[Docs] [txt|pdf|xml|html] [Tracker] [WG] [Email] [Diff1] [Diff2] [Nits]

Versions: 00 01 02 draft-ietf-aqm-eval-guidelines

Internet Engineering Task Force                             N. Kuhn, Ed.
Internet-Draft                                          Telecom Bretagne
Intended status: Informational                         P. Natarajan, Ed.
Expires: February 10, 2015                                 Cisco Systems
                                                                  D. Ros
                                           Simula Research Laboratory AS
                                                              N. Khademi
                                                      University of Oslo
                                                         August 11, 2014

                    AQM Characterization Guidelines
                   draft-kuhn-aqm-eval-guidelines-02

Abstract

   Unmanaged large buffers in today's networks have given rise to a slew
   of performance issues.  These performance issues can be addressed by
   some form of Active Queue Management (AQM), optionally in combination
   with a packet scheduling scheme such as fair queuing.  The IETF AQM
   and packet scheduling working group was formed to standardize AQM
   schemes that are robust, easily implemented, and successfully
   deployed in today's networks.  This document describes various
   criteria for performing precautionary characterizations of AQM
   proposals.  This document also helps in ascertaining whether any
   given AQM proposal should be taken up for standardization by the AQM
   WG.

Status of this Memo

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

   Internet-Drafts are working documents of the Internet Engineering
   Task Force (IETF).  Note that other groups may also distribute
   working documents as Internet-Drafts.  The list of current Internet-
   Drafts is at http://datatracker.ietf.org/drafts/current/.

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

   This Internet-Draft will expire on February 10, 2015.

Copyright Notice

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





Kuhn, Natarajan, Ros & Expires February 10, 2015                [Page 1]


Internet-Draft      AQM Characterization Guidelines          August 2014


   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents (http://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
     1.1.  Guidelines for AQM designers . . . . . . . . . . . . . . .  4
     1.2.  Reducing the latency and maximizing the goodput  . . . . .  5
     1.3.  Glossary . . . . . . . . . . . . . . . . . . . . . . . . .  5
     1.4.  Requirements Language  . . . . . . . . . . . . . . . . . .  5
   2.  End-to-end metrics . . . . . . . . . . . . . . . . . . . . . .  5
     2.1.  Flow Completion time . . . . . . . . . . . . . . . . . . .  6
     2.2.  Packet loss  . . . . . . . . . . . . . . . . . . . . . . .  6
     2.3.  Packet loss synchronization  . . . . . . . . . . . . . . .  6
     2.4.  Goodput  . . . . . . . . . . . . . . . . . . . . . . . . .  6
     2.5.  Latency and jitter . . . . . . . . . . . . . . . . . . . .  7
     2.6.  Discussion on the trade-off between latency and goodput  .  7
   3.  Generic set up for evaluations . . . . . . . . . . . . . . . .  7
     3.1.  Topology and notations . . . . . . . . . . . . . . . . . .  8
     3.2.  Buffer size  . . . . . . . . . . . . . . . . . . . . . . .  9
     3.3.  Congestion controls  . . . . . . . . . . . . . . . . . . .  9
   4.  Various TCP variants . . . . . . . . . . . . . . . . . . . . .  9
     4.1.  TCP-friendly Sender  . . . . . . . . . . . . . . . . . . . 10
     4.2.  Aggressive Transport Sender  . . . . . . . . . . . . . . . 10
     4.3.  Unresponsive Transport Sender  . . . . . . . . . . . . . . 10
     4.4.  TCP initial congestion window  . . . . . . . . . . . . . . 10
     4.5.  Traffic Mix  . . . . . . . . . . . . . . . . . . . . . . . 11
   5.  RTT fairness . . . . . . . . . . . . . . . . . . . . . . . . . 12
     5.1.  Motivation . . . . . . . . . . . . . . . . . . . . . . . . 12
     5.2.  Required tests . . . . . . . . . . . . . . . . . . . . . . 12
     5.3.  Metrics to evaluate the RTT fairness . . . . . . . . . . . 13
   6.  Burst absorption . . . . . . . . . . . . . . . . . . . . . . . 13
     6.1.  Motivation . . . . . . . . . . . . . . . . . . . . . . . . 13
     6.2.  Required tests . . . . . . . . . . . . . . . . . . . . . . 14
     6.3.  Metrics to evaluate the burst absorption capacity  . . . . 14
   7.  Stability  . . . . . . . . . . . . . . . . . . . . . . . . . . 14
     7.1.  Motivation . . . . . . . . . . . . . . . . . . . . . . . . 14
     7.2.  Required tests . . . . . . . . . . . . . . . . . . . . . . 15
       7.2.1.  Mild Congestion  . . . . . . . . . . . . . . . . . . . 15
       7.2.2.  Medium Congestion  . . . . . . . . . . . . . . . . . . 15
       7.2.3.  Heavy Congestion . . . . . . . . . . . . . . . . . . . 15
       7.2.4.  Varying Available Bandwidth  . . . . . . . . . . . . . 15
     7.3.  Parameter sensitivity and stability analysis . . . . . . . 16
   8.  Implementation cost  . . . . . . . . . . . . . . . . . . . . . 16
     8.1.  Motivation . . . . . . . . . . . . . . . . . . . . . . . . 16
     8.2.  Required discussion  . . . . . . . . . . . . . . . . . . . 17
   9.  Operator control knobs and auto-tuning . . . . . . . . . . . . 17

Kuhn, Natarajan, Ros & Expires February 10, 2015                [Page 2]


Internet-Draft      AQM Characterization Guidelines          August 2014

   10. Interaction with ECN . . . . . . . . . . . . . . . . . . . . . 18
     10.1.  Motivation  . . . . . . . . . . . . . . . . . . . . . . . 18
     10.2.  Required discussion . . . . . . . . . . . . . . . . . . . 18
   11. Interaction with scheduling  . . . . . . . . . . . . . . . . . 18
     11.1.  Motivation  . . . . . . . . . . . . . . . . . . . . . . . 18
     11.2.  Required discussion . . . . . . . . . . . . . . . . . . . 18
   12. Discussion on methodology, metrics, AQM comparisons and packet
       sizes  . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
     12.1.  Methodology . . . . . . . . . . . . . . . . . . . . . . . 18
     12.2.  Comments on metrics measurement . . . . . . . . . . . . . 19
     12.3.  Comparing AQM schemes . . . . . . . . . . . . . . . . . . 19
       12.3.1.  Performance comparison  . . . . . . . . . . . . . . . 19
       12.3.2.  Deployment comparison . . . . . . . . . . . . . . . . 20
     12.4.  Packet sizes and congestion notification  . . . . . . . . 20
   13. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 20
   14. Contributors . . . . . . . . . . . . . . . . . . . . . . . . . 20
   15. IANA Considerations  . . . . . . . . . . . . . . . . . . . . . 21
   16. Security Considerations  . . . . . . . . . . . . . . . . . . . 21
   17. References . . . . . . . . . . . . . . . . . . . . . . . . . . 21
     17.1.  Normative References  . . . . . . . . . . . . . . . . . . 21
     17.2.  Informative References  . . . . . . . . . . . . . . . . . 21
   Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 22

1.  Introduction

   Active Queue Management (AQM) addresses the concerns arising from
   using unnecessarily large and unmanaged buffers in order to improve
   network and application performance.  Several AQM algorithms have
   been proposed in the past years, most notable being Random Early
   Detection (RED), BLUE, and Proportional Integral controller (PI), and
   more recently CoDel [CODEL] and PIE [PIE].  In general, these
   algorithms actively interact with the Transmission Control Protocol
   (TCP) and any other transport protocol that deploys a congestion
   control scheme to manage the amount of data they keep in the network.
   The available buffer space in the routers and switches is large
   enough to accommodate the short-term buffering requirements.  AQM
   schemes aim at reducing mean buffer occupancy, and therefore both
   end-to-end delay and jitter.  Some of these algorithms, notably RED,
   have also been widely implemented in some network devices.  However,
   any potential benefits of the RED AQM scheme have not been realized
   since RED is reported to be usually turned off.  The main reason of
   this reluctance to use RED in today's deployments is its sensitivity
   to the operating conditions in the network and the difficulty of
   tuning its parameters.

   A buffer is a physical volume of memory in which a queue or set of
   queues are stored.  In real implementations of switches, a global
   memory is shared between the available devices: the size of the
   buffer for a given communication does not make sense, as its
   dedicated memory may vary over the time and real world buffering
   architectures are complex.  For the sake of simplicity, when speaking
   of a specific queue in this document, "buffer size" refers to the





Kuhn, Natarajan, Ros & Expires February 10, 2015                [Page 3]


Internet-Draft      AQM Characterization Guidelines          August 2014

   maximum amount of data the buffer may store, which may be measured in
   bytes or packets.  The rest of this memo therefore refers to the
   maximum queue depth as the size of the buffer for a given
   communication.

   In order to meet mostly throughput-based SLA requirements and to
   avoid packet drops, many home gateway manufacturers resort to
   increasing the available memory beyond "reasonable values".  This
   increase is also referred to as Bufferbloat [BB2011].  Deploying
   large unmanaged buffers on the Internet has lead to the increase in
   end-to-end delay, resulting in poor performance for latency sensitive
   applications such as real-time multimedia (e.g., voice, video,
   gaming, etc.). The degree to which this affects modern networking
   equipment, especially consumer-grade equipment, produces problems
   even with commonly used web services.  Active queue management is
   thus essential to control queuing delay and decrease network latency.

   The AQM and Packet Scheduling working group was recently formed
   within the TSV area to address the problems with large unmanaged
   buffers in the Internet.  Specifically, the AQM WG is tasked with
   standardizing AQM schemes that not only address concerns with such
   buffers, but also that are robust under a wide variety of operating
   conditions.  In order to ascertain whether the WG should undertake
   standardizing an AQM proposal, the WG requires guidelines for
   assessing AQM proposals.  This document provides the necessary
   characterization guidelines.

1.1.  Guidelines for AQM designers

   One of the key objectives behind formulating the guidelines is to
   help ascertain whether a specific AQM is not only better than drop-
   tail but also safe to deploy.  The guidelines help to quantify AQM
   schemes' performance in terms of latency reduction, goodput
   maximization and the trade-off between the two.  The guidelines also
   help to discuss AQM's safe deployment, including self adaptation,
   stability analysis, fairness, design/implementation complexity and
   robustness to different operating conditions.

   This memo details generic characterization scenarios that any AQM
   proposal MUST be evaluated against.  Irrespective of whether or not
   an AQM is standardized by the WG, we recommend the relevant scenarios
   and metrics discussed in this document to be considered.  This
   document presents central aspects of an AQM algorithm that MUST be
   considered whatever the context is, such as burst absorption
   capacity, RTT fairness or resilience to fluctuating network
   conditions.  These guidelines could not cover every possible aspect
   of a particular algorithm.  In addition, it is worth noting that the
   proposed criteria are not bound to a particular evaluation toolset.
   These guidelines do not present context dependent scenarios (such as






Kuhn, Natarajan, Ros & Expires February 10, 2015                [Page 4]


Internet-Draft      AQM Characterization Guidelines          August 2014

   Wi-Fi, data-centers or rural broadband).

   This document details how an AQM designer can rate the feasibility of
   their proposal in different types of network devices (switches,
   routers, firewalls, hosts, drivers, etc.) where an AQM may be
   implemented.

1.2.  Reducing the latency and maximizing the goodput

   The trade-off between reducing the latency and maximizing the goodput
   is intrinsically linked to each AQM scheme and is key to evaluating
   its performance.  This trade-off MUST be considered in various
   scenarios to ensure the safety of an AQM deployment.  Whenever
   possible, solutions should aim at both maximizing goodput and
   minimizing latency.  This document proposes guidelines that enable
   the reader to quantify (1) reduction of latency, (2) maximization of
   goodput and (3) the trade-off between the two.

   Testers SHOULD discuss in a reference document the performance of
   their proposal in terms of performance and deployment in regards with
   those of drop-tail: basically, these guidelines provide the tools to
   understand the deployment costs versus the potential gain in
   performance of the introduction of the proposed scheme.

1.3.  Glossary

   o  AQM: there may be confusion whether a scheduling scheme is added
      to an AQM or is a part of the AQM. The rest of this memo refers to
      AQM as a dropping policy that does not feature a scheduling
      scheme.

   o  buffer: a physical volume of memory in which a queue or set of
      queues are stored.

   o  buffer size: the maximum amount of data that may be stored in a
      buffer, measured in bytes or packets.

1.4.  Requirements Language

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
   document are to be interpreted as described in RFC 2119 [RFC2119].

2.  End-to-end metrics











Kuhn, Natarajan, Ros & Expires February 10, 2015                [Page 5]


Internet-Draft      AQM Characterization Guidelines          August 2014


   End-to-end delay is the result of propagation delay, serialization
   delay, service delay in a switch, medium-access delay and queuing
   delay, summed over the network elements in the path.  AQM algorithms
   may reduce the queuing delay by providing signals to the sender on
   the emergence of congestion, but any impact on the goodput must be
   carefully considered.  This section presents the metrics that SHOULD
   be used to better quantify (1) the reduction of latency, (2)
   maximization of goodput and (3) the trade-off between the two.  These
   metrics SHOULD be considered to better assess the performance of an
   AQM scheme.

2.1.  Flow Completion time

   The flow completion time is an important performance metric for the
   end user.  Considering the fact that an AQM scheme may drop packets,
   the flow completion time is directly linked to the dropping policy of
   the AQM scheme.  This metric helps to better assess the performance
   of an AQM depending on the flow size.

2.2.  Packet loss

   Packet losses, that may occur in a queue, impact on the end-to-end
   performance at the receiver's side.

   The tester MUST evaluate, at the receiver:

   o  the long term packet loss probability;

   o  the interval between consecutive losses;

   o  the packet loss pattern.

2.3.  Packet loss synchronization

   One goal of an AQM algorithm should be to help with avoiding global
   synchronization of flows going through the bottleneck buffer on which
   the AQM operates ([RFC2309]). It is therefore important to assess the
   "degree" of packet-loss synchronization between flows, with and
   without the AQM under consideration.

   As discussed e.g.  in [LOSS-SYNCH-MET-08], loss synchronization among
   flows may be quantified by several, slightly different, metrics that
   capture different aspects of the same issue.  However, in real-world
   measurements the choice of metric may be imposed by practical
   considerations (e.g., is there fine-grained information on packet
   losses in the bottleneck available or not). For the purpose of AQM
   characterization, a good candidate metric is the global
   synchronization ratio, measuring the proportion of flows losing
   packets during a loss event.  [YU06] used this metric in real-world
   experiments to characterize synchronization along arbitrary Internet
   paths; the full methodology is described in [YU06].

2.4.  Goodput

Kuhn, Natarajan, Ros & Expires February 10, 2015                [Page 6]


Internet-Draft      AQM Characterization Guidelines          August 2014


   Measuring the goodput enables an end-to-end appreciation of how well
   the AQM improves transport and application performance.  The measured
   end-to-end goodput is linked to the AQM scheme's dropping policy --
   the smaller the packet drops, the fewer packets need retransmission,
   minimizing AQM's impact on transport and application performance.
   Additionally, an AQM scheme may resort to Explicit Congestion
   Notification (ECN) marking as an initial means to control delay.
   Again, marking packets instead of dropping them reduces number of
   packet retransmissions and increases goodput.  Overall, end-to-end
   goodput values help evaluate the AQM scheme's effectiveness in
   minimizing packet drops that impact application performance and
   estimate how well the AQM scheme works with ECN.

2.5.  Latency and jitter

   The end-to-end latency differs from the queuing delay: it is linked
   to the network topology and the path characteristics.  Moreover, the
   jitter strongly depends on the traffic and the topology as well.  The
   introduction of an AQM scheme would impact on these metrics and the
   end-to-end evaluation of performance SHOULD consider them to better
   assess the AQM schemes.

   The guidelines advice that the tester SHOULD determine the minimum,
   average and maximum measurements for these metrics  and the
   coefficient of variation for their average values as well.

2.6.  Discussion on the trade-off between latency and goodput

   The metrics presented in this section MAY be considered, in order to
   discuss and quantify the trade-off between latency and goodput.

   This trade-off can also be illustrated with figures following the
   recommendations of the section 5 of [TCPEVAL2013].

   The end-to-end trade-off MUST be considered:

   o  end-to-end delay vs.  goodput: the x-axis shows the average end-
      to-end delay and the y-axis the average goodput;

   o  drop rate vs.  end-to-end delay: the x-axis shows the end-to-end
      delay and the y-axis the drop rate.

   This pair of graphs provide part of a better understanding (1) of the
   delay/goodput/drop-rate trade-off for a given congestion control
   mechanism, and (2) of how the goodput and average queue size vary as
   a function of the traffic load.

3.  Generic set up for evaluations

   This section presents the topology that can be used for each of the
   following scenarios, the corresponding notations and discuss various
   assumptions that have been made in the document.


Kuhn, Natarajan, Ros & Expires February 10, 2015                [Page 7]


Internet-Draft      AQM Characterization Guidelines          August 2014


3.1.  Topology and notations


       +---------+                                        +-----------+
       |senders A|                                        |receivers B|
       +---------+                                        +-----------+

       +--------------+                                +--------------+
       |traffic class1|                                |traffic class1|
       |--------------|                                |--------------|
       | SEN.Flow1.1 +---------+            +-----------+ REC.Flow1.1 |
       |        +     |        |            |          |        +     |
       |        |     |        |            |          |        |     |
       |        +     |        |            |          |        +     |
       | SEN.Flow1.X +-----+   |            |  +--------+ REC.Flow1.X |
       +--------------+    |   |            |  |       +--------------+
            +            +-+---+---+     +--+--+---+            +
            |            |Router L |     |Router R |            |
            |            |---------|     |---------|            |
            |            | AQM     |     |         |            |
            |            | BuffSize|     |         |            |
            |            | (Bsize) +-----+         |            |
            |            +-----+--++     ++-+------+            |
            +                  |  |       | |                   +
       +--------------+        |  |       | |          +--------------+
       |traffic classN|        |  |       | |          |traffic classN|
       |--------------|        |  |       | |          |--------------|
       | SEN.FlowN.1 +---------+  |       | +-----------+ REC.FlowN.1 |
       |        +     |           |       |            |        +     |
       |        |     |           |       |            |        |     |
       |        +     |           |       |            |        +     |
       | SEN.FlowN.Y +------------+       +-------------+ REC.FlowN.Y |
       +--------------+                                +--------------+


   Figure 1 is a generic topology where:

   o  various classes of traffic can be introduced;

   o  the timing of each flow (i.e., when does each flow start and stop)
      may be different;

   o  each class of traffic can consider various number of flows;

   o  each link is characterized by a couple (RTT,capacity);

   o  Flows are generated between A and B, sharing a bottleneck (Routers
      L and R);

   o  The links are supposed to be asymmetric in terms of bandwidth: the
      capacity from senders to receivers is higher than the one from



Kuhn, Natarajan, Ros & Expires February 10, 2015                [Page 8]


Internet-Draft      AQM Characterization Guidelines          August 2014

      receivers to senders.

   This topology may not perfectly reflect actual topologies, however,
   this simple topology is commonly used in the world of simulations and
   small testbeds.  This topology can be considered as adequate to
   evaluate AQM proposals, such as proposed in [TCPEVAL2013].  The
   tester should pay attention to the topology that has been used to
   evaluate the AQM scheme against which he compares his proposal.

3.2.  Buffer size

   The size of the buffers MAY be carefully set considering the
   bandwidth-delay product.  However, if the context or the application
   requires a specific buffer size, the tester MUST justify and detail
   the way the maximum queue depth is set while presenting the results
   of its evaluation.  Indeed, the size of the buffer may impact on the
   AQM performance and is a dimensioning parameter that will be
   considered for a fair comparison between AQM proposals.

3.3.  Congestion controls

   This memo features three kind of congestion controls:

   o  TCP-friendly congestion controls: a base-line congestion control
      for this category is TCP New Reno, as explained in  [RFC5681].

   o  Aggressive congestion controls: a base-line congestion control for
      this category is TCP Cubic.

   o  Less-than Best Effort (LBE) congestion controls: an LBE congestion
      control 'results in smaller bandwidth and/or delay impact on
      standard TCP than standard TCP itself, when sharing a bottleneck
      with it.'  [RFC6297]

   Recent transport layer protocols are not mentioned in the following
   sections, for the sake of simplicity.

4.  Various TCP variants

   Network and end devices need to be configured with a reasonable
   amount of buffers in order to absorb transient bursts.  In some
   situations, network providers configure devices with large buffers to
   avoid packet drops and increase goodput.  Transmission Control
   Protocol (TCP) fills up these unmanaged buffers until the TCP sender
   receives a signal (packet drop) to cut down the sending rate.  The
   larger the buffer, the higher the buffer occupancy, and therefore the
   queuing delay.  On the other hand, an efficient AQM scheme sends out
   early congestion signals to TCP senders so that the queuing delay is
   brought under control.






Kuhn, Natarajan, Ros & Expires February 10, 2015                [Page 9]


Internet-Draft      AQM Characterization Guidelines          August 2014


   Not all applications run over the same flavor of TCP. Variety of
   senders generate different classes of traffic which may not react to
   congestion signals (aka unresponsive flows) or may not cut down their
   sending rate as expected (aka aggressive flows): AQM schemes aim at
   maintaining the queuing delay under control, which is challenged if
   blasting traffics are present.

   This section provides guidelines to assess the performance of an AQM
   proposal based on various metrics presented in Section 2 irrespective
   of traffic profiles involved -- different senders (TCP variants,
   unresponsive, aggressive), traffic mix with different applications,
   etc.

4.1.  TCP-friendly Sender

   This scenario helps to evaluate how an AQM scheme reacts to a TCP-
   friendly transport sender.  A single long-lived, non application
   limited, TCP New Reno flow transmits data between sender A and
   receiver B. Other TCP friendly congestion control schemes such as
   TCP-friendly rate control [RFC5348] etc MAY also be considered.

   For each TCP-friendly transport considered, the graphs described in
   Section 2.6 MUST be generated.

4.2.  Aggressive Transport Sender

   This scenario helps to evaluate how an AQM scheme reacts to a
   transport sender whose sending rate is more aggressive than a single
   TCP-friendly sender.  A single long-lived, non application limited,
   TCP Cubic flow transmits data between sender A and receiver B. Other
   aggressive congestion control schemes MAY also be considered.

   For each flavor of aggressive transport, the graphs described in
   Section 2.6 MUST be generated.

4.3.  Unresponsive Transport Sender

   This scenario helps evaluate how an AQM scheme reacts to a transport
   sender who is not responsive to congestion signals (ECN marks and/or
   packet drops) from the AQM scheme.  In order to create a test
   environment that results in queue build up, we consider unresponsive
   flow(s) whose sending rate is greater than the bottleneck link
   capacity between routers L and R. Note that faulty transport
   implementations on end hosts and/or faulty network elements en-route
   that "hide" congestion signals in packet headers [I-D.ietf-aqm-
   recommendation] may also lead to a similar situation, such that the
   AQM scheme needs to adapt to unresponsive traffic.

   This scenario consists of long-lived non application limited UDP flow
   transmits data  between sender A and receiver B. Graphs described in
   Section 2.6 MUST be generated.

4.4.  TCP initial congestion window

Kuhn, Natarajan, Ros & Expires February 10, 2015               [Page 10]


Internet-Draft      AQM Characterization Guidelines          August 2014


   This scenario helps evaluate how an AQM scheme adapts to a traffic
   mix consisting of different variants of TCP for various values of the
   initial congestion window (IW):

   o  TCP: Cubic and/or New Reno;

   o  IW: 3 or 10 packets.

   Figure 2 presents the various cases for the traffic that MUST be
   generated between sender A and receiver B.

    +----+-----------------------------------------------+
    |Case| Number of flows                               |
    +    +-----------+------------+----------+-----------+
    |    |Cubic (IW3)|Cubic (IW10)|Reno (IW3)|Reno (IW10)|
    +----+-----------+------------+----------+-----------+
    |I   |        1  |         1  |       0  |        0  |
    |    |           |            |          |           |
    |II  |        0  |         0  |       1  |        1  |
    |    |           |            |          |           |
    |III |        1  |         0  |       1  |        0  |
    |    |           |            |          |           |
    |IV  |        0  |         1  |       0  |        1  |
    +----+-----------+------------+----------+-----------+

   For each of these scenarios, the graphs described in Section 2.6 MUST
   be generated for each class of traffic.

4.5.  Traffic Mix

   This scenario helps to evaluate how an AQM scheme reacts to a traffic
   mix consisting of different applications such as bulk transfer, web,
   voice, video traffic.  These testing cases presented in this
   subsection have been inspired by the table 2 of [DOCSIS2013]:

   o  Bulk TCP transfer

   o  Web traffic

   o  VoIP

   o  Constant bit rate UDP traffic

   o  Adaptive video streaming

   Figure 3 presents the various cases for the traffic that MUST be
   generated between sender A and receiver B.







Kuhn, Natarajan, Ros & Expires February 10, 2015               [Page 11]


Internet-Draft      AQM Characterization Guidelines          August 2014


            +----+-----------------------------+
            |Case| Number of flows             |
            +    +----+----+----+---------+----+
            |    |VoIP|Webs|CBR |AdaptVid |FTP |
            +----+----+----+----+---------+----+
            |I   |  1 |  1 |  0 |      0  |  0 |
            |    |    |    |    |         |    |
            |II  |  1 |  1 |  0 |      0  |  1 |
            |    |    |    |    |         |    |
            |III |  1 |  1 |  0 |      0  |  5 |
            |    |    |    |    |         |    |
            |IV  |  1 |  1 |  1 |      0  |  5 |
            |    |    |    |    |         |    |
            |V   |  1 |  1 |  0 |      1  |  5 |
            |    |    |    |    |         |    |
            +----+----+----+----+---------+----+

   For each of these scenarios, the graphs described in Section 2.6 MUST
   be generated for each class of traffic.  In addition, other metrics
   such as end-to-end latency, jitter and flow completion time MUST be
   generated.

5.  RTT fairness

5.1.  Motivation

   The capability of AQM schemes to control the queuing delay highly
   depends on the way end-to-end protocols react to congestion signals.
   When the RTT varies, the behaviour of congestion controls is impacted
   and so the capability of AQM schemes to control the queue.  It is
   therefore important to assess the AQM schemes against a set of RTTs
   (e.g., from 5 ms to 200 ms).

   Also, asymmetry in terms of RTT between various paths SHOULD be
   considered so that the fairness between the flows can be discussed as
   one may react faster to congestion than another.  The introduction of
   AQM schemes  may improve this fairness.

   Moreover, introducing an AQM  scheme may result in the absence of
   fairness between the flows, even when the RTTs are identical.  This
   potential lack of fairness SHOULD be evaluated.

5.2.  Required tests

   The topology that SHOULD be used is detailed in Figure 1:

   o  to evaluate the inter-RTT fairness, for each run, ten flows
      divided into two categories.  Category I (Flow1.1, ..., Flow1.5)
      which RTT between sender A and Router L SHOULD be 5ms.  Category
      II (Flow2.1, ..., Flow 2.5) which RTT between sender A and Router




Kuhn, Natarajan, Ros & Expires February 10, 2015               [Page 12]


Internet-Draft      AQM Characterization Guidelines          August 2014

      L SHOULD be in [5ms;200ms].

   o  to evaluate the impact of the RTT value on the AQM performance and
      the intra-protocol fairness, for each run, ten flows (Flow1.1,
      ..., Flow1.5 and Flow2.1, ..., Flow2.5) SHOULD be introduced.  For
      each experiment, the set of RTT SHOULD be the same for all the
      flows and in [5ms;200ms].

   These flows MUST use the same congestion control algorithm.

5.3.  Metrics to evaluate the RTT fairness

   The output that MUST be measured is:

   o  for the inter-RTT fairness: (1) the cumulated average goodput of
      the flows from Category I, goodput_Cat_I (Section 2.4); (2) the
      cumulated average goodput of the flows from Category II,
      goodput_Cat_II (Section 2.4); (3) the ratio goodput_Cat_II/
      goodput_Cat_I; (4) the average packet drop rate for each category
      (Section 2.2).

   o  for the intra-protocol RTT fairness: (1) the cumulated averga
      goodput of the ten flows (Section 2.4); (2) the average packet
      drop rate for the ten flows(Section 2.2).

6.  Burst absorption

6.1.  Motivation

   Packet arrivals can be bursty due to various reasons.  Dropping one
   or more packets from a burst may result in performance penalties for
   the corresponding flows since the dropped packets have to be
   retransmitted.  Performance penalties may turn into unmet SLAs and be
   disincentives to AQM adoption.  Therefore, an AQM scheme SHOULD be
   designed to accommodate transient bursts.  AQM schemes do not present
   the same tolerance to bursts of packets arriving in the buffer: this
   tolerance MUST be quantified.

   Note that accommodating bursts translates to higher queue length and
   queuing delay.  Naturally, it is important that the AQM scheme brings
   bursty traffic under control quickly.  On the other hand, spiking
   packet drops in order to bring packet bursts quickly under control
   could result in multiple drops per flow and severely impact transport
   and application performance.  Therefore, an AQM scheme SHOULD bring
   bursts under control by balancing both aspects -- (1) queuing delay
   spikes are minimized and (2) performance penalties for ongoing flows
   in terms of packet drops are minimized.








Kuhn, Natarajan, Ros & Expires February 10, 2015               [Page 13]


Internet-Draft      AQM Characterization Guidelines          August 2014


   An AQM scheme maintains short queues to allow the remaining space in
   the queue for bursts of packets.  The tolerance to bursts of packets
   depends on the number of packets in the queue, which is directly
   linked to the AQM algorithm.  Moreover, one AQM scheme may implement
   a feature controlling the maximum size of accepted bursts, that may
   depend on the buffer occupancy or the currently estimated queuing
   delay.  Also, the impact of the buffer size on the burst allowance
   MAY be evaluated.

6.2.  Required tests

   For this scenario, the following traffic MUST be generated from
   sender A to receiver B:

   o  IW10: TCP transfer with initial congestion window set to 10 of
      5MB;

   o  Bursty video frames;

   o  Web traffic;

   o  Constant bit rate UDP traffic.

   Figure 4 presents the various cases for the traffic that MUST be
   generated between sender A and receiver B.

        +-----------------------------------------+
        |Case| Number of traffic                  |
        |    +-----+----+----+--------------------+
        |    |Video|Webs| CBR| Bulk Traffic (IW10)|
        +----|-----|----|----|--------------------|
        |I   |  0  |  1 |  1 |     0              |
        |----|-----|----|----|--------------------|
        |II  |  0  |  1 |  1 |     1              |
        |----|-----|----|----|--------------------|
        |III |  1  |  1 |  1 |     0              |
        +----|-----|----|----|--------------------|
        |IV  |  1  |  1 |  1 |     0              |
        +----|-----|----|----|--------------------|
        |V   |  1  |  1 |  1 |     1              |
        +----+-----+----+----+--------------------+

6.3.  Metrics to evaluate the burst absorption capacity

   For each of these scenarios, the graphs described in Section 2.6 MUST
   be generated.  In addition, other metrics such as end-to-end latency,
   jitter, flow completion time MUST be generated.

7.  Stability

7.1.  Motivation



Kuhn, Natarajan, Ros & Expires February 10, 2015               [Page 14]


Internet-Draft      AQM Characterization Guidelines          August 2014


   Network devices experience varying operating conditions depending on
   factors such as time of day, deployment scenario etc.  For example:

   o  Traffic and congestion levels are higher during peak hours than
      off-peak hours.

   o  In the presence of scheduler, a queue's draining rate may vary
      depending on other queues: a low load on a high priority queue
      implies higher draining rate for lower priority queues.

   o  The available capacity on the physical layer may vary over time
      such as in the context of lossy channels.

   Whether the target context is a not stable environment, the
   capability of an AQM scheme to actually maintain its control on the
   queuing delay and buffer occupancy is challenged.  This document
   propose guidelines to assess the behaviour of AQM schemes under
   varying congestion levels and varying draining rates.

7.2.  Required tests

7.2.1.  Mild Congestion

   This scenario helps to evaluate how an AQM scheme reacts to a light
   load of incoming traffic resulting in mild congestion -- packet drop
   rates less than 1%. Each single-lived non application limited TCP
   flow transfers data.

   For this scenario, the graphs described in Section 2.6 MUST be
   generated.

7.2.2.  Medium Congestion

   This scenario helps to evaluate how an AQM scheme reacts to incoming
   traffic resulting in medium congestion -- packet drop rates between
   1%-3%. Each single-lived non application limited TCP flow transfers
   data.

   For this scenario, the graphs described in Section 2.6 MUST be
   generated.

7.2.3.  Heavy Congestion

   This scenario helps to evaluate how an AQM scheme reacts to incoming
   traffic resulting in heavy congestion -- packet drop rates between
   5%-10%. Each single lived non application limited TCP flow transfers
   data.

   For this scenario, the graphs described in Section 2.6 MUST be
   generated.

7.2.4.  Varying Available Bandwidth


Kuhn, Natarajan, Ros & Expires February 10, 2015               [Page 15]


Internet-Draft      AQM Characterization Guidelines          August 2014


   This scenario helps evaluate how an AQM scheme adapts to varying
   available bandwidth on the outgoing link.

   To simulate varying draining rates, the bottleneck bandwidth between
   nodes 'Router L' and 'Router R' varies over the course of the
   experiment as follows:

   o  Experiment 1: the capacity varies between two values according to
      a large time scale.  As an example, the following phases may be
      considered: phase I - 100Mbps during 0-5s; phase II - 10Mbps
      during 5-10s: phase I again, ... and so on.

   o  Experiment 2: the capacity varies between two values according to
      a short time scale.  As an example, the following phases may be
      considered: phase I - 100Mbps during 100ms; phase II - 10Mbps
      during 100ms; phase I again during 100ms, ... and so on.

   More realistic fluctuating bandwidth patterns MAY be considered.

   The scenario consists of TCP New Reno flows between sender A and
   receiver B. In order to better assess the impact of draining rates on
   the AQM behavior, the tester MUST compare its performance with those
   of drop-tail.

   For this scenario, the graphs described in Section 2.6 MUST be
   generated.  Moreover, one graph SHOULD be generated for each of the
   phases previously detailed.

7.3.  Parameter sensitivity and stability analysis

   An AQM scheme's control law is the primary means by which the AQM
   controls queuing delay.  Hence understanding the AQM control law is
   critical to understanding AQM behavior.  The AQM's control law may
   include several input parameters whose values affect the AQM output
   behavior and stability.  Additionally, AQM schemes may auto-tune
   parameter values in order to maintain stability under different
   network conditions (such as different  congestion levels, draining
   rates or network environments). The stability of these auto-tuning
   techniques is also important to understand.

   AQM proposals SHOULD provide background material showing control
   theoretic analysis of the AQM control law and the input parameter
   space within which the control law operates as expected; or could use
   other ways to discuss its stability.  For parameters that are auto-
   tuned, the material SHOULD include stability analysis of the auto-
   tuning mechanism(s) as well.  Such analysis helps to understand an
   AQM's  control law better and the network conditions/deployments
   under which the AQM is stable.

8.  Implementation cost

8.1.  Motivation


Kuhn, Natarajan, Ros & Expires February 10, 2015               [Page 16]


Internet-Draft      AQM Characterization Guidelines          August 2014


   An AQM's successful deployment is directly related to its ease of
   implementation.  Network devices may need hardware or software
   implementations of the AQM. Depending on a device's capabilities and
   limitations, the device may or may not be able to implement some or
   all parts of the AQM logic.

   AQM proposals SHOULD provide pseudo-code for the complete AQM scheme,
   highlighting generic implementation-specific aspects of the scheme
   such as "drop-tail" vs.  "drop-head", inputs (current queuing delay,
   queue length), computations involved, need for timers etc.  This
   helps identify costs associated with implementing the AQM on a
   particular hardware or software device.  Also, it helps the WG
   understand which kind of devices can easily support the AQM  and
   which cannot.

8.2.  Required discussion

   AQM proposals SHOULD highlight parts of AQM logic that are device
   dependent and discuss if and how AQM behavior could be impacted by
   the device.  For example, a queue-delay based AQM scheme requires
   current queuing delay as input from the device.  If the device
   already maintains this value, then it is trivial to implement the AQM
   logic on the device.  On the other hand, if the device provides
   indirect means to estimate queuing delay (for example: timestamps,
   dequeing rate etc.), then the AQM behavior is sensitive to how good
   the queuing delay estimate turns out on that device.  Highlighting
   the AQM's sensitivity to queuing delay estimate helps implementers
   identify optimal means of implementing the AQM on a device.

9.  Operator control knobs and auto-tuning

   One of the biggest hurdles for RED deployment was/is its parameter
   sensitivity to operating conditions -- how difficult it is to tune
   important RED parameters for a deployment in order to get maximum
   benefit from the RED implementation.  Fluctuating congestion levels
   and network conditions add to the complexity.  Incorrect parameter
   values lead to poor performance.  This is one reason why RED is
   reported to be usually turned off.

   Any AQM scheme is likely to have parameters whose values affect the
   AQM's control law and behavior.  Exposing all these parameters as
   control knobs to a network operator (or user) can easily result in an
   unsafe AQM deployment.  Unexpected AQM behavior ensues when parameter
   values are not set properly.  A minimal number of control knobs
   minimizes the number of ways a, possible naive, user can break the
   AQM system.  Fewer control knobs make the AQM scheme more user-
   friendly and easier to deploy and debug.

   We recommend that an AQM scheme SHOULD minimize the number of control
   knobs exposed for operator tuning.  An AQM scheme SHOULD expose only
   those knobs that control the macroscopic AQM behavior such as queue
   delay threshold, queue length threshold, etc.


Kuhn, Natarajan, Ros & Expires February 10, 2015               [Page 17]


Internet-Draft      AQM Characterization Guidelines          August 2014


   Additionally, an AQM scheme's safety is directly related to its
   stability under varying operating conditions such as varying traffic
   profiles and fluctuating network conditions, as described in Section
   7. Operating conditions vary often and hence it is necessary that the
   AQM MUST remain stable under these conditions without the need for
   additional external tuning.  If AQM parameters require tuning under
   these conditions, then the AQM MUST self-adapt necessary parameter
   values by employing auto-tuning techniques.

10.  Interaction with ECN

10.1.  Motivation

   Apart from packet drops, Explicit Congestion Notification (ECN) is an
   alternative means to signal data senders about network congestion.
   The AQM recommendation document [I-D.ietf-aqm-recommendation]
   describes some of the benefits of using ECN with AQM.

10.2.  Required discussion

   An AQM scheme MAY support ECN, in which case testers MUST discuss and
   describe the support of ECN.

11.  Interaction with scheduling

11.1.  Motivation

   Coupled with an AQM scheme, a router may schedule the transmission of
   packets in a specific manner by introducing a scheduling scheme.
   This algorithm may create sub-queues and integrate a dropping policy
   on each of these sub-queues.  Another scheduling policy may modify
   the way packets are sequenced, modifying the timestamp of each
   packet.

11.2.  Required discussion

   The scheduling and the AQM conjointly impact on the end-to-end
   performance.  During the characterization process of a dropping
   policy, the tester MAY discuss the feasibility to add scheduling on
   top of its algorithm.  This discussion MAY detail if the dropping
   policy is applied while packets are enqueued or dequeued.

12.  Discussion on methodology, metrics, AQM comparisons and packet
     sizes

12.1.  Methodology

   A sufficiently detailed description of the test setup SHOULD be
   provided.  Indeed, that would allow other to replicate the tests if
   needed.  This test setup MAY include software and hardware versions.
   The tester MAY make its data available.



Kuhn, Natarajan, Ros & Expires February 10, 2015               [Page 18]


Internet-Draft      AQM Characterization Guidelines          August 2014


   The proposals SHOULD be experimented on real systems, or they MAY be
   evaluated with event-driven simulations (such as NS-2, NS-3, OMNET,
   etc.). The proposed scenarios are not bound to a particular
   evaluation toolset.

12.2.  Comments on metrics measurement

   In this document, we present the end-to-end metrics that SHOULD be
   evaluated to evaluate the trade-off between latency and goodput.  The
   queue-related metrics enable a better understanding of the AQM
   behavior under tests and the impact of its internal parameters.
   Whenever it is possible, these guidelines advice to consider queue-
   related metrics, such as link utilization, queuing delay, queue size
   or packet loss.

   These guidelines could hardly detail the way the metrics can be
   measured depends highly on the evaluation toolset.

12.3.  Comparing AQM schemes

   This memo recognizes that the guidelines mentioned above may be used
   for comparing AQM schemes.  This memo recommends that AQM schemes
   MUST be compared against both performance and deployment categories.
   In addition, this section details how best to achieve a fair
   comparison of AQM schemes by avoiding certain pitfalls.

12.3.1.  Performance comparison

   AQM schemes MUST be compared against all the generic scenarios
   presented in this memo.  AQM schemes MAY be compared for specific
   network environments such as data center, home networks etc.  If an
   AQM scheme's parameter(s) were externally tuned for optimization or
   other purposes, these values MUST be disclosed.

   Note that AQM schemes belong to different varieties such as queue-
   length based scheme (ex: RED) or queue-delay based scheme (ex: CoDel,
   PIE). Also, AQM schemes expose different control knobs associated
   with different semantics.  For example, while both PIE and CoDel are
   queue-delay based schemes and each expose a knob to control the
   queueing delay -- PIE's "queueing delay reference" vs.  CoDel's
   "queueing delay target", the two schemes' knobs have different
   semantics resulting in different control points.  Such differences in
   AQM schemes can be easily overlooked while making comparisons.

   This document recommends the following procedures for a fair
   performance comparison of two AQM schemes:








Kuhn, Natarajan, Ros & Expires February 10, 2015               [Page 19]


Internet-Draft      AQM Characterization Guidelines          August 2014


   1.  comparable control parameters and comparable input values:
       carefully identify the set of parameters that control similar
       behavior between the two AQM schemes and ensure these parameters
       have comparable input values.  For example, while comparing how
       well a queue-length based AQM X controls queueing delay vs.
       queue-delay based AQM Y, identify the two schemes' parameters
       that control queue delay and ensure that their input values are
       comparable.  Similarly, to compare two AQM schemes on how well
       they accommodate bursts, identify burst-related control
       parameters and ensure they are configured with similar values.

   2.  compare over a range of input configurations: there could be
       situations when the set of control parameters that affect a
       specific behavior have different semantics between the two AQM
       schemes.  As mentioned above, PIE's knob to control queue delay
       has different semantics from CoDel's.  In such situations, the
       schemes MUST be compared over a range of input configurations.
       For example, compare PIE vs.  CoDel over the range of delay input
       configurations -- 5ms, 10ms, 15ms etc.

12.3.2.  Deployment comparison

   AQM schemes MUST be compared against deployment criteria such as the
   parameter sensitivity (Section 7.3), the auto-tuning (Section 9) or
   the implementation cost (Section 8).

12.4.  Packet sizes and congestion notification

   An AQM scheme may be considering packet sizes while generating
   congestion signals.  [RFC7141] discusses the motivations behind the
   same.  For example, control packets such as DNS requests/responses,
   TCP SYNs/ACKs are small, and their loss can severely impact
   application performance.  An AQM scheme may therefore be biased
   towards small packets by dropping them with smaller probability
   compared to larger packets.  However, such an AQM scheme is unfair to
   data senders generating larger packets.  Data senders, malicious or
   otherwise, are motivated to take advantage of the AQM scheme by
   transmitting smaller packets, and could result in unsafe deployments
   and unhealthy transport and/or application designs.

   An AQM scheme SHOULD adhere to recommendations outlined in [RFC7141],
   and SHOULD NOT provide undue advantage to flows with smaller packets.

13.  Acknowledgements

   This work has been partially supported by the European Community
   under its Seventh Framework Programme through the Reducing Internet
   Transport Latency (RITE) project (ICT-317700).

14.  Contributors




Kuhn, Natarajan, Ros & Expires February 10, 2015               [Page 20]


Internet-Draft      AQM Characterization Guidelines          August 2014


   Many thanks to S. Akhtar, A.B. Bagayoko, F. Baker, D. Collier-Brown,
   G. Fairhurst, T. Hoiland-Jorgensen, C. Kulatunga, R. Pan, D. Taht and
   M. Welzl for detailed and wise feedback on this document.

15.  IANA Considerations

   This memo includes no request to IANA.

16.  Security Considerations

   This document, by itself, presents no new privacy nor security
   issues.

17.  References

17.1.  Normative References

   [I-D.ietf-aqm-recommendation]
              Baker, F. and G. Fairhurst, "IETF Recommendations
              Regarding Active Queue Management", Internet-Draft draft-
              ietf-aqm-recommendation-07, August 2014.

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

   [RFC7141]  Briscoe, B. and J. Manner, "Byte and Packet Congestion
              Notification", RFC 7141, 2014.

17.2.  Informative References

   [BB2011]   "BufferBloat: what's wrong with the internet?", ACM Queue
              vol.  9, 2011.

   [CODEL]    Nichols, K. and V. Jacobson, "Controlling Queue Delay",
              ACM Queue , 2012.

   [DOCSIS2013]
              White, G. and D. Rice, "Active Queue Management Algorithms
              for DOCSIS 3.0", Technical report - Cable Television
              Laboratories , 2013.

   [LOSS-SYNCH-MET-08]
              Hassayoun, S. and D. Ros, "Loss Synchronization and Router
              Buffer Sizing with High-Speed Versions of TCP", IEEE
              INFOCOM Workshops , 2008.

   [PIE]      Pan, R., Natarajan, P., Piglione, C., Prabhu, MS.,
              Subramanian, V., Baker, F. and B. VerSteeg, "PIE: A
              lightweight control scheme to address the bufferbloat
              problem", IEEE HPSR , 2013.




Kuhn, Natarajan, Ros & Expires February 10, 2015               [Page 21]


Internet-Draft      AQM Characterization Guidelines          August 2014


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

   [RFC5348]  Floyd, S., Handley, M., Padhye, J. and J. Widmer, "TCP
              Friendly Rate Control (TFRC): Protocol Specification", RFC
              5348, September 2008.

   [RFC5681]  Allman, M., Paxson, V. and E. Blanton, "TCP Congestion
              Control", RFC 5681, September 2009.

   [RFC6297]  Welzl, M. and D. Ros, "A Survey of Lower-than-Best-Effort
              Transport Protocols", RFC 6297, June 2011.

   [TCPEVAL2013]
              Hayes, D., Ros, D., Andrew, L.L.H. and S. Floyd, "Common
              TCP Evaluation Suite", IRTF ICCRG , 2013.

   [YU06]     Jay, P., Fu, Q. and G. Armitage, "A preliminary analysis
              of loss synchronisation between concurrent TCP flows",
              Australian Telecommunication Networks and Application
              Conference (ATNAC) , 2006.

Authors' Addresses

   Nicolas Kuhn, editor
   Telecom Bretagne
   2 rue de la Chataigneraie
   Cesson-Sevigne, 35510
   France

   Phone: +33 2 99 12 70 46
   Email: nicolas.kuhn@telecom-bretagne.eu


   Preethi Natarajan, editor
   Cisco Systems
   510 McCarthy Blvd
   Milpitas, California
   United States

   Email: prenatar@cisco.com









Kuhn, Natarajan, Ros & Expires February 10, 2015               [Page 22]


Internet-Draft      AQM Characterization Guidelines          August 2014


   David Ros
   Simula Research Laboratory AS
   P.O. Box 134
   Lysaker, 1325,
   Norway

   Phone: +33 299 25 21 21
   Email: dros@simula.no


   Naeem Khademi
   University of Oslo
   Department of Informatics, PO Box 1080 Blindern
   N-0316 Oslo,
   Norway

   Phone: +47 2285 24 93
   Email: naeemk@ifi.uio.no



































Kuhn, Natarajan, Ros & Expires February 10, 2015               [Page 23]


Html markup produced by rfcmarkup 1.129d, available from https://tools.ietf.org/tools/rfcmarkup/