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Active Queue Management                         Wolfram Lautenschlaeger
and Packet Scheduling (aqm)                                       Nokia
Internet Draft                                                Bell Labs
Intended status:
Expires: November 2016       May 25, 2016



            Global Synchronization Protection for Packet Queues
                        draft-lauten-aqm-gsp-03.txt


Abstract

   The congestion avoidance processes of transmission capacity sharing
   TCP flows tend to be synchronized among each other, so that the rate
   variations of the individual flows do not compensate. In contrary,
   they accumulate into large variations of the whole aggregate. The
   effect is known as global synchronization. Large queuing buffer
   demand and large latency and jitter are the consequences. Global
   Synchronization Protection (GSP) is an extension of regular tail drop
   packet queuing schemes that prevents global synchronization. For
   large traffic aggregates the de-correlation between the individual
   flow variations reduces buffer demand and packet sojourn time by an
   order of magnitude and more. Even though quite simple, the solution
   has a theoretical background and is not heuristic. It has been tested
   with a Linux kernel implementation and shows equivalent performance
   as other relevant AQM schemes.

Status of this Memo

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   The list of Internet-Draft Shadow Directories can be accessed at
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   This Internet-Draft will expire on November 25, 2016.

Copyright Notice

   Copyright (c) 2016 IETF Trust and the persons identified as the
   document authors. All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
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   to this document.



Table of Contents

   1. Introduction...................................................2
   2. Conventions used in this document..............................4
   3. Root cause of global synchronization...........................4
   4. Protecting queues of global synchronization....................5
      4.1. Basic algorithm...........................................5
      4.2. Interval adaptation at large flow numbers.................5
      4.3. Interval adaptation at small RTT..........................7
      4.4. Sanity checks and special cases...........................7
   5. Delay based operation..........................................8
      5.1. Queue delay vs. queue size................................8
      5.2. Delay based GSP...........................................8
   6. Security Considerations........................................9
   7. IANA Considerations............................................9
   8. Conclusions....................................................9
   9. References.....................................................9
      9.1. Normative References......................................9
      9.2. Informative References....................................9
   10. Acknowledgments..............................................10

1. Introduction

   The congestion window (CWND) of a particular TCP connection, in
   combination with the round trip time (RTT), limits the transmission
   rate of the flow, which enables adaptation of the sending rate to the
   actual network conditions, [1]. TCP uses a rather coarse congestion
   control feedback by halving the congestion window in response to


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   packet loss. To fill a bottleneck link by 100% anyway, a packet
   buffer in front of the link is required. For a single TCP flow a
   buffer in the range of bottleneck capacity multiplied by the round
   trip time is required (bandwidth-delay product rule, BDP), [2]. For
   aggregated traffic of many flows the picture is not so clear.
   Conservative estimations tend towards BDP of the whole aggregate,
   i.e. link capacity * RTT. At the other hand, rate reductions due to
   CWND halving are still only in the range of a particular flow rate.
   With the assumption of N sharing flows, this yields ideally a buffer
   size of only (link capacity/N)*RTT. Unfortunately this value cannot
   be reached in practice. It would require a uniform distribution of
   rate reductions by the different flows over time. In opposite, rate
   reductions of bottleneck sharing flows tend to synchronize among each
   other, which is called global synchronization. In worst case, with
   all flows synchronized, the buffer demand is back at BDP of the whole
   traffic, thus confirming the conservative estimation.

   There are cases where global synchronization does not occur, in
   particular large number of flows (N>500), large spread of RTT between
   the different flows, and high frequency of flow renewals. In these
   cases the buffer size can be reduced to BDP/sqrt(N), which lies
   between the conservative and overly optimistic estimations above,[3].
   Nevertheless there are still doubts, whether the absence of global
   synchronization is a general reliable design assumption for high
   capacity links, [4].

   Most Active Queue Management (AQM) algorithms are aiming at better
   control of the queue size (RED [5]) or the queue delay (CoDel [6],
   PIE [7]), which implies control over global synchronization. Global
   Synchronization Protection (GSP [10]) goes the other way round. It
   suppresses the root cause of global synchronization and de-correlates
   the CWND variations of the competing flows, but it does not try to
   impact the behavior of a particular flow. This way it moves the
   buffer size demand down from conservative BDP of the whole link into
   the direction towards the ideal BDP of only one of the competing
   flows.

   Experiments on GSP performance, as reported in [10], show that the
   stabilizing effect of GSP is equivalent to that of the other AQMs. It
   is a simple extension to plain tail drop queues. The basic algorithm
   is memoryless and does not need artificial randomization.
   Particularly for small numbers of flows it performs better than
   randomized AQMs like e.g. RED or PIE. Without any special precautions
   GSP is robust to bursts and never drops at low or empty queues.
   Automatic parameter adaptation, if needed at all, is external to the
   basic control loop, which makes it less time critical and less



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   sensitive to traffic conditions than other AQMs with integrated
   control and adaptation loops.

2. Conventions used in this document

   In this document, the term "packet drop" is used for congestion
   notification, silently assuming that congestion marking for ECN could
   be equally applied.

   In this document, the term "queue size" is preferably applied in
   number of bytes, however, the algorithm could be also applied to the
   number of packets, or even to the queuing delay (milliseconds).

3. Root cause of global synchronization

   Global synchronization occurs in cases where a number of greedy TCP
   flows with comparably uniform RTT cross a tail drop queue in front of
   a shared transmission link. Greedy TCP means, the flow is probing the
   available capacity on this particular link and is not limited
   elsewhere (up- or downstream). Tail drop means, a newly arriving
   packet is placed at the end of the queue if buffer space permits.
   Otherwise it is dropped. The queue is drained from head of the queue
   at the speed of the link as long as packets are available.

   In congestion avoidance state, all senders gradually increase their
   sending rate, which is, after a while, exceeding the link capacity so
   that the queue in front of the link is filling up. At some point in
   time, a first packet is dropped due to lack of buffer space. Ideally,
   the TCP flow, where the dropped packet belongs to, reduces its
   sending rate, the queue relaxes, its size goes down, and subsequently
   arriving packets again can be placed in the buffer. Senders continue
   to increase their sending rates until the next drop, and so on.

   Unfortunately not one, but several packets get dropped in such
   incident for following reason: The rate reduction due to the first
   dropped packet needs at least one RTT to take effect at the queue
   entry. During that RTT interval all senders continue to gradually
   increase their sending rates, whereas the queue is still full.
   Further packets need to be dropped. It can be shown analytically that
   for N flows with NewReno and delayed ACK the number of drops is in
   the range of N/2. Experiments confirm this and show an even higher
   number with CUBIC. The outcome is that even though the rate reduction
   by one flow would suffice, not one, but as much as half of the flows
   are triggered within one RTT to reduce their sending rates - we have
   global synchronization. A more detailed analytical and experimental
   investigation of the effect can be found in [8].



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4. Protecting queues of global synchronization

       4.1. Basic algorithm

   The basic algorithm works as follows: Set a threshold on queue size
   below the actual buffer size. If a new packet arrives and the queue
   size is above the threshold, then immediately drop that packet. After
   that, ignore any further threshold violation for a timeout interval
   of 1 - 3 RTT. After expiry of the timeout proceed as above.

   Algorithm:

   initialization:
       interval = e.g. 2 * RTT
       threshold = e.g. 1/2 * buffer size
       timeout_expiry = now(), with now() returning the current time

   at any packet arrival do:
       if queue_size > threshold && now() > timout_expiry:
           drop this packet
           timeout_expiry = now() + interval
       else
           enqueue this packet

   The first dropped packet is triggering the rate reduction by one of
   the end points. During the timeout the queue is growing further
   beyond the threshold until the rate reduction takes effect at queue
   entry. Afterwards the queue size should have dropped below the
   threshold, so that at timeout expiry the threshold is typically not
   violated anymore. No explicit action occurs at timeout expiry, which
   makes the parameter rather insensitive to the actual traffic
   characteristics. Even if the timeout interval is too short, the
   algorithm still reduces global synchronization.

       4.2. Interval adaptation at large flow numbers

   The basic algorithm works well for moderate numbers of flows N, i.e.
   in a range of 1 < N < 20. More precisely, at flow numbers N smaller
   than the average CWND of one of the sharing flows. At larger numbers
   the total rate increase during the timeout interval is larger than
   the subsequent rate reduction by one of the flows. As consequence,
   after timeout expiry the threshold is still violated, the queue is
   growing further and further, and, eventually, reaches the buffer
   limit and enters tail drop operation. The performance is still better



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   than plain tail drop and one could rely on the observation that at
   large flow numbers global synchronization disappears, anyway.

   Alternatively the initial timeout interval can be reduced, depending
   on the actual traffic, in a way, where not just once, but twice, or
   even more times per RTT the timeout expires. The adaptation criterion
   is the proportion of time above and below threshold. In regular
   operation according to the basic algorithm, the queue is most of the
   time below the threshold. If, however, the queue is more frequently
   above than below threshold, the interval should be reduced until
   equilibrium is reached. In this condition the queue is oscillating
   around the threshold, periodically dropping during times above the
   threshold, quite similar like PED [9].

   Algorithm:

   initialization:
          tau = a preset parameter controlling the adaptation speed;
                e.g. 500 milliseconds or 5 * initial_interval
          initial_interval - the preset timeout interval as in the basic
              algorithm
          cumTime = 0; the cumulative time above/below threshold
          state = CLEAR; the recent overflow state of the queue

      at any packet arrival do:
          if the packet would overflow the buffer (hard tail drop):
              state = OVERFLOW
          if state == OVERFLOW && queue is empty:
              state = DRAIN
          if state == DRAIN && queue_size > threshold:
              state = CLEAR

          update the cumulative time cumTime:
              account by twice the duration for queue episodes
                  that are entirely ABOVE the threshold
              if status == CLEAR:
                  account by negated duration for queue episodes
                      that are entirely BELOW the threshold

          clamp the cumulative time:
              cumTime = max(cumTime, 0)
              cumTime = min(cumTime, some sanity limit
                            of several minutes)


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          calculate timeout interval (to be used at next drop decision):
              interval = initial_interval/(1+cumTime/tau)

   The adaptation heuristics works as follows: The basic GSP algorithm
   executes single drops per threshold violation as long as the
   cumulative time (cumTime) is clamped at zero. As soon as the
   cumulative time gets positive, the adaptation algorithm implements an
   integral controller for the drop rate that the basic GSP algorithm
   executes during times of threshold violation. The transition between
   both operating conditions is smooth. The adaptation speed is
   controlled by the parameter tau.

   Plain adaptation by cumulative times above / below threshold might
   latch up in circumstances where abrupt traffic changes cause massive
   buffer overflows. To avoid this, after a hard buffer overflow the
   accounting for times below threshold is suspended until the queue
   performed a full cycle of down to empty and back above threshold.

       4.3. Interval adaptation at small RTT

   The RTT is not known exactly but there should be at least a rough
   idea on the range of RTT for setting up the timeout interval. If this
   estimation is much too large, a similar situation occurs like in the
   large flow numbers case. The total rate increase during the timeout
   interval (which turns out to be multiple RTTs) is larger than the
   subsequent rate reduction by one flow. The adaptation rule is the
   same as for large flow numbers, section 4.2.

       4.4. Sanity checks and special cases

   An additional rule can be introduced that prevents large packet
   bursts from immediately triggering the drop: Restart the timeout not
   only after a packet drop but also whenever a packet is arriving at an
   empty queue.

   at any packet arrival do:
       if queue is empty:
           timeout_expiry = now() + interval









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5. Delay based operation

       5.1. Queue delay vs. queue size

   Recent new AQM proposals ([6], [7]) are focusing on queue delay
   rather than on queue size in bytes. One reason for this move is that
   ideally the steady state queue oscillation depends only on the RTT
   and on the number of sharing TCP flows - if measured in delay. If
   measured in bytes, however, the queue oscillation additionally
   depends on the link capacity. The oscillation span sets the minimum
   queue size for 100% link utilization. (This is where the bandwidth
   delay product rule comes from.) A larger queue creates only
   unnecessary delay (standing queue).

   Obviously it is preferable to stabilize the delay instead of the
   size. It eliminates the interface rate from the parameter list, which
   is particularly welcome in circumstances with unknown or variable
   drain rate. Such situations are typical for low priority queues in
   front of a priority scheduler and generally in wireless scenarios.

       5.2. Delay based GSP

   In section 4. we silently assumed queue size in bytes. However, the
   algorithm can be equally applied to the queue delay (packet sojourn
   time). In this case the threshold has to be in milliseconds, whereas
   the empty queue condition remains the same as before.

   While the queue size in bytes or packets is typically maintained by
   ordinary queue implementations, obtaining the queue delay requires
   additional effort. Two solutions are available and both are
   applicable to GSP: Time stamping of packets like in CoDel [6] or
   estimating the drain rate for a translation of size into delay like
   in PIE [7].

   Algorithm (time stamping):

   at any arrival of a packet p do:
       p.time = now()

   at any departure of a packet p do:
       queue_delay = now() - p.time



   The basic algorithm of section 4.1. rephrased to delay based
   operation:



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   at any packet arrival do:
       if queue_delay > threshold && now() > timout_expiry:
           drop this packet
           timeout_expiry = now() + interval
       else
           enqueue this packet

   Please note that queue_delay is a per queue variable, not per packet,
   i.e. the drop decision at enqueuing (tail drop) depends on the delay
   that another, most recently dequeued packet experienced. This
   approach is a forecast of the expected delay and it is justified by
   the inherent inertance of the queue itself.

6. Security Considerations

   Global synchronization is a particular problem of many elastic flows
   sharing a bottleneck. GSP is there to prevent this. But it does not
   protect of unresponsive flows. If the congestion notification
   according to section 4.1. randomly hits an unresponsive flow then the
   expected rate reduction within the timeout interval might simply not
   happen, which postpones the notification by one timeout interval. The
   interval adaptation of section 4.2. automatically ensures that the
   timeout interval is reduced accordingly, depending on the average
   fraction of unresponsive traffic. In extreme cases, when the
   unresponsive traffic alone is exceeding the link capacity, GSP
   behaves like plain tail drop.

7. IANA Considerations

   There are no actions for IANA.

8. Conclusions

   tbc

9. References

       9.1. Normative References



       9.2. Informative References

   [1]   Van Jacobson, Congestion avoidance and control, Proc. SIGCOMM
         '88, 1988



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   [2]   M. Mathis, J. Semke, J. Mahdavi, and T. Ott, The Macroscopic
         Behavior of the TCP Congestion Avoidance Algorithm, Comput.
         Commun. Rev., 27.3, 1997, pp. 67-82.

   [3]   G. Appenzeller, I. Keslassy, and N. McKeown, Sizing router
         buffers, Proc. ACM SIGCOMM '04, 2004.

   [4]   G. Vu-Brugier, R. S. Stanojevic, D. J. Leith, R. N. Shorten, A
         critique of recently proposed buffer-sizing strategies, ACM
         SIGCOMM Computer Communication Review, 37.1, 2007

   [5]   S. Floyd, Van Jacobsen, Random Early Detection Gateways for
         Congestion Avoidance, IEEE/ACM Trans. Networking, 1.4, 1993

   [6]   K. Nichols, Van Jacobson, "Controlling Queue Delay", ACM Queue
         - Networks, 2012

   [7]   R. Pan, P. Natarajan, C. Piglione, M.S. Prabhu, V. Subramanian,
         F. Baker, B. VerSteeg, PIE: A lightweight control scheme to
         address the bufferbloat problem, 14th High Performance
         Switching and Routing (HPSR), 2013 IEEE

   [8]   W. Lautenschlaeger, A deterministic TCP bandwidth sharing
         model, 2014, online http://arxiv.org/pdf/1404.4173v1

   [9]   A. Francini, Beyond RED: Periodic Early Detection for On-Chip
         Buffer Memories in Network Elements, Proc. IEEE High-
         Performance Switching and Routing Conference (HPSR 2011),
         Cartagena, Spain, July 4-6, 2011

   [10]  W. Lautenschlaeger, A. Francini, Global Synchronization
         Protection for Bandwidth Sharing TCP Flows in High-Speed Links,
         IEEE HPSR 2015, Budapest, Hungary, July 2015, online
         http://arxiv.org/pdf/1602.05333



10. Acknowledgments

   This document was prepared using 2-Word-v2.0.template.dot.









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Authors' Addresses

   Wolfram Lautenschlaeger
   Alcatel-Lucent Deutschland AG
   Bell Labs
   Lorenzstrasse 10
   70435 Stuttgart
   Germany

   Email: Wolfram.Lautenschlaeger@nokia.com





































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