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Model Based Bulk Performance Metrics
draft-ietf-ippm-model-based-metrics-00

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This is an older version of an Internet-Draft that was ultimately published as RFC 8337.
Authors Matt Mathis , Al Morton
Last updated 2013-06-21
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draft-ietf-ippm-model-based-metrics-00
IP Performance Working Group                                   M. Mathis
Internet-Draft                                               Google, Inc
Intended status: Experimental                                  A. Morton
Expires: December 23, 2013                                     AT&T Labs
                                                           June 21, 2013

                  Model Based Bulk Performance Metrics
               draft-ietf-ippm-model-based-metrics-00.txt

Abstract

   We introduce a new class of model based metrics designed to determine
   if a long path can meet predefined end-to-end application performance
   targets.  This is done by subpath at a time testing -- by applying a
   suite of single property tests to successive subpaths of a long path.
   In many cases these single property tests are based on existing IPPM
   metrics, with the addition of success and validity criteria.  The
   subpath at a time tests are designed to facilitate IP providers
   eliminating all known conditions that might prevent the full end-to-
   end path from meeting the users target performance.

   This approach makes it possible to to determine the IP performance
   requirements needed to support the desired end-to-end TCP
   performance.  The IP metrics are based on traffic patterns that mimic
   TCP but are precomputed independently of the actual behavior of TCP
   over the subpath under test.  This makes the measurements open loop,
   eliminating nearly all of the difficulties encountered by traditional
   bulk transport metrics, which rely on congestion control equilibrium
   behavior.

   A natural consequence of this methodology is verifiable network
   measurement: measurements from any given vantage point are repeatable
   from other vantage points.

   Formatted: Fri Jun 21 18:23:29 PDT 2013

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

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   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 December 23, 2013.

Copyright Notice

   Copyright (c) 2013 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
   Provisions Relating to IETF Documents
   (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.

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Table of Contents

   1.  Introduction . . . . . . . . . . . . . . . . . . . . . . . . .  5
     1.1.  TODO . . . . . . . . . . . . . . . . . . . . . . . . . . .  6
   2.  Terminology  . . . . . . . . . . . . . . . . . . . . . . . . .  6
   3.  New requirements relative to RFC 2330  . . . . . . . . . . . .  8
   4.  Background . . . . . . . . . . . . . . . . . . . . . . . . . .  9
     4.1.  TCP properties . . . . . . . . . . . . . . . . . . . . . . 11
   5.  Common Models and Parameters . . . . . . . . . . . . . . . . . 12
     5.1.  Target End-to-end parameters . . . . . . . . . . . . . . . 13
     5.2.  Common Model Calculations  . . . . . . . . . . . . . . . . 13
     5.3.  Parameter Derating . . . . . . . . . . . . . . . . . . . . 14
   6.  Common testing procedures  . . . . . . . . . . . . . . . . . . 15
     6.1.  Traffic generating techniques  . . . . . . . . . . . . . . 15
       6.1.1.  Paced transmission . . . . . . . . . . . . . . . . . . 15
       6.1.2.  Constant window pseudo CBR . . . . . . . . . . . . . . 16
         6.1.2.1.  Scanned window pseudo CBR  . . . . . . . . . . . . 16
       6.1.3.  Intermittent Testing . . . . . . . . . . . . . . . . . 16
       6.1.4.  Intermittent Scatter Testing . . . . . . . . . . . . . 17
     6.2.  Interpreting the Results . . . . . . . . . . . . . . . . . 17
       6.2.1.  Test outcomes  . . . . . . . . . . . . . . . . . . . . 17
       6.2.2.  Statistical criteria for measuring run_length  . . . . 17
       6.2.3.  Classifications of tests . . . . . . . . . . . . . . . 19
       6.2.4.  Reordering Tolerance . . . . . . . . . . . . . . . . . 20
     6.3.  Test Qualifications  . . . . . . . . . . . . . . . . . . . 20
       6.3.1.  Verify the Traffic Generation Accuracy . . . . . . . . 20
       6.3.2.  Verify the absence of cross traffic  . . . . . . . . . 21
       6.3.3.  Additional test preconditions  . . . . . . . . . . . . 22
   7.  Single Property Tests  . . . . . . . . . . . . . . . . . . . . 22
     7.1.  Basic Data and Loss Rate Tests . . . . . . . . . . . . . . 22
       7.1.1.  Loss Rate at Paced Full Data Rate  . . . . . . . . . . 22
       7.1.2.  Loss Rate at Full Data Windowed Rate . . . . . . . . . 23
       7.1.3.  Background Loss Rate Tests . . . . . . . . . . . . . . 23
     7.2.  Standing Queue tests . . . . . . . . . . . . . . . . . . . 24
       7.2.1.  Congestion Avoidance . . . . . . . . . . . . . . . . . 24
       7.2.2.  Buffer Bloat . . . . . . . . . . . . . . . . . . . . . 25
       7.2.3.  Duplex Self Interference . . . . . . . . . . . . . . . 25
     7.3.  Slowstart tests  . . . . . . . . . . . . . . . . . . . . . 25
       7.3.1.  Full Window slowstart test . . . . . . . . . . . . . . 25
       7.3.2.  Slowstart AQM test . . . . . . . . . . . . . . . . . . 26
     7.4.  Sender Rate Burst tests  . . . . . . . . . . . . . . . . . 26
       7.4.1.  Sender TCP Send Offload (TSO) tests  . . . . . . . . . 26
       7.4.2.  Sender Full Window burst test  . . . . . . . . . . . . 26
   8.  Combined Tests . . . . . . . . . . . . . . . . . . . . . . . . 27
     8.1.  Sustained burst test . . . . . . . . . . . . . . . . . . . 27
   9.  Calibration  . . . . . . . . . . . . . . . . . . . . . . . . . 28
   10. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 28
   11. Informative References . . . . . . . . . . . . . . . . . . . . 28

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   Appendix A.  Model Derivations . . . . . . . . . . . . . . . . . . 29
   Appendix B.  old text  . . . . . . . . . . . . . . . . . . . . . . 29
     B.1.  An earlier document  . . . . . . . . . . . . . . . . . . . 30
     B.2.  End-to-end parameters from subpaths  . . . . . . . . . . . 31
     B.3.  Per subpath parameters . . . . . . . . . . . . . . . . . . 32
     B.4.  Version Control  . . . . . . . . . . . . . . . . . . . . . 32
   Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 32

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1.  Introduction

   Model based bulk performance metrics evaluate an Internet paths
   ability to carry bulk data.  TCP models are used to design a targeted
   diagnostic suite of IP performance tests which can be applied
   independently to each subpath of the full end-to-end path.  The
   targeted diagnostic suites are constructed such that independent
   tests of the subpaths will accurately predict if the full end-to-end
   path can deliver bulk data at the specified performance target,
   independent of the measurement vantage points or other details of the
   test procedures used to measure each subpath.

   Each test in the targeted diagnostic suite consists of a precomputed
   traffic pattern and statistical criteria for evaluating packet
   delivery.

   TCP models are used to design traffic patterns that mimic TCP or
   other bulk transport protocol operating at the target performance and
   RTT over a full range of conditions, including flows that are bursty
   at multiple time scales.  The traffic patterns are computed in
   advance based on the properties of the full end-to-end path and
   independent of the properties of individual subpaths.  As much as
   possible the traffic is generated deterministically in ways that
   minimizes the extent to which test methodology, measurement points,
   measurement vantage or path partitioning effect the details of the
   traffic.

   Models are also used to compute the statistical criteria for
   evaluating the IP diagnostics tests.  The criteria for passing each
   test must be determined from the end-to-end target performance and
   independent of the RTT or other properties of the subpath under test.
   In addition to passing or failing, a test can be inconclusive if the
   precomputed traffic pattern was not authentically generated, test
   preconditions were not met or the measurement results were not
   statistically significantly.

   TCP's ability to compensate for less than ideal network conditions is
   fundamentally affected by the RTT and MTU of the end-to-end Internet
   path that it traverses which are both fixed properties of the end-to-
   end path.  The target values for these three parameters, Data Rate,
   RTT and MTU, are determined by the application, its intended use and
   the physical infrastructure over which it traverses.  They are used
   to inform the models used to design the targeted diagnostic suite.

   Section 2 defines terminology used throughout this document.  It has
   been difficult to develop BTC metrics due to some overlooked
   requirements described in Section 3 and some intrinsic problems with
   using protocols for measurement, described in Section 4.  In

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   Section 5 we describe the models and common parameters used to derive
   the targeted diagnostic suite.  In Section 6 we describe common
   testing procedures used by all of the tests.  Each subpath is
   evaluated using suite of far simpler and more predictable single
   property tests described in Section 7.  Section 8 describes some
   combined tests that are more efficient to implement and deploy.
   However, if they fail they may not clearly indicate the nature of the
   problem.

   There exists a small risk that model based metric itself might yield
   a false pass result, in the sense that every subpath of an end-to-end
   path passes every IP diagnostic test and yet a real application falls
   to attain the performance target over the end-to-end path.  If this
   happens, then the calibration procedure described in Section 9 needs
   to be used to validate and potentially revise the models.

   Future document will define model based metrics for other traffic
   classes and application types, such as real time.

1.1.  TODO

   Please send comments on this draft to ippm@ietf.org.  See
   http://goo.gl/02tkD for more information including: interim drafts,
   an up to date todo list and information on contributing.

   Formatted: Fri Jun 21 18:23:29 PDT 2013

2.  Terminology

   Properties determined by the end-to-end path and application.  They
   are described in more detail in Section 5.1.

   end-to-end target parameters:  Application or transport performance
      goals for the end-to-end path.  They include the target data rate,
      RTT and MTU described below.
   Target Data Rate:  The application or ultimate user's performance
      goal.  This must be slightly smaller than the actual link rate,
      otherwise there is no margin for compensating for RTT or other
      path protperties.
   Target RTT (Round Trip Time):  The RTT over which the application
      must meet the target performance.
   Target MTU (Maximum Transmission Unit):  Assume 1500 Bytes per packet
      unless otherwise specified.  If some subpath forces a smaller MTU,
      then it becomes the target MTU, and all subpaths must be tested
      with the same smaller MTU.

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   Effective Bottleneck Data Rate:  This is the bottleneck data rate
      that might be inferred from the ACK stream, by looking at how much
      data the ACK stream reports was delivered per unit time.  See
      Section 4.1 for more details.
   Permitted Number of Connections:  The target rate can be more easily
      obtained by dividing the traffic across more than one connection.
      In general the number of concurrent connections is determined by
      the application, however see the comments below on multiple
      connections.
   [sender] [interface] rate:  The burst data rate, constrained by the
      data sender's interfaces.  Today 1 or 10 Gb/s are typical.
   Header overhead:  The IP and TCP header sizes, which are the portion
      of each MTU not available for carrying application payload.
      Without loss of generality this is assumed to be the size for
      returning acknowledgements (ACKs).  For TCP, the Maximum Segment
      Size (MSS) is the Target MTU minus the header overhead.

   Terminology about paths, etc.  See [RFC2330] and
   [I-D.morton-ippm-lmap-path].

   [data] sender  Host sending data and receiving ACKs, typically via
      TCP.
   [data] receiver  Host receiving data and sending ACKs, typically via
      TCP.
   subpath  Subpath as defined in [RFC2330].
   Measurement Point  Measurement points as described in
      [I-D.morton-ippm-lmap-path].
   test path  A path between two measurement points that includes a
      subpath of the end-to-end path under test, plus possibly
      additional infrastructure between the measurement points and the
      subpath.
   [Dominant] Bottleneck  The Bottleneck that determines a flow's self
      clock.  It generally determines the traffic statistics for the
      entire path.  See Section 4.1.
   front path  The subpath from the data sender to the dominant
      bottleneck.
   back path  The subpath from the dominant bottleneck to the receiver.
   return path  The path taken by the ACKs from the data receiver to the
      data sender.
   cross traffic  Other, potentially interfering, traffic competing for
      resources (network and/or queue capacity).

   Basic parameters common to all models and subpath tests.  They are
   described in more detail in Section 5.2.

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   @  @@@
   pipe size  The number of packets needed in flight (the window size)
      to exactly fill some network path or sub path.  The is the window
      size which in normally the onset of queueing.
   target_pipe_size:  The number of packets in flight (the window size)
      needed to exactly meet the target rate, with a single stream and
      no cross traffic for the specified target data rate, RTT and MTU.
   subpath pipe size
   run length  Observed, measured or specified number of packets that
      are (to be) delivered between losses or ECN marks.  Nominally one
      over the loss probability.
   target_run_length  Required run length computed from the target data
      rate, RTT and MTU.
   reference_target_run_length:  One specific conservative estimate of
      the number of packets that must be delivered between loss episodes
      in most diagnostic tests.
   derating:  The modeling framework permits some latitude in derating
      some specific test parameters as described in Section 5.3.

   Test types [These need work]

   capacity tests:  For "capacity tests" is required that as long as the
      test traffic is within the proper envelope for the target end-to-
      end performance, the average packet losses must be below the
      threshold computed by the model.
   Engineering tests:  Engineering tests verify that the subpath under
      test interacts well with TCP style self clocked protocols using
      adaptive congestion control based on packet loss and ECN marks.
      For example "AQM Tests" verify that when the presented load
      exceeds the capacity of the subpath, the subpath signals for the
      transport protocol to slow down, by appropriately ECN marking or
      dropping some of the packets.  Note while that cross traffic is
      can cause capacity tests to fail, it has the potential to cause
      AQM tests to false pass, which is why AQM tests require separate
      test procedures.

3.  New requirements relative to RFC 2330

   Model Based Metrics are designed to fulfil some additional
   requirement that were not recognized at the time RFC 2330 was
   written.  These missing requirements may have significantly
   contributed to policy difficulties in the IP measurement space.  Some
   additional requirements are:

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   o  Metrics must be actionable by the ISP - they have to be
      interpreted in terms of behaviors or properties at the IP or lower
      layers, that an ISP can test, repair and verify.
   o  Metrics must be vantage point invariant over a significant range
      of measurement point choices (e.g., measurement points as
      described in [I-D.morton-ippm-lmap-path]), including off path
      measurement points.  The only requirements on MP selection should
      be that the portion of the path that is not under test is
      effectively ideal (or is non ideal in calibratable ways) and the
      end-to-end RTT between MPs is below some reasonable bound.
   o  Metrics must be repeatable by multiple parties.  It must be
      possible for different parties to make the same measurement and
      observe the same results.  In particular it is specifically
      important that both a consumer (or their delegate) and ISP be able
      to perform the same measurement and get the same result.

   NB: All of the metric requirements in RFC 2330 should be reviewed and
   potentially revised.  If such a document is opened soon enough, this
   entire section should be dropped.

4.  Background

   At the time the IPPM WG was chartered, sound Bulk Transport Capacity
   measurement was known to be beyond our capabilities.  By hindsight it
   is now clear why it is such a hard problem:
   o  TCP is a control system with circular dependencies - everything
      affects performance, including components that are explicitly not
      part of the test.
   o  Congestion control is an equilibrium process, transport protocols
      change the network (raise loss probability and/or RTT) to conform
      to their behavior.
   o  TCP's ability to compensate for network flaws is directly
      proportional to the number of roundtrips per second (i.e.
      inversely proportional to the RTT).  As a consequence a flawed
      link may pass a short RTT local test even though it fails when the
      path is extended by a perfect network to some larger RTT.
   o  TCP has a meta Heisenberg problem - Measurement and cross traffic
      interact in unknown and ill defined ways.  The situation is
      actually worse than the traditional physics problem where you can
      at least estimate the relative momentum of the measurement and
      measured particles.  For network measurement you can not in
      general determine the relative "elasticity" of the measurement
      traffic and cross traffic, so you can not even gage the relative
      magnitude of their effects on each other.

   The MBM approach is to "open loop" TCP by precomputing traffic
   patterns that are typically generated by TCP operating at the given

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   target parameters, and evaluating delivery statistics (losses and
   delay).  In this approach the measurement software explicitly
   controls the data rate, transmission pattern or cwnd (TCP's primary
   congestion control state variables) to create repeatable traffic
   patterns that mimic TCP behavior but are independent of the actual
   network behavior of the subpath under test.  These patterns are
   manipulated to probe the network to verify that it can deliver all of
   the traffic patterns that a transport protocol is likely to generate
   under normal operation at the target rate and RTT.

   Models are used to determine the actual test parameters (burst size,
   loss rate, etc) from the target parameters.  The basic method is to
   use models to estimate specific network properties required to
   sustain a given transport flow (or set of flows), and using a suite
   of metrics to confirm that the network meets the required properties.

   A network is expected to be able to sustain a Bulk TCP flow of a
   given data rate, MTU and RTT when the following conditions are met:
   o  The raw link rate is higher than the target data rate.
   o  The raw packet loss rate is lower than required by a suitable TCP
      performance model
   o  There is sufficient buffering at the dominant bottleneck to absorb
      a slowstart rate burst large enough to get the flow out of
      slowstart at a suitable window size.
   o  There is sufficient buffering in the front path to absorb and
      smooth sender interface rate bursts at all scales that are likely
      to be generated by the application, any channel arbitration in the
      ACK path or other mechanisms.
   o  When there is a standing queue at a bottleneck for a shared media
      subpath, there are suitable bounds on how the data and ACKs
      interact, for example due to the channel arbitration mechanism.
   o  When there is a slowly rising standing queue at the bottleneck the
      onset of packet loss has to be at an appropriate point (time or
      queue depth) and progressive.

   The tests to verify these condition are described in Section 7.

   Note that this procedure is not invertible: a singleton measurement
   is a pass/fail evaluation of a given path or subpath at a given
   performance.  Measurements to confirm that a link passes at one
   particular performance may not be generally be useful to predict if
   the link will pass at a different performance.

   Although they are not invertible, they do have several other valuable
   properties, such as natural ways to define several different
   composition metrics [RFC5835].

   [Add text on algebra on metrics (A-Frame from [RFC2330]) and

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   tomography.]  The Spatial Composition of fundamental IPPM metrics has
   been studied and standardized.  For example, the algebra to combine
   empirical assessments of loss ratio to estimate complete path
   performance is described in section 5.1.5. of [RFC6049].  We intend
   to use this and other composition metrics as necessary.

4.1.  TCP properties

   TCP and SCTP are self clocked protocols.  The dominant steady state
   behavior is to have an approximately fixed quantity of data and
   acknowledgements (ACKs) circulating in the network.  The receiver
   reports arriving data by returning ACKs to the data sender, the data
   sender most frequently responds by sending exactly the same quantity
   of data back into the network.  The quantity of data plus the data
   represented by ACKs circulating in the network is referred to as the
   window.  The mandatory congestion control algorithms incrementally
   adjust the widow by sending slightly more or less data in response to
   each ACK.  The fundamentally important property of this systems is
   that it is entirely self clocked: The data transmissions are a
   reflection of the ACKs that were delivered by the network, the ACKs
   are a reflection of the data arriving from the network.

   A number of phenomena can cause bursts of data, even in idealized
   networks that are modeled as simple queueing systems.

   During slowstart the data rate is doubled by sending twice as much
   data as was delivered to the receiver.  For slowstart to be able to
   fill such a network the network must be able to tolerate slowstart
   bursts up to the full pipe size inflated by the anticipated window
   reduction on the first loss.  For example, with classic Reno
   congestion control, an optimal slowstart has to end with a burst that
   is twice the bottleneck rate for exactly one RTT in duration.  This
   burst causes a queue which is exactly equal to the pipe size (the
   window is exactly twice the pipe size) so when the window is halved,
   the new window will be exactly the pipe size.

   Another source of bursts are application pauses.  If the application
   pauses (stops reading or writing data) for some fraction of one RTT,
   state-of-the-art TCP to "catches up" to the earlier window size by
   sending a burst of data at the full sender interface rate.  To fill
   such a network with a realistic application, the network has to be
   able to tolerate interface rate bursts from the data sender large
   enough to cover the worst case application pause.

   Note that if the bottleneck data rate is significantly slower than
   the rest of the path, the slowstart bursts will not cause significant
   queues anywhere else along the path; they primarily exercise the
   queue at the dominant bottleneck.  Furthermore although the interface

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   rate bursts caused by the application are likely to be smaller than
   burst at the last RTT of slowstart, they are at a higher rate so they
   can exercise queues at arbitrary points along the "front path" from
   the data sender up to and including the queue at the bottleneck.

   For many network technologies a simple queueing model does not apply:
   the network schedules, thins or otherwise alters the ACKs and data
   stream, generally to raise the efficiency of the channel allocation
   process when confronted with relatively widely spaced ACKs.  These
   efficiency strategies are ubiquitous for wireless and other half
   duplex or broadcast media.

   Altering the ACK stream generally has two consequences: raising the
   effective bottleneck rate making slowstart burst at higher rates
   (possibly as high as the sender's interface rate) and effectively
   raising the RTT by the time that the ACKs were postponed.  The first
   effect can be partially mitigated by reclocking ACKs once they are
   through the bottleneck on the return to the sender, however this
   further raises the effective RTT.  The most extreme example of this
   class of behaviors is a half duplex channel that is never released
   until the current sender has no pending traffic.  Such environments
   intrinsically cause self clocked protocols revert to extremely
   inefficient stop and wait behavior, where they send an entire window
   of data as a single burst, followed by the entire window of ACKs on
   the return path.

   If a particular end-to-end path contains a link or device that alters
   the ACK stream, then the entire path from the sender up to the
   bottleneck must be tested at the burst parameters implied by the ACK
   scheduling algorithms.  The most important parameter is the Effective
   Bottleneck Data Rate, which is the average rate at which the ACKs
   advance snd.una.  Note that thinning the ACKs (relying on the
   cumulative nature of seg.ack to permit discarding some ACKs) is
   implies an effectively infinite bottleneck data rate.

   To verify that a path can meet the performance target, Model Based
   Metrics need to independently confirm that the entire path can
   tolerate bursts of the dimensions that are likely to be induced by
   the application and any data or ACK scheduling.  Two common cases are
   the most important: slowstart bursts of with more than the
   target_pipe_size data at twice the effective bottleneck data rate;
   and somewhat smaller sender interface rate bursts.

5.  Common Models and Parameters

   Transport performance models are used to derive the test parameters
   for test suites of simple diagnostics from the end-to-end target

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   parameters and additional ancillary parameters.

5.1.  Target End-to-end parameters

   The target end to end parameters are the target data rate, target RTT
   and target MTU as defined in Section 2 These parameters are
   determined by the needs of the application or the ultimate end user
   and the end-to-end Internet path.  They are in units that make sense
   to the upper layer: payload bytes delivered, excluding header
   overheads for IP, TCP and other protocol.

   Ancillary parameters include the effective bottleneck rate and the
   permitted number of connections (numb_cons).

   The use of multiple connections has been very controversial since the
   beginning of the World-Wide-Web[first complaint].  Modern browsers
   open many connections [BScope].  Experts associated with IETF
   transport area have frequently spoken against this practice [long
   list].  It is not inappropriate to assume some small number of
   concurrent connections (e.g. 4 or 6), to compensate for limitation in
   TCP.  However, choosing too large a number is at risk of being
   interpreted as a signal by the web browser community that this
   practice has been embraced by the Internet service provider
   community.  It may not be desirable to send such a signal.

5.2.  Common Model Calculations

   The most important derived parameter is target_pipe_size (in
   packets), which is the number of packets needed exactly meet the
   target rate, with numb_cons connections and no cross traffic for the
   specified target RTT and MTU.  It is given by:

   target_pipe_size = (target_rate / numb_cons) * target_RTT / (
   target_MTU - header_overhead )

   If the transport protocol (e.g.  TCP) average window size is smaller
   than this, it will not meet the target rate.

   The reference_target_run_length, which is the most conservative model
   for the minimum spacing between losses, can derived as follows:
   assume the link_data_rate is infinitesimally larger than the
   target_data_rate.  Then target_pipe_size also predicts the onset of
   queueing.  If the transport protocol (e.g.  TCP) has an average
   window size that is larger than the target_pipe_size, the excess
   packets will form a standing queue at the bottleneck.

   If the transport protocol is using standard Reno style Additive
   Increase, Multiplicative Decrease congestion control [RFC5681], then

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   there must be target_pipe_size roundtrips between losses.  Otherwise
   the multiplicative window reduction triggered by a loss would cause
   the network to be underfilled.  Following [MSMO97], we derive the
   losses must be no more frequent than every 1 in
   (3/2)(target_pipe_size^2) packets.  This provides the reference value
   for target_run_length which is typically the number of packets that
   must be delivered between loss episodes in the tests below:

   reference_target_run_length = (3/2)(target_pipe_size^2)

   Note that this calculation is based on a number of assumptions that
   may not apply.  Appendix A discusses these assumptions and provides
   some alternative models.  The actual method for computing
   target_run_length MUST be documented along with the rationale for the
   underlying assumptions and the ratio of chosen target_run_length to
   reference_target_run_length. @@@ MOVE

   Although this document gives a lot of latitude for calculating
   target_run_length, people designing suites of tests need to consider
   the effect of their choices on the ongoing conversation and tussle
   about the relevance of "TCP friendliness" as an appropriate model for
   capacity allocation.  Choosing a target_run_length that is
   substantially smaller than reference_target_run_length is equivalent
   to saying that it is appropriate for the transport research community
   to abandon "TCP friendliness" as a fairness model and to develop more
   aggressive Internet transport protocols, and for applications to
   continue (or even increase) the number of connections that they open
   concurrently.

   The calculations for individual parameters are presented with the
   each single property test.  In general these calculations permit some
   derating as described in Section 5.3.  For test parameters that can
   be derated and are proportional to target_pipe_size, it is
   recommended that the derating be specified relative to
   target_pipe_size calculations using numb_cons=1, although the
   derating may additionally be specified relative to the
   target_pipe_size common to other tests.

5.3.  Parameter Derating

   Since some aspects of the models are very conservative, the modeling
   framework permits some latitude in derating some specific test
   parameters.  For example classical performance models suggest that in
   order to be sure that a single TCP stream can fill a link, it needs
   to have a full bandwidth-delay-product worth of buffering at the
   bottleneck[QueueSize].  In real networks with real applications this
   is often overly conservative.  Rather than trying to formalize more
   complicated models we permit some test parameters to be relaxed as

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   long as they meet some additional procedural constraints:
   o  The method used compute and justify the derated metrics is
      published in such a way that it becomes a matter of public record.
      @@@ introduce earlier
   o  The calibration procedures described in Section 9 are used to
      demonstrate the feasibility of meeting the performance targets
      with the derated test parameters.
   o  The calibration process itself is documented is such a way that
      other researchers can duplicate the experiments and validate the
      results.

   In the test specifications in Section 7 assume 0 < derate <= 1, is a
   derating parameter.  These will be individually named in the final
   document.  In all cases making derate smaller makes the test more
   tolerant.  Derate = 1 is "full strenght".

   Note that some test parameters are not permitted to be derated.

6.  Common testing procedures

6.1.  Traffic generating techniques

6.1.1.  Paced transmission

   Paced (burst) transmissions: send bursts of data on a timer to meet a
   particular target rate and pattern.
   Single:  Send individual packets at the specified rate or headway.
   Burst:  Send sender interface rate bursts on a timer.  Specify any 3
      of average rate, packet size, burst size (number of packets) and
      burst headway (burst start to start).  These bursts are typically
      sent as back-to-back packets at the testers interface rate.
   Slowstart:  Send 4 packet sender interface rate bursts at an average
      rate equal to the minimum of twice effective bottleneck link rate
      or the sender interface rate.  This corresponds to the average
      rate during a TCP slowstart when Appropriate Byte Counting [ABC]
      is present or delayed ack is disabled.
   Repeated Slowstart:  Slowstart pacing itself is typically part of
      larger scale pattern of repeated bursts, such as sending
      target_pipe_size packets as slowstart bursts on a target_RTT
      headway (burst start to burst start).  Such a stream has three
      different average rates, depending on the averaging time scale.
      At the finest time scale the average rate is the same as the
      sender interface rate, at a medium scale the average rate is twice
      the bottleneck link rate and at the longest time scales the
      average rate is the target data rate, adjusted to include header
      overhead.

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   Note that if the effective bottleneck link rate is more than half of
   the sender interface rate, slowstart bursts become sender interface
   rate bursts.

6.1.2.  Constant window pseudo CBR

   Implement pseudo CBR by running a standard protocol such as TCP with
   a fixed window size.  This has the advantage that it can be
   implemented as part of real content delivery.  The rate is only
   maintained in average over each RTT, and is subject to limitations of
   the transport protocol.

   For tests that have strongly prescribed data rates, if the transport
   protocol fails to maintain the test rate for any reason related to
   the network itself, such as packet losses or congestion, the test
   should be considered inconclusive.  Otherwise there are some cases
   where tester failures might cause false negative link test results.

6.1.2.1.  Scanned window pseudo CBR

   Same as the above, except the window is incremented once per
   2*target_pipe_size, starting from below target_pipe[@@@ test pipe]
   and sweeping up to first loss or some other event.  This is analogous
   to the tests implemented in Windowed Ping [WPING] and pathdiag
   [Pathdiag]

6.1.3.  Intermittent Testing

   Any test which does not depend on queueing (e.g. the CBR tests) or
   experiences periodic zero outstanding data during normal operation
   (e.g. between bursts for burst tests), can be formulated as an
   intermittent test.

   The Intermittent testing can be used for ongoing monitoring for
   changes in subpath quality with minimal disruption users.  It should
   be used in conjunction with the full rate test because this method
   assesses an average_run_length over a long time interval w.r.t. user
   sessions.  It may false fail due to other legitimate congestion
   causing traffic or may false pass changes in underlying link
   properties (e.g. a modem retraining to an out of contract lower
   rate).

   [Need text about bias (false pass) in the shadow of loss caused by
   excessive bursts]

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6.1.4.  Intermittent Scatter Testing

   Intermittent scatter testing: when testing the network path to or
   from an ISP subscriber aggregation point (CMTS, DSLAM, etc),
   intermittent tests can be spread across a pool of users such that no
   one users experiences the full impact of the testing, even though the
   traffic to or from the ISP subscriber aggregation point is sustained
   at full rate.

6.2.  Interpreting the Results

6.2.1.  Test outcomes

   A singleton is a pass fail measurement.  If any subpath fails any
   test it can be assumed that the end-to-end path will also fail to
   attain the target performance under some conditions.

   In addition we use "inconclusive" outcome to indicate that a test
   failed to attain the required test conditions.  This is important to
   the extent that the tests themselves use protocols that have built in
   control systems which might interfere with some aspect of the test.
   For example consider a test is implemented by adding rate controls
   and instrumentation to TCP: failing to attain the specified data rate
   has to be treated an inconclusive, unless the test clearly fails
   (target_run_lenght is too small).  This is because failing to reach
   the target rate is an ambiguous signature for problems with either
   the test procedure (a problem with the TCP implementation or the test
   path RTT is too long) or the subpath itself.

   The vantage independence properties of Model Based Metrics depends on
   the accuracy of the distinction between failing and inconclusive
   tests.  One of the goals of evolving test designs will be to keep
   sharpening the distinction between failing and inconclusive tests.

   One of the goals of evolving the testing process, procedures and
   measurement point selection should be to minimize the number of
   inconclusive tests.

6.2.2.  Statistical criteria for measuring run_length

   When evaluating the observed run_length, we need to determine
   appropriate packet stream sizes and acceptable error levels to test
   efficiently.  In practice, can we compare the empirically estimated
   loss probabilities with the targets as the sample size grows?  How
   large a sample is needed to say that the measurements of packet
   transfer indicate a particular run-length is present?

   The generalized measurement can be described as recursive testing:

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   send a flight of packets and observe the packet transfer performance
   (loss ratio or other metric, any defect we define).

   As each flight is sent and measured, we have an ongoing estimate of
   the performance in terms of defect to total packet ratio (or an
   empirical probability).  Continue to send until conditions support a
   conclusion or a maximum sending limit has been reached.

   We have a target_defect_probability, 1 defect per target_run_length,
   where a "defect" is defined as a lost packet, a packet with ECN mark,
   or other impairment.  This constitutes the null Hypothesis:

   H0:  no more than one defects in target_run_length = (3/2)*(flight)^2
      packets

   and we can stop sending flights of packets if measurements support
   accepting H0 with the specified Type I error = alpha (= 0.05 for
   example).

   We also have an alternative Hypothesis to evaluate: if performance is
   significantly lower than the target_defect_probability, say half the
   target:

   H1:  one or more defects in target_run_length/2 packets

   and we can stop sending flights of packets if measurements support
   rejecting H0 with the specified Type II error = beta, thus preferring
   the alternate H1.

   H0 and H1 constitute the Success and Failure outcomes described
   elsewhere in the memo, and while the ongoing measurements do not
   support either hypothesis the current status of measurements is
   inconclusive.

   The problem above is formulated to match the Sequential Probability
   Ratio Test (SPRT) [StatQC] [temp ref:
   http://en.wikipedia.org/wiki/Sequential_probability_ratio_test ],
   which also starts with a pair of hypothesis specified as above:

   H0:  p = p0 = one defect in target_run_length
   H1:  p = p1 = one defect in target_run_length/2
   As flights are sent and measurements collected, the tester evaluates
   the cumulative log-likelihood ratio:

   S_i = S_i-1 + log(Lambda_i)

   where Lambda_i is the ratio of the two likelihood functions
   (calculated on the measurement at packet i, and index i increases

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   linearly over all flights of packets ) for p0 and p1 [temp ref:
   http://en.wikipedia.org/wiki/Likelihood_function ].

   The SPRT specifies simple stopping rules:

   o  a < S_i < b: continue testing
   o  S_i <= a: Accept H0
   o  S_i >= b: Accept H1
   where a and b are based on the Type I and II errors, alpha and beta:

   a ~= Log((beta/(1-alpha)) and b ~= Log((1-beta)/alpha)

   with the error probabilities decided beforehand, as above.

   The calculations above are implemented in the R-tool for Statistical
   Analysis, in the add-on package for Cross-Validation via Sequential
   Testing (CVST) [http://www.r-project.org/] [Rtool] [CVST] .

6.2.3.  Classifications of tests

   Tests are annotated with "(capacity)", "(engineering)" or
   "(monitoring)". @@@@MOVE to definitions?

   Capacity tests determine if a network subpath has sufficient capacity
   to deliver the target performance.  As such, they reflect parameters
   that can transition from passing to failing as a consequence of
   additional presented load or the actions of other network users.  By
   definition, capacity tests also consume network resources (capacity
   and/or buffer space), and their test schedules must be balanced by
   their cost.

   Monitoring tests are design to capture the most important aspects of
   a capacity test, but without causing unreasonable ongoing load
   themselves.  As such they may miss some details of the network
   performance, but can serve as a useful reduced cost proxy for a
   capacity test.

   Engineering tests evaluate how network algorithms (such as AQM and
   channel allocation) interact with transport protocols.  These tests
   are likely to have complicated interactions with other network
   traffic and can be inversely sensitive to load.  For example a test
   to verify that an AQM algorithm causes ECN marks or packet drops
   early enough to limit queue occupancy may experience a false pass
   results in the presence of bursty cross traffic.  It is important
   that engineering tests be performed under a wide range of conditions,
   including both in situ and bench testing, and under a variety of load
   conditions.  Ongoing monitoring is less likely to be useful for these
   tests, although sparse in situ testing might be appropriate.

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   @@@ Add single property vs combined tests here?

6.2.4.  Reordering Tolerance

   All tests must be instrumented for reordering [RFC4737].

   NB: there is no global consensus for how much reordering tolerance is
   appropriate or reasonable.  ("None" is absolutely unreasonable.)

   Section 5 of [RFC4737] proposed a metric that may be sufficient to
   designate isolated reordered packets as effectively lost, because
   TCP's retransmission response would be the same.

   [As a strawman, we propose the following:] TCP should be able to
   adapt to reordering as long as the reordering extent is no more than
   the maximum of one half window or 1 mS, whichever is larger.  Note
   that there is a fundamental tradeoff between tolerance to reordering
   and how quickly algorithms such as fast retransmit can repair losses.
   Within this limit on reorder extent, there should be no bound on
   reordering frequency.

   NB: Current TCP implementations are not compatible with this metric.
   We view this as bugs in current TCP implementations.

   Parameters:
   Reordering displacement:  the maximum of one half of target_pipe_size
      or 1 mS.

6.3.  Test Qualifications

   Things to monitor before, during and after a test.

6.3.1.  Verify the Traffic Generation Accuracy

   for most tests, failing to accurately generate the test traffic
   indicates an inconclusive tests, since it has to be presumed that the
   error in traffic generation might have affected the test outcome.  To
   the extent that the network itself had an effect on the the traffic
   generation (e.g. in the standing queue tests) the possibility exists
   that allowing too large of error margin in the traffic generation
   might introduce feedback loops that comprise the vantage independents
   properties of these tests.

   Parameters:

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   Maximum Data Rate Error  The permitted amount that the test traffic
      can be different than specified for the current test.  This is a
      symmetrical bound.
   Maximum Data Rate Overage  The permitted amount that the test traffic
      can be above than specified for the current test.
   Maximum Data Rate Underage  The permitted amount that the test
      traffic can be less than specified for the current test.

6.3.2.  Verify the absence of cross traffic

   The proper treatment of cross traffic is different for different
   subpaths.  In general when testing infrastructure which is associated
   with only one subscriber, the test should be treated as inconclusive
   it that subscriber is active on the network.  However, for shared
   infrastructure, the question at hand is likely to be testing if
   provider has sufficient total capacity.  In such cases the presence
   of cross traffic due to other subscribers is explicitly part of the
   network conditions and its effects are explicitly part of the test.

   Note that canceling tests due to load on subscriber lines may
   introduce sampling errors for testing other parts of the
   infrastructure.  For this reason tests that are scheduled but not run
   due to load should be treated as a special case of "inconclusive".

   Use a passive packet or SNMP monitoring to verify that the traffic
   volume on the subpath agrees with the traffic generated by a test.
   Ideally this should be performed before during and after each test.

   The goal is provide quality assurance on the overall measurement
   process, and specifically to detect the following measurement
   failure: a user observes unexpectedly poor application performance,
   the ISP observes that the access link is running at the rated
   capacity.  Both fail to observe that the user's computer has been
   infected by a virus which is spewing traffic as fast as it can.

   Parameters:
   Maximum Cross Traffic Data Rate  The amount of excess traffic
      permitted.  Note that this will be different for different tests.

   One possible method is an adaptation of: www-didc.lbl.gov/papers/
   SCNM-PAM03.pdf D Agarwal etal.  "An Infrastructure for Passive
   Network Monitoring of Application Data Streams".  Use the same
   technique as that paper to trigger the capture of SNMP statistics for
   the link.

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6.3.3.  Additional test preconditions

   Send pre-load traffic as needed to activate radios with a sleep mode,
   or other "reactive network" elements (term defined in
   [draft-morton-ippm-2330-update-01]).

   Use the procedure above to confirm that the pre-test background
   traffic is low enough.

7.  Single Property Tests

7.1.  Basic Data and Loss Rate Tests

   We propose several versions of the loss rate test.  All are rate
   controlled at or below the target_data_rate.  The first, performed at
   constant full data rate, is intrusive and recommend for infrequent
   testing, such as when a service is first turned up or as part of an
   auditing process.  The second, background loss rate, is designed for
   ongoing monitoring for change is subpath quality.

7.1.1.  Loss Rate at Paced Full Data Rate

   Confirm that the observed run length is at least the
   target_run_lenght while sending at the target_rate.  This test
   implicitly confirms that sub_path has sufficient raw capacity to
   carry the target_data_rate.  This version of the loss rate test
   relies on timers to schedule data transmission at a true constant bit
   rate (CBR).

   Test Parameters:
   Run Length  Same as target_run_lenght
   Data Rate  Same as target_data_rate
   Maximum Cross Traffic  A specified small fraction of
      target_data_rate.

   Note that target_run_lenght and target_data_rate parameters MUST NOT
   be derated.  If the default parameters are too stringent an alternate
   model as described in Appendix A can be used to compute
   target_run_lenght.

   The test traffic is sent using the procedures in Section 6.1.1 at
   target_data_rate with a burst size of 1, subject to the
   qualifications in Section 6.3.  The receiver accumulates packet
   delivery statistics as described in Section 6.2 to score the outcome:

   Pass: it is statistically significantly that the observed run length
   is larger than the target_run_length.

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   Fail: it is statistically significantly that the observed run length
   is smaller than the target_run_length.

   Inconclusive: The test failed to meet the qualifications defined in
   Section 6.3 or neither test was statistically significant.

7.1.2.  Loss Rate at Full Data Windowed Rate

   Confirm that the observed run length is at least the
   target_run_lenght while sending at the target_rate.  This test
   implicitly confirms that sub_path has sufficient raw capacity to
   carry the target_data_rate.  This version of the loss rate test
   relies on a fixed window to self clock data transmission into the
   network.  This is more authentic.

   Test Parameters:
   Run Length  Same as target_run_lenght
   Data Rate  Same as target_data_rate
   Maximum Cross Traffic  A specified small fraction of
      target_data_rate.

   Note that target_run_lenght and target_data_rate parameters MUST NOT
   be derated.  If the default parameters are too stringent an alternate
   model as described in Appendix A can be used to compute
   target_run_lenght.

   The test traffic is sent using the procedures in Section 6.1.1 at
   target_data_rate with a burst size of 1, subject to the
   qualifications in Section 6.3.  The receiver accumulates packet
   delivery statistics as described in Section 6.2 to score the outcome:

   Pass: it is statistically significantly that the observed run length
   is larger than the target_run_length.

   Fail: it is statistically significantly that the observed run length
   is smaller than the target_run_length.

   Inconclusive: The test failed to meet the qualifications defined in
   Section 6.3 or neither test was statistically significant.

7.1.3.  Background Loss Rate Tests

   The background loss rate is a low rate version of the target rate
   test above, designed for ongoing monitoring for changes in subpath
   quality without disrupting users.  It should be used in conjunction
   with the above full rate test because it may be subject to false
   results under some conditions, in particular it may false pass
   changes in underlying link properties (e.g. a modem retraining to an

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   out of contract lower rate).

   Parameters:
   Run Length  Same as target_run_lenght
   Data Rate  Some small fraction of target_data_rate, such as 1%.

   Once the preconditions described in Section 6.3 are met, the test
   data is sent at the prescribed rate with a burst size of 1.  The
   receiver accumulates packet delivery statistics and the procedures
   described in Section 6.2.1 and Section 6.3 are used to score the
   outcome:

   Pass: it is statistically significantly that the observed run length
   is larger than the target_run_length.

   Fail: it is statistically significantly that the observed run length
   is smaller than the target_run_length.

   Inconclusive: Neither test was statistically significant or there was
   excess cross traffic during the test.

7.2.  Standing Queue tests

   These test confirm that the bottleneck is well behaved across the
   onset of queueing.  For conventional bottlenecks this will be from
   the onset of queuing to the point where there is a full target_pipe
   of standing data.  Well behaved generally means lossless for
   target_run_length, followed by a small number of losses to signal to
   the transport protocol that it should slow down.  Losses that are too
   early can prevent the transport from averaging above the target_rate.
   Losses that are too late indicate that the queue might be subject to
   bufferbloat and subject other flows to excess queuing delay.  Excess
   losses (more than half of of target_pipe) make loss recovery
   problematic for the transport protcol.

   These tests can also observe some problems with channel acquisition
   systems, especially at the onset of persistent queueing.  Details
   TBD.

7.2.1.  Congestion Avoidance

   Use the procedure in Section 6.1.2.1 to sweep the window (rate) from
   below link_pipe up to beyond target_pipe+link_pipe.  Depending on
   events that happen during the scan, score the link.  Identify the
   power_point=MAX(rate/RTT) as the start of the test.

   Fail if first loss is too early (loss rate too high) on repeated
   tests or if the losses are more than half of the outstanding data. (a

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   capacity test)

7.2.2.  Buffer Bloat

   Use the procedure in Section 6.1.2.1 to sweep the window (rate) from
   below link_pipe up to beyond target_pipe+link_pipe.  Depending on
   events that happen during the scan, score the link.  Identify the
   "power point:MAX(rate/RTT) as the start of the test (should be
   window=target_pipe)

   Fail if first loss is too late (insufficient AQM and subject to
   bufferbloat - an engineering test).  NO THEORY

7.2.3.  Duplex Self Interference

   Use the procedure in Section 6.1.2.1 to sweep the window (rate) from
   below link_pipe up to beyond target_pipe+required_queue.  Depending
   on events that happen during the scan, score the link.  Identify the
   "power point:MAX(rate/RTT) as the start of the test (should be
   window=target_pipe) @@@ add required_queue and power_point

   Fail if RTT is non-monotonic by more than a small number of packet
   times (channel allocation self interference - engineering) IS THIS
   SUFFICIENT?

7.3.  Slowstart tests

   These tests mimic slowstart: data is sent at slowstart_rate (twice
   subpath_rate).  They are deemed inconclusive if the elapsed time to
   send the data burst is not less than half of the (extrapolated) time
   to receive the ACKs. (i.e. sending data too fast is ok, but sending
   it slower than twice the actual bottleneck rate is deemed
   inconclusive).  Space the bursts such that the average ACK rate is
   equal to or faster than the target_data_rate.

   These tests are not useful at burst sizes smaller than the sender
   interface rate tests, since the sender interface rate tests are more
   strenuous.  If it is necessary to derate the sender interface rate
   tests, then the full window slowstart test (un-derated) would be
   important.

7.3.1.  Full Window slowstart test

   Send (target_pipe_size+required_queue)*derate bursts must have fewer
   than one loss per target_run_length*derate.  Note that these are the
   same parameters as the Sender Full Window burst test, except the
   burst rate is at slowestart rate, rather than sender interface rate.
   SHOULD derate=1.

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   Otherwise TCP will exit from slowstart prematurely, and only reach a
   full target_pipe_size window by way of congestion avoidance.

   This is a capacity test: cross traffic may cause premature losses.

7.3.2.  Slowstart AQM test

   Do a continuous slowstart (date rate = slowstart_rate), until first
   loss, and repeat, gathering statistics on the last delivered packet's
   RTT and window size.  Fail if too large (NO THEORY for value).

   This is an engineering test: It would be best performed on a
   quiescent network or testbed, since cross traffic might cause a false
   pass.

7.4.  Sender Rate Burst tests

   These tests us "sender interface rate" bursts.  Although this is not
   well defined it should be assumed to be current state of the art
   server grade hardware (often 10Gb/s today). (load)

7.4.1.  Sender TCP Send Offload (TSO) tests

   If MIN(target_pipe_size, 42) packet bursts meet target_run_lenght
   (Not derated!).

   Otherwise the link will interact badly with modern server NIC
   implementations, which as an optimization to reduce host side
   interactions (interrupts etc) accept up to 64kB super packets and
   send them as 42 seperate packets on the wire side.cc (load)

7.4.2.  Sender Full Window burst test

   target_pipe_size*derate bursts have fewer than one loss per
   target_run_length*derate.

   Otherwise application pauses will cause unwarranted losses.  Current
   standards permit TCP to send a full cwnd burst following an
   application pause.  (Cwnd validation in not required, but even so
   does not take effect until the pause is longer than RTO).

   NB: there is no model here for what is good enough. derate=1 is
   safest, but may be unnecessarily conservative for some applications.
   Some application, such as streaming video need derate=1 to be
   efficient when the application pacing quanta is larger than cwnd.
   (load)

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8.  Combined Tests

   These tests are more efficient from a deployment/operational
   perspective, but may not be possible to diagnose if they fail.

8.1.  Sustained burst test

   Send target_pipe_size sender interface rate bursts every target_RTT,
   verify that the observed run length meets target_run_length.  Key
   observations:
   o  This test is RTT invariant, as long as the tester can generate the
      required pattern.
   o  The subpath under test is expected to go idle for some fraction of
      the time: (link_rate-target_rate)/link_rate.  Failing to do so
      suggests a problem with the procedure.
   o  This test is more strenuous than the slowstart tests: they are not
      needed if the link passes underated sender interface rate burst
      tests.
   o  This test could be derated by reducing both the burst size and
      headway (same average data rate).
   o  A link that passes this test is likely to be able to sustain
      higher rates (close to link_rate) for paths with RTTs smaller than
      the target_RTT.  Offsetting this performance underestimation is
      the rationale behind permitting derating in general.
   o  This test should be implementable with standard instrumented TCP,
      [RFC 4898] using a specialized measurement application at one end
      and a minimal service at the other end [RFC 863, RFC 864].  It may
      require tweaks to the TCP implementation.
   o  This test is efficient to implement, since it does not require
      per-packet timers, and can make maximal use of TSO in modern NIC
      hardware.
   o  This test is not totally sufficient: the standing window
      engineering tests are also needed to be sure that the link is well
      behaved at and beyond the onset of congestion.
   o  I believe that this test can be proven to be the one capacity test
      to supplant them all.

   Example

   To confirm that a 100 Mb/s link can reliably deliver single 10
   MByte/s stream at a distance of 50 mS, test the link by sending 346
   packet bursts every 50 mS (10 MByte/s payload rate, assuming a 1500
   Byte IP MTU and 52 Byte TCP/IP headers).  These bursts are 4196288
   bits on the wire (assuming 16 bytes of link overhead and framing) for
   an aggregate test data rate of 8.4 Mb/s.

   To pass the test using the most conservative TCP model for a single
   stream the observed run length must be larger than 179574 packets.

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   This is the same as less than one loss per 519 bursts (1.5*346) or
   every 26 seconds.

   Note that this test potentially cause transient 346 packet queues at
   the bottleneck.

9.  Calibration

   If using derated metrics, or when something goes wrong, the results
   must be calibrated against a traditional BTC.  The preferred
   diagnostic follow-up to calibration issues is to run open end-to-end
   measurements on an open platform, such as Measurement Lab
   [http://www.measurementlab.net/]

10.  Acknowledgements

   Ganga Maguluri suggested the statistical test for measuring loss
   probability in the target run length.

   Meredith Whittaker for improving the clarity of the communications.

11.  Informative References

   [RFC2330]  Paxson, V., Almes, G., Mahdavi, J., and M. Mathis,
              "Framework for IP Performance Metrics", RFC 2330,
              May 1998.

   [RFC4737]  Morton, A., Ciavattone, L., Ramachandran, G., Shalunov,
              S., and J. Perser, "Packet Reordering Metrics", RFC 4737,
              November 2006.

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

   [RFC5835]  Morton, A. and S. Van den Berghe, "Framework for Metric
              Composition", RFC 5835, April 2010.

   [RFC6049]  Morton, A. and E. Stephan, "Spatial Composition of
              Metrics", RFC 6049, January 2011.

   [I-D.morton-ippm-lmap-path]
              Bagnulo, M., Burbridge, T., Crawford, S., Eardley, P., and
              A. Morton, "A Reference Path and Measurement Points for
              LMAP", draft-morton-ippm-lmap-path-00 (work in progress),
              January 2013.

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   [MSMO97]   Mathis, M., Semke, J., Mahdavi, J., and T. Ott, "The
              Macroscopic Behavior of the TCP Congestion Avoidance
              Algorithm", Computer Communications Review volume 27,
              number3, July 1997.

   [WPING]    Mathis, M., "Windowed Ping: An IP Level Performance
              Diagnostic", INET 94, June 1994.

   [Pathdiag]
              Mathis, M., Heffner, J., O'Neil, P., and P. Siemsen,
              "Pathdiag: Automated TCP Diagnosis", Passive and Active
              Measurement , June 2008.

   [BScope]   Broswerscope, "Browserscope Network tests", Sept 2012,
              <http://www.browserscope.org/?category=network>.

   [Rtool]    R Development Core Team, "R: A language and environment
              for statistical computing. R Foundation for Statistical
              Computing, Vienna, Austria. ISBN 3-900051-07-0, URL
              http://www.R-project.org/",  , 2011.

   [StatQC]   Montgomery, D., "Introduction to Statistical Quality
              Control - 2nd ed.", ISBN 0-471-51988-X, 1990.

   [CVST]     Krueger, T. and M. Braun, "R package: Fast Cross-
              Validation via Sequential Testing", version 0.1, 11 2012.

Appendix A.  Model Derivations

   This appendix describes several different ways to calculate
   target_run_length and the implication of the chosen calculation.

   Rederive MSMO97 under two different assumptions: target_rate =
   link_rate and target_rate < 2 * link_rate.

   Show equivalent derivation for CUBIC.

   Commentary on the consequence of the choice.

Appendix B.  old text

   This entire section is contains scraps of text to be moved, removed
   or absorbed elsewhere in the document

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B.1.  An earlier document

   Step 0: select target end-to-end parameters: a target rate and target
   RTT.  The primary test will be to confirm that the link quality is
   sufficient to meet the specified target rate for the link under test,
   when extended to the target RTT by an ideal network.  The target rate
   must be below the actual link rate and nominally the target RTT would
   be longer than the link RTT.  There should probably be a convention
   for the relationship between link and target rates (e.g. 85%).

   For example on a 10 Mb/s link, the target rate might be 1 MBytes/s,
   at an RTT of 100 mS (a typical continental scale path).

   Step 1: On the basis of the target rate and RTT and your favorite TCP
   performance model, compute the "required run length", which is the
   required number of consecutive non-losses between loss episodes.  The
   run length resembles one over the loss probability, if clustered
   losses only count as a single event.  Also select "test duration" and
   "test rate".  The latter would nominally the same as the target rate,
   but might be different in some situations.  There must be
   documentation connecting the test rate, duration and required run
   length, to the target rate and RTT selected in step 0.

   Continuing the above example: Assuming a 1500 Byte MTU.  The
   calculated model loss rate for a single TCP stream is about 0.01% (1
   loss in 1E4 packets).

   Step 2, the actual measurement proceeds as follows: Start an
   unconstrained bulk data flow using any modern TCP (with large buffers
   and/or autotuning).  During the first interval (no rate limits)
   observe the slowstart (e.g. tcpdump) and measure: Peak burst size;
   link clock rate (delivery rate for each round); peak data rate for
   the fastest single RTT interval; fraction of segments lost at the end
   of slowstart.  After the flow has fully recovered from the slowstart
   (details not important) throttle the flow down to the test rate (by
   clamping cwnd or application pacing at the sender or receiver).
   While clamped to the test rate, observe the losses (run length) for
   the chosen test duration.  The link passes the test if the slowstart
   ends with less than approximately 50% losses and no timeouts, the
   peak rate is at least the target rate, and the measured run length is
   better than the required run length.  There will also need to be some
   ancillary metrics, for example to discard tests where the receiver
   closes the window, invalidating the slowstart test.  [This needs to
   be separated into multiple subtests]

   Optional step 3: In some cases it might make sense to compute an
   "extrapolated rate", which is the minimum of the observed peak rate,
   and the rate computed from the specified target RTT and the observed

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   run length by using a suitable TCP performance model.  The
   extrapolated rate should be annotated to indicate if it was run
   length or peak rate limited, since these have different predictive
   values.

   Other issues:

   If the link RTT is not substantially smaller than the target RTT and
   the actual run length is close to the target rate, a standards
   compliant TCP implementation might not be effective at accurately
   controlling the data rate.  To be independent of the details of the
   TCP implementation, failing to control the rate has to be treated as
   a spoiled measurement, not a infrastructure failure.  This can be
   overcome by "stiffening" TCP by using a non-standard congestion
   control algorithm.  For example if the rate controlling by clamping
   cwnd then use "relentless TCP" style reductions on loss, and lock
   ssthresh to the cwnd clamp.  Alternatively, implement an explicit
   rate controller for TCP.  In either case the test must be abandoned
   (aborted) if the measured run length is substantially below the
   target run length.

   If the test is run "in situ" in a production environment, there also
   needs to be baseline tests using alternate paths to confirm that
   there are no bottlenecks or congested links between the test end
   points and the link under test.

   It might make sense to run multiple tests with different parameters,
   for example infrequent tests with test rate equal to the target rate,
   and more frequent, less disruptive tests with the same target rate
   but the test rate equal to 1% of the target rate.  To observe the
   required run length, the low rate test would take 100 times longer to
   run.

   Returning to the example: a full rate test would entail sending 690
   pps (1 MByte/s) for several tens of seconds (e.g. 50k packets), and
   observing that the total loss rate is below 1:1e4.  A less disruptive
   test might be to send at 6.9 pps for 100 times longer, and observing

B.2.  End-to-end parameters from subpaths

   [This entire section needs to be overhauled and should be skipped on
   a first reading.  The concepts defined here are not used elsewhere.]

   The following optional parameters apply for testing generalized end-
   to-end paths that include subpaths with known specific types of
   behaviors that are not well represented by simple queueing models:

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   Bottleneck link clock rate:  This applies to links that are using
      virtual queues or other techniques to police or shape users
      traffic at lower rates full link rate.  The bottleneck link clock
      rate should be representative of queue drain times for short
      bursts of packets on an otherwise unloaded link.
   Channel hold time:  For channels that have relatively expensive
      channel arbitration algorithms, this is the typical (maximum?)
      time that data and or ACKs are held pending acquiring the channel.
      While under heavy load, the RTT may be inflated by this parameter,
      unless it is built into the target RTT
   Preload traffic volume:  If the user's traffic is shaped on the basis
      of average traffic volume, this is volume necessary to invoke
      "heavy hitter" policies.
   Unloaded traffic volume:  If the user's traffic is shaped on the
      basis of average traffic volume, this is the maximum traffic
      volume that a test can use and stay within a "light user"
      policies.

   Note on a ConEx enabled network [ConEx], the word "traffic" in the
   last two items should be replaced by "congestion" i.e. "preload
   congestion volume" and "unloaded congestion volume".

B.3.  Per subpath parameters

   [This entire section needs to be overhauled and should be skipped on
   a first reading.  The concepts defined here are not used elsewhere.]

   Some single parameter tests also need parameter of the subpath.

   subpath RTT:  RTT of the subpath under test.
   subpath link clock rate:  If different than the Bottleneck link clock
      rate

B.4.  Version Control

   Formatted: Fri Jun 21 18:23:29 PDT 2013

Authors' Addresses

   Matt Mathis
   Google, Inc
   1600 Amphitheater Parkway
   Mountain View, California  93117
   USA

   Email: mattmathis@google.com

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   Al Morton
   AT&T Labs
   200 Laurel Avenue South
   Middletown, NJ  07748
   USA

   Phone: +1 732 420 1571
   Email: acmorton@att.com
   URI:   http://home.comcast.net/~acmacm/

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