  |
                           | +-+    | +-+      +-+
                           | |3|    | |1|    +>|1|
                           | +-+    | +-+    | +-+
                           |  A     |  A     |  A
                           |  |     |  |     |  |
                          ++--++   ++--++   ++--++
                  "now"+->| Q  |-->| Q  |-->| Q  |-->...
                          +----+   +----+   +----+
                             A       A         A
                             |3      |2        |1
                          +++++++++++++++++++++++
                          ||||     Flow      ||||
                          +++++++++++++++++++++++

                         Figure 2: Calendar Queue

   In any event, a sub-queue contains the traffic for a point in time or
   a point in sequence, and data is sent from each sub-queue in
   succession.  If sub-queues are associated with time, an interesting
   end case develops: If the system is draining a given sub-queue, and
   the time of the next sub-queue arrives, what should the system do?
   One potentially valid line of reasoning would have it continue
   delivering the data in the present queue, on the assumption that it
   will likely trade off for time in the next.  Another potentially
   valid line of reasoning would have it discard any waiting data in the
   present queue and move to the next.

2.2.4.  Work Conserving Models and Stochastic Fairness Queuing

   McKenney's Stochastic Fairness Queuing [SFQ] is an example of a work
   conserving algorithm.  This algorithm measures packets, and considers
   a "flow" to be an equivalence class of traffic defined by a hashing
   algorithm over the source and destination IPv4 addresses.  As packets
   arrive, the enqueue function performs the indicated hash and places
   the packet into the indicated sub-queue.  The dequeue function
   operates as described in Section 2.2.2; sub-queues are inspected in
   round-robin sequence, and if they contain one or more packets, a
   packet is removed.





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   Shreedhar's Deficit Round Robin [DRR] model modifies the quanta to
   bytes, and deals with variable length packets.  A sub-queue
   descriptor contains a waiting quantum (the amount intended to be
   dequeued on the previous dequeue attempt that was not satisfied), a
   per-round quantum (the sub-queue is intended to dequeue a certain
   number of bytes each round), and a maximum to permit (some multiple
   of the MTU).  In each dequeue attempt, the dequeue method sets the
   waiting quantum to the smaller of the maximum quantum and the sum of
   the waiting and incremental quantum.  It then dequeues up to the
   waiting quantum, in bytes, of packets in the queue, and reduces the
   waiting quantum by the number of bytes dequeued.  Since packets will
   not normally be exactly the size of the quantum, some dequeue
   attempts will dequeue more than others, but they will over time
   average the incremental quantum per round if there is data present.

   McKenny or Shreedhar's models could be implemented as described in
   Section 2.2.3.  The weakness of a WRR approach is the search time
   expended when the queuing system is relatively empty, which the
   calendar queue model obviates.

2.2.5.  Non Work Conserving Models and Virtual Clock

   Zhang's Virtual Clock [VirtualClock] is an example of a non-work-
   conserving algorithm.  It is trivially implemented as described in
   Section 2.2.3.  It associates buckets with intervals in time, with
   durations on the order of microseconds to tens of milliseconds.  Each
   flow is assigned a rate in bytes per interval.  The flow entry
   maintains a point in time the "next" packet in the flow should be
   scheduled.

   On enqueue, the method determines whether the "next schedule" time is
   "in the past"; if so, the packet is scheduled "now", and if not, the
   packet is scheduled at that time.  It then calculates the new "next
   schedule" time, as the current "next schedule" time plus the length
   of the packet divided by the rate; if the resulting time is also in
   the past, the "next schedule" time is set to "now", and otherwise to
   the calculated time.  As noted in Section 2.2.3, there is an
   interesting point regarding "too much time in the future"; if a
   packet is scheduled too far into the future, it may be marked or
   dropped in the AQM procedure, and if it runs beyond the end of the
   queuing system, may be defensively tail dropped.

   On dequeue, the bucket associated with the time "now" is inspected.
   If it contains a packet, the packet is dequeued and transmitted.  If
   the bucket is empty and the time for the next bucket has not arrived,
   the system waits, even if there is a packet in the next bucket.  As
   noted in Section 2.2.3, there is an interesting point regarding the
   queue associated with "now".  If a subsequent bucket, even if it is



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   actually empty, would be delayed by the transmission of a packet, one
   could imagine marking the packet ECN CE [RFC3168] [RFC6679] or
   dropping the packet.

3.  Queuing, Marking, and Dropping

   Queuing, marking, and dropping are integrated in any system that has
   a queue.  If nothing else, as memory is finite, a system has to drop
   as discussed in Section 2.2.3 and Section 2.2.5 in order to protect
   itself.  However, host transports interpret drops as signals, so AQM
   algorithms use that as a mechanism to signal.

   It is useful to think of the effects of queuing as a signal as well.
   In TCP, SCTP, and protocols like them, delay experienced by a packet
   can be used to guess the rate available at a given time on a path
   even though the characteristics of the path and competing traffic
   remain unknown [PacketPair].  The mathematical side of that is that
   if two packets were sent at the same time, the ratio of the size of
   the second packet divided by the difference in arrival times of the
   two packets cannot exceed the capacity of the link (although it may
   well be lower).  From an engineering perspective, the receiver sends
   acknowledgements as data is received, so the arrival of
   acknowledgements at the sender paces the sender at approximately the
   average rate it is able to achieve through the network.  This is true
   even if the sender keeps an arbitrarily large amount of data stored
   in network queues, and is the basis for delay-based congestion
   control algorithms.  So, delaying a packet momentarily in order to
   permit another session to improve its operation has the effect of
   signaling a slightly lower capacity to the sender.

3.1.  Queuing with Tail Mark/Drop

   In the default case, in which a FIFO queue is used with defensive
   tail-drop only, the effect is therefore to signal to the sender in
   two ways:

   o  Ack Clocking, pacing the sender to send at approximately the rate
      it can deliver data to the receiver, and

   o  Defensive loss, when a sender sends faster than available capacity
      (such as by probing network capacity when fully utilizing that
      capacity) and overburdens a queue.

3.2.  Queuing with CoDel Mark/Drop

   In any case wherein a queuing algorithm is used along with CoDel
   [I-D.nichols-tsvwg-codel], the sequence of events is that a packet is
   time-stamped, enqueued, dequeued, compared to a subsequent reading of



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   the clock, and then acted on, whether by dropping it, marking and
   forwarding it, or simply forwarding it.  This is to say that the only
   drop algorithm inherent in queuing is the defensive drop when the
   queue's resources are overrun.  However, the intention of marking or
   dropping is to signal to the sender much earlier, when a certain
   amount of delay has been observed,. The CoDel algorithm is completely
   separate from the queuing algorithm.  Hence, in a FIFO+CoDel,
   SFQ+CoDel, or Virtual Clock+CoDel implementation, the queuing
   algorithm is completely separate from the AQM algorithm.  Using them
   in series results in four signals to the sender:

   o  Ack Clocking, pacing the sender to send at approximately the rate
      it can deliver data to the receiver through a queue,

   o  Lossless signaling that a certain delay threshold has been
      reached, if ECN [RFC3168][RFC6679] is in use,

   o  Intentional signaling via loss that a certain delay threshold has
      been reached, if ECN is not in use, and

   o  Defensive loss, when a sender sends faster than available capacity
      (such as by probing network capacity when fully utilizing that
      capacity) and overburdens a queue.

3.3.  Queuing with PIE Mark/Drop

   In any case wherein a queuing algorithm is used along with PIE
   [I-D.pan-tsvwg-pie], RED, or other such algorithms, the sequence of
   events is that a queue is inspected, a packet is dropped, marked, or
   left unchanged, enqueued, dequeued, compared to a subsequent reading
   of the clock, and then forwarded on.  This is to say that the AQM
   Mark/Drop Algorithm precedes enqueue; if it has not been effective
   and as a result the queue is out of resources anyway, the defensive
   drop algorithm steps in, and failing that, the queue operates in
   whatever way it does.  Hence, in a FIFO+PIE, SFQ+PIE, or Virtual
   Clock+PIE implementation, the queuing algorithm is again completely
   separate from the AQM algorithm.  Using them in series results in
   four signals to the sender:

   o  Ack Clocking, pacing the sender to send at approximately the rate
      it can deliver data to the receiver through a queue,

   o  Lossless signaling that a queue depth that corresponds to a
      certain delay threshold has been reached, if ECN is in use,

   o  Intentional signaling via loss that a queue depth that corresponds
      to a certain delay threshold has been reached, if ECN is not in
      use, and



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   o  Defensive loss, when a sender sends faster than available capacity
      (such as by probing network capacity when fully utilizing that
      capacity) and overburdens a queue.

4.  Conclusion

   To summarize, in Section 2, implementation approaches for several
   classes of queueing algorithms were explored.  Queuing algorithms
   such as SFQ, Virtual Clock, and FlowQueue-Codel
   [I-D.hoeiland-joergensen-aqm-fq-codel] have value in the network, in
   that they delay packets to enforce a rate upper bound or to permit
   competing flows to compete more effectively.  ECN Marking and loss
   are also useful signals if used in a manner that enhances TCP/SCTP
   operation or restrains unmanaged UDP data flows.

   It is, however, incorrect to discuss a scheduler and a mark/drop
   algorithm working together as a single algorithm, even if they are
   coded that way and even if there might be optimizations that can be
   done between the two.  Conceptually, they operate in series, as
   discussed in Section 3.  The observed effects also differ; while
   defensive loss protects the intermediate system and provides a
   signal, AQM mark/drop works to reduce mean latency, and the
   scheduling of flows works to modify flow interleave and
   acknowledgement pacing.  Certain features like flow isolation are
   provided by fair queueing related designs, not the effect of the mark
   /drop algorithm.

5.  IANA Considerations

   This memo asks the IANA for no new parameters.

6.  Security Considerations

   This memo adds no new security issues; it observes on implementation
   strategies for Diffserv implementation.

7.  Acknowledgements

   This note grew out of, and is in response to, mailing list
   discussions in AQM, in which some have pushed an algorithm the
   compare to AQM marking and dropping algorithms, but which includes
   SFQ.  The authors think highly of queuing algorithms that can ensure
   certain behaviors, but in this context believe that coupling queuing
   and marking or dropping is unwarranted and masks issues with the mark
   /drop algorithm in question.






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8.  References

8.1.  Normative References

   [RFC2475]  Blake, S., Black, D., Carlson, M., Davies, E., Wang, Z.,
              and W. Weiss, "An Architecture for Differentiated
              Services", RFC 2475, December 1998.

8.2.  Informative References

   [DRR]      Microsoft Corporation and Washington University in St.
              Louis, "Efficient fair queueing using deficit round
              robin", ACM SIGCOMM 1995, October 1995,
              <http://ieeexplore.ieee.org/stamp/
              stamp.jsp?tp=&arnumber=502236>.

   [GPS]      Xerox PARC, University of California, Berkeley, and Xerox
              PARC, "Analysis and simulation of a fair queueing
              algorithm", ACM SIGCOMM 1989, September 1989,
              <http://blizzard.cs.uwaterloo.ca/keshav/home/Papers/data/
              89/fq.pdf>.

   [I-D.hoeiland-joergensen-aqm-fq-codel]
              Hoeiland-Joergensen, T., McKenney, P., Taht, D., Gettys,
              J., and E. Dumazet, "FlowQueue-Codel", draft-hoeiland-
              joergensen-aqm-fq-codel-00 (work in progress), March 2014.

   [I-D.nichols-tsvwg-codel]
              Nichols, K. and V. Jacobson, "Controlled Delay Active
              Queue Management", draft-nichols-tsvwg-codel-01 (work in
              progress), February 2013.

   [I-D.pan-tsvwg-pie]
              Pan, R., Natarajan, P., Piglione, C., and M. Prabhu, "PIE:
              A Lightweight Control Scheme To Address the Bufferbloat
              Problem", draft-pan-tsvwg-pie-00 (work in progress),
              December 2012.

   [PacketPair]
              University of California Berkeley, "Congestion Control in
              Computer Networks", UC Berkeley TR-654 1991, September
              1991, <http://blizzard.cs.uwaterloo.ca/keshav/home/Papers/
              data/91/ch4.pdf>.

   [RFC0970]  Nagle, J., "On packet switches with infinite storage", RFC
              970, December 1985.





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   [RFC2990]  Huston, G., "Next Steps for the IP QoS Architecture", RFC
              2990, November 2000.

   [RFC3168]  Ramakrishnan, K., Floyd, S., and D. Black, "The Addition
              of Explicit Congestion Notification (ECN) to IP", RFC
              3168, September 2001.

   [RFC6057]  Bastian, C., Klieber, T., Livingood, J., Mills, J., and R.
              Woundy, "Comcast's Protocol-Agnostic Congestion Management
              System", RFC 6057, December 2010.

   [RFC6679]  Westerlund, M., Johansson, I., Perkins, C., O'Hanlon, P.,
              and K. Carlberg, "Explicit Congestion Notification (ECN)
              for RTP over UDP", RFC 6679, August 2012.

   [SFQ]      SRI International, "Stochastic Fairness Queuing", IEEE
              Infocom 1990, June 1990, <http://www2.rdrop.com/~paulmck/
              scalability/paper/sfq.2002.06.04.pdf>.

   [VirtualClock]
              Xerox PARC, "Virtual Clock", ACM SIGCOMM 1990, September
              1990,
              <http://www.cs.ucla.edu/~lixia/papers/90sigcomm.pdf>.

Appendix A.  Change Log

   Initial Version:  June 2014

Authors' Addresses

   Fred Baker
   Cisco Systems
   Santa Barbara, California  93117
   USA

   Email: fred@cisco.com


   Rong Pan
   Cisco Systems
   Milpitas, California  95035
   USA

   Email: ropan@cisco.com







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