IP Performance Working Group                                   M. Mathis
Internet-Draft                                               Google, Inc
Intended status: Experimental                                  A. Morton
Expires: April 21, 2016                                        AT&T Labs
                                                            Oct 19, 2015

            Model Based Metrics for Bulk Transport Capacity


   We introduce a new class of Model Based Metrics designed to assess if
   a complete Internet path can be expected to meet a predefined Target
   Transport Performance by applying a suite of IP diagnostic tests to
   successive subpaths.  The subpath-at-a-time tests can be robustly
   applied to key infrastructure, such as interconnects or even
   individual devices, to accurately detect if any part of the
   infrastructure will prevent paths traversing it from meeting the
   Target Transport Performance.

   For Bulk Transport Capacity, the IP diagnostics are built on test
   streams that mimic TCP over the complete path and statistical
   criteria for evaluating the packet transfer statistics of those
   streams.  The temporal structure of the test stream (bursts, etc)
   mimic TCP or other transport protocol carrying bulk data over a long
   path but are constructed to be independent of the details of the
   subpath under test, end systems or applications.  Likewise the
   success criteria evaluates the packet transfer statistics of the
   subpath against criteria determined by protocol performance models
   applied to the Target Transport Performance of the complete path.
   The success criteria also does not depend on the details of the
   subpath, end systems or application.

   Model Based Metrics exhibit several important new properties not
   present in other Bulk Transport Capacity Metrics, including the
   ability to reason about concatenated or overlapping subpaths.  The
   results are vantage independent which is critical for supporting
   independent validation of tests by comparing results from multiple
   measurement points.

   This document does not define the IP diagnostic tests, but provides a
   framework for designing suites of IP diagnostic tests that are
   tailored to confirming that infrastructure can meet the predetermined
   Target Transport Performance.

Status of this Memo

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Table of Contents

   1.  Introduction . . . . . . . . . . . . . . . . . . . . . . . . .  5
     1.1.  Version Control  . . . . . . . . . . . . . . . . . . . . .  6
   2.  Overview . . . . . . . . . . . . . . . . . . . . . . . . . . .  7
   3.  Terminology  . . . . . . . . . . . . . . . . . . . . . . . . .  9
   4.  Background . . . . . . . . . . . . . . . . . . . . . . . . . . 16
     4.1.  TCP properties . . . . . . . . . . . . . . . . . . . . . . 17
     4.2.  Diagnostic Approach  . . . . . . . . . . . . . . . . . . . 19
     4.3.  New requirements relative to RFC 2330  . . . . . . . . . . 19
   5.  Common Models and Parameters . . . . . . . . . . . . . . . . . 20
     5.1.  Target End-to-end parameters . . . . . . . . . . . . . . . 20
     5.2.  Common Model Calculations  . . . . . . . . . . . . . . . . 21
     5.3.  Parameter Derating . . . . . . . . . . . . . . . . . . . . 22
     5.4.  Test Preconditions . . . . . . . . . . . . . . . . . . . . 22
   6.  Generating test streams  . . . . . . . . . . . . . . . . . . . 23
     6.1.  Mimicking slowstart  . . . . . . . . . . . . . . . . . . . 24
     6.2.  Constant window pseudo CBR . . . . . . . . . . . . . . . . 25
     6.3.  Scanned window pseudo CBR  . . . . . . . . . . . . . . . . 25
     6.4.  Concurrent or channelized testing  . . . . . . . . . . . . 26
   7.  Interpreting the Results . . . . . . . . . . . . . . . . . . . 27
     7.1.  Test outcomes  . . . . . . . . . . . . . . . . . . . . . . 27
     7.2.  Statistical criteria for estimating run_length . . . . . . 29
     7.3.  Reordering Tolerance . . . . . . . . . . . . . . . . . . . 31
   8.  IP Diagnostic Tests  . . . . . . . . . . . . . . . . . . . . . 31
     8.1.  Basic Data Rate and Packet Transfer Tests  . . . . . . . . 32
       8.1.1.  Delivery Statistics at Paced Full Data Rate  . . . . . 32
       8.1.2.  Delivery Statistics at Full Data Windowed Rate . . . . 33
       8.1.3.  Background Packet Transfer Statistics Tests  . . . . . 33
     8.2.  Standing Queue Tests . . . . . . . . . . . . . . . . . . . 33
       8.2.1.  Congestion Avoidance . . . . . . . . . . . . . . . . . 35
       8.2.2.  Bufferbloat  . . . . . . . . . . . . . . . . . . . . . 35
       8.2.3.  Non excessive loss . . . . . . . . . . . . . . . . . . 35
       8.2.4.  Duplex Self Interference . . . . . . . . . . . . . . . 36
     8.3.  Slowstart tests  . . . . . . . . . . . . . . . . . . . . . 36
       8.3.1.  Full Window slowstart test . . . . . . . . . . . . . . 36
       8.3.2.  Slowstart AQM test . . . . . . . . . . . . . . . . . . 37
     8.4.  Sender Rate Burst tests  . . . . . . . . . . . . . . . . . 37
     8.5.  Combined and Implicit Tests  . . . . . . . . . . . . . . . 38
       8.5.1.  Sustained Bursts Test  . . . . . . . . . . . . . . . . 38
       8.5.2.  Streaming Media  . . . . . . . . . . . . . . . . . . . 39
   9.  An Example . . . . . . . . . . . . . . . . . . . . . . . . . . 40
   10. Validation . . . . . . . . . . . . . . . . . . . . . . . . . . 41
   11. Security Considerations  . . . . . . . . . . . . . . . . . . . 42
   12. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 43
   13. IANA Considerations  . . . . . . . . . . . . . . . . . . . . . 43
   14. References . . . . . . . . . . . . . . . . . . . . . . . . . . 43
     14.1. Normative References . . . . . . . . . . . . . . . . . . . 43

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     14.2. Informative References . . . . . . . . . . . . . . . . . . 44
   Appendix A.  Model Derivations . . . . . . . . . . . . . . . . . . 46
     A.1.  Queueless Reno . . . . . . . . . . . . . . . . . . . . . . 47
   Appendix B.  The effects of ACK scheduling . . . . . . . . . . . . 48
   Appendix C.  Version Control . . . . . . . . . . . . . . . . . . . 49
   Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 49

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1.  Introduction

   Model Based Metrics (MBM) rely on mathematical models to specify a
   Targeted Suite of IP Diagnostic tests, designed to assess whether
   common transport protocols can be expected to meet a predetermined
   Target Transport Performance over an Internet path.  This note
   describes the modeling framework to derive the test parameters for
   accessing an Internet path's ability to support a predetermined Bulk
   Transport Capacity.

   Each test in the Targeted IP Diagnostic Suite (TIDS) measures some
   aspect of IP packet transfer needed to meet the Target Transport
   Performance.  For Bulk Transport Capacity the TIDS includes IP
   diagnostic tests to verify that there is: sufficient IP capacity
   (data rate); sufficient queue space at bottlenecks to absorb and
   deliver typical transport bursts; and that the background packet loss
   ratio is low enough not to interfere with congestion control; and
   other properties described below.  Unlike typical IPPM metrics which
   yield measures of network properties, Model Based Metrics nominally
   yield pass/fail evaluations of the ability of standard transport
   protocols to meet the specific performance objective over some
   network path.

   In most cases the IP diagnostic tests can be implemented by combining
   existing IPPM metrics with additional controls for generating test
   streams having a specified temporal structure (busts or standing
   queues, etc) and statistical criteria for evaluating packet transfer.
   The temporal structure of the test streams mimic transport protocol
   behavior over the complete path, the statistical criteria models the
   transport protocol's response to less than ideal IP packet transfer.

   This note describes an alternative to the approach presented in "A
   Framework for Defining Empirical Bulk Transfer Capacity Metrics"
   [RFC3148].  In the future, other Model Based Metrics may cover other
   applications and transports, such as VoIP over RTP.

   The MBM approach, mapping Target Transport Performance to a Targeted
   IP Diagnostic Suite (TIDS) of IP tests, solves some intrinsic
   problems with using TCP or other throughput maximizing protocols for
   measurement.  In particular all throughput maximizing protocols (and
   TCP congestion control in particular) cause some level of congestion
   in order to detect when they have filled the network.  This self
   inflicted congestion obscures the network properties of interest and
   introduces non-linear equilibrium behaviors that make any resulting
   measurements useless as metrics because they have no predictive value
   for conditions or paths different than that of the measurement
   itself.  These problems are discussed at length in Section 4.

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   A Targeted IP Diagnostic Suite does not have such difficulties.  IP
   diagnostics can be constructed such that they make strong statistical
   statements about path properties that are independent of the
   measurement details, such as vantage and choice of measurement
   points.  Model Based Metrics are designed to bridge the gap between
   empirical IP measurements and expected TCP performance.

1.1.  Version Control

   RFC Editor: Please remove this entire subsection prior to

   Please send comments about 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: Mon Oct 19 15:59:51 PDT 2015

   Changes since -06 draft:
   o  More language nits:
      *  "Targeted IP Diagnostic Suite (TIDS)" replaces "Targeted
         Diagnostic Suite (TDS)".
      *  "implied bottleneck IP capacity" replaces "implied bottleneck
         IP rate".
      *  Updated to ECN CE Marks.
      *  Added "specified temporal structure"
      *  "test stream" replaces "test traffic"
      *  "packet transfer" replaces "packet delivery"
      *  Reworked discussion of slowstart, bursts and pacing.
      *  RFC 7567 replaces RFC 2309.

   Changes since -05 draft:
   o  Wordsmithing on sections overhauled in -05 draft.
   o  Reorganized the document:
      *  Relocated subsection "Preconditions".
      *  Relocated subsection "New Requirements relative to RFC 2330".
   o  Addressed nits and not so nits by Ruediger Geib.  (Thanks!)
   o  Substantially tightened the entire definitions section.
   o  Many terminology changes, to better conform to other docs :
      *  IP rate and IP capacity (following RFC 5136) replaces various
         forms of link data rate.
      *  subpath replaces link.
      *  target_window_size replaces target_pipe_size.
      *  implied bottleneck IP rate replaces effective bottleneck link
      *  Packet delivery statistics replaces delivery statistics.

   Changes since -04 draft:

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   o  The introduction was heavily overhauled: split into a separate
      introduction and overview.
   o  The new shorter introduction:
      *  Is a problem statement;
      *  This document provides a framework;
      *  That it replaces TCP measurement by IP tests;
      *  That the results are pass/fail.
   o  Added a diagram of the framework to the overview
   o  and introduces all of the elements of the framework.
   o  Renumbered sections, reducing the depth of some section numbers.
   o  Updated definitions to better agree with other documents:
      *  Reordered section 2
      *  Bulk [data] performance -> Bulk Transport Capacity, everywhere
         including the title.
      *  loss rate and loss probability -> packet loss ratio
      *  end-to-end path -> complete path
      *  [end-to-end][target] performance -> Target Transport
      *  load test -> capacity test

2.  Overview

   This document describes a modeling framework for deriving a Targeted
   IP Diagnostic Suite from a predetermined Target Transport
   Performance.  It is not a complete specification, and relies on other
   standards documents to define important details such as packet type-p
   selection, sampling techniques, vantage selection, etc.  We imagine
   Fully Specified Targeted IP Diagnostic Suites (FSTIDS), that define
   all of these details.  We use Targeted IP Diagnostic Suite (TIDS) to
   refer to the subset of such a specification that is in scope for this
   document.  This terminology is defined in Section 3.

   Section 4 describes some key aspects of TCP behavior and what they
   imply about the requirements for IP packet transfer.  Most of the IP
   diagnostic tests needed to confirm that the path meets these
   properties can be built on existing IPPM metrics, with the addition
   of statistical criteria for evaluating packet transfer and in a few
   cases, new mechanisms to implement the required temporal structure.
   (One group of tests, the standing queue tests described in
   Section 8.2, don't correspond to existing IPPM metrics, but suitable
   metrics can be patterned after existing tools.)

   Figure 1 shows the MBM modeling and measurement framework.  The
   Target Transport Performance, at the top of the figure, is determined
   by the needs of the user or application, outside the scope of this
   document.  For Bulk Transport Capacity, the main performance
   parameter of interest is the Target Data Rate.  However, since TCP's

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   ability to compensate for less than ideal network conditions is
   fundamentally affected by the Round Trip Time (RTT) and the Maximum
   Transmission Unit (MTU) of the complete path, these parameters must
   also be specified in advance based on knowledge about the intended
   application setting.  They may reflect a specific application over
   real path through the Internet or an idealized application and
   hypothetical path representing a typical user community.  Section 5
   describes the common parameters and models derived from the Target
   Transport Performance.

               Target Transport Performance
     (Target Data Rate, Target RTT and Target MTU)
                    |  mathematical  |
                    |     models     |
                    |                |
   Traffic parameters |            | Statistical criteria
                      |            |
              |       |   * * *    | Diagnostic Suite  |
         _____|_______V____________V________________   |
       __|____________V____________V______________  |  |
       |           IP diagnostic test             | |  |
       |              |            |              | |  |
       | _____________V__        __V____________  | |  |
       | |     test     |        |   Delivery  |  | |  |
       | |    stream    |        |  Evaluation |  | |  |
       | |  generation  |        |             |  | |  |
       | -------v--------        ------^--------  | |  |
       |   |    v    test stream via   ^      |   | |--
       |   |  -->======================>--    |   | |
       |   |       subpath under test         |   |-
       ----V----------------------------------V--- |
           | |  |                             | |  |
           V V  V                             V V  V
       fail/inconclusive            pass/fail/inconclusive

   Overall Modeling Framework

                                 Figure 1

   The mathematical models are used to design traffic patterns that
   mimic TCP or other transport protocol delivering bulk data and
   operating at the Target Data Rate, MTU and RTT over a full range of
   conditions, including flows that are bursty at multiple time scales.
   The traffic patterns are generated based on the three target

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   parameters of complete path and independent of the properties of
   individual subpaths using the techniques described in Section 6.  As
   much as possible the measurement traffic is generated
   deterministically (precomputed) to minimize the extent to which test
   methodology, measurement points, measurement vantage or path
   partitioning affect the details of the measurement traffic.

   Section 7 describes packet transfer statistics and methods test them
   against the bounds provided by the mathematical models.  Since these
   statistics are typically the composition of subpaths of the complete
   path [RFC6049] , in situ testing requires that the end-to-end
   statistical bounds be apportioned as separate bounds for each
   subpath.  Subpaths that are expected to be bottlenecks may be
   expected to contribute a larger fraction of the total packet loss.
   In compensation, non-bottlenecked subpaths have to be constrained to
   contribute less packet loss.  The criteria for passing each test of a
   TIDS is an apportioned share of the total bound determined by the
   mathematical model from the Target Transport Performance.

   Section 8 describes the suite of individual tests needed to verify
   all of required IP delivery properties.  A subpath passes if and only
   if all of the individual IP diagnostic tests pass.  Any subpath that
   fails any test indicates that some users are likely fail to attain
   their Target Transport Performance under some conditions.  In
   addition to passing or failing, a test can be deemed to be
   inconclusive for a number of reasons including: the precomputed
   traffic pattern was not accurately generated; the measurement results
   were not statistically significant; and others such as failing to
   meet some required test preconditions.  If all tests pass, except
   some are inconclusive then the entire suite is deemed to be

   In Section 9 we present an example TIDS that might be representative
   of HD video, and illustrate how Model Based Metrics can be used to
   address difficult measurement situations, such as confirming that
   intercarrier exchanges have sufficient performance and capacity to
   deliver HD video between ISPs.

   Since there is some uncertainty in the modeling process, Section 10
   describes a validation procedure to diagnose and minimize false
   positive and false negative results.

3.  Terminology

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   document are to be interpreted as described in [RFC2119].

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   Note that terms containing underscores (rather than spaces) appear in
   equations in the modeling sections.  In some cases both forms are
   used for aesthetic reasons, they do not have different meanings.

   General Terminology:

   Target:  A general term for any parameter specified by or derived
      from the user's application or transport performance requirements.
   Target Transport Performance:  Application or transport performance
      goals for the complete path.  For Bulk Transport Capacity defined
      in this note the Target Transport Performance includes the Target
      Data Rate, Target RTT and Target MTU as described below.
   Target Data Rate:  The specified application data rate required for
      an application's proper operation.  Conventional BTC metrics are
      focused on the Target Data Rate, however these metrics had little
      or no predictive value because they do not consider the effects of
      the other two parameters of the Target Transport Performance, the
      RTT and MTU of the complete paths.
   Target RTT (Round Trip Time):  The specified baseline (minimum) RTT
      of the longest complete path over which the user expects to be
      able meet the target performance.  TCP and other transport
      protocol's ability to compensate for path problems is generally
      proportional to the number of round trips per second.  The Target
      RTT determines both key parameters of the traffic patterns (e.g.
      burst sizes) and the thresholds on acceptable IP packet transfer
      statistics.  The Target RTT must be specified considering
      appropriate packets sizes: MTU sized packets on the forward path,
      ACK sized packets (typically header_overhead) on the return path.
      Note that Target RTT is specified and not measured, it determines
      the applicability of MBM measuremets for paths that are different
      than the measured path.
   Target MTU (Maximum Transmission Unit):  The specified maximum MTU
      supported by the complete path the over which the application
      expects to meet the target performance.  Assume 1500 Byte MTU
      unless otherwise specified.  If some subpath has a smaller MTU,
      then it becomes the Target MTU for the complete path, and all
      model calculations and subpath tests must use the same smaller
   Targeted IP Diagnostic Suite (TIDS):  A set of IP diagnostic tests
      designed to determine if an otherwise ideal complete path
      containing the subpath under test can sustain flows at a specific
      target_data_rate using target_MTU sized packets when the RTT of
      the complete path is target_RTT.
   Fully Specified Targeted IP Diagnostic Suite:  A TIDS together with
      additional specification such as "type-p", etc which are out of
      scope for this document, but need to be drawn from other standards

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   Bulk Transport Capacity:  Bulk Transport Capacity Metrics evaluate an
      Internet path's ability to carry bulk data, such as large files,
      streaming (non-real time) video, and under some conditions, web
      images and other content.  Prior efforts to define BTC metrics
      have been based on [RFC3148], which predates our understanding of
      TCP and the requirements described in Section 4
   IP diagnostic tests:  Measurements or diagnostics to determine if
      packet transfer statistics meet some precomputed target.
   traffic patterns:  The temporal patterns or burstiness of traffic
      generated by applications over transport protocols such as TCP.
      There are several mechanisms that cause bursts at various time
      scales as described in Section 4.1.  Our goal here is to mimic the
      range of common patterns (burst sizes and rates, etc), without
      tying our applicability to specific applications, implementations
      or technologies, which are sure to become stale.
   packet transfer statistics:  Raw, detailed or summary statistics
      about packet transfer properties of the IP layer including packet
      losses, ECN Congestion Experienced (CE) marks, reordering, or any
      other properties that may be germane to transport performance.
   packet loss ratio:  As defined in [I-D.ietf-ippm-2680-bis].
   apportioned:  To divide and allocate, for example budgeting packet
      loss across multiple subpaths such that the losses will accumulate
      to less than a specified end-to-end loss ratio.  Apportioning
      metrics is essentially the inverse of the process described in
   open loop:  A control theory term used to describe a class of
      techniques where systems that naturally exhibit circular
      dependencies can be analyzed by suppressing some of the
      dependencies, such that the resulting dependency graph is acyclic.

   Terminology about paths, etc.  See [RFC2330] and [RFC7398].

   data sender:  Host sending data and receiving ACKs.
   data receiver:  Host receiving data and sending ACKs.
   complete path:  The end-to-end path from the data sender to the data
   subpath:  A portion of the complete path.  Note that there is no
      requirement that subpaths be non-overlapping.  A subpath can be a
      small as a single device, link or interface.
   measurement point:  Measurement points as described in [RFC7398].
   test path:  A path between two measurement points that includes a
      subpath of the complete path under test.  If the measurement
      points are off path, the test path may include "test leads"
      between the measurement points and the subpath.

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   dominant bottleneck:  The bottleneck that generally determines most
      of packet transfer statistics for the entire path.  It typically
      determines a flow's self clock timing, packet loss and ECN
      Congestion Experienced (CE) marking rate, with other potential
      bottlenecks having less effect on the packet transfer statistics.
      See Section 4.1 on TCP properties.
   front path:  The subpath from the data sender to the dominant
   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
      network resources (bandwidth and/or queue capacity).

   Properties determined by the complete path and application.  These
   are described in more detail in Section 5.1.

   Application Data Rate:  General term for the data rate as seen by the
      application above the transport layer in bytes per second.  This
      is the payload data rate, and explicitly excludes transport and
      lower level headers (TCP/IP or other protocols), retransmissions
      and other overhead that is not part to the total quantity of data
      delivered to the application.
   IP rate:  The actual number of IP-layer bytes delivered through a
      subpath, per unit time, including TCP and IP headers, retransmits
      and other TCP/IP overhead.  Follows from IP-type-P Link Usage
   IP capacity:  The maximum number of IP-layer bytes that can be
      transmitted through a subpath, per unit time, including TCP and IP
      headers, retransmits and other TCP/IP overhead.  Follows from IP-
      type-P Link Capacity [RFC5136].
   bottleneck IP capacity:  The IP capacity of the dominant bottleneck
      in the forward path.  All throughput maximizing protocols estimate
      this capacity by observing the IP rate delivered through the
      bottleneck.  Most protocols derive their self clocks from the
      timing of this data.  See Section 4.1 and Appendix B for more
   implied bottleneck IP capacity:  This is the bottleneck IP capacity
      implied by the ACKs returning from the receiver.  It is determined
      by looking at how much application data the ACK stream at the
      sender reports delivered to the data receiver per unit time at
      various time scales.  If the return path is thinning, batching or
      otherwise altering the ACK timing the implied bottleneck IP
      capacity over short time scales might be substantially larger than
      the bottleneck IP capacity averaged over a full RTT.  Since TCP
      derives its clock from the data delivered through the bottleneck
      the front path must have sufficient buffering to absorb any data
      bursts at the dimensions (duration and IP rate) implied by the ACK

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      stream, potentially doubled during slowstart.  If the return path
      is not altering the ACK stream, then the implied bottleneck IP
      capacity will be the same as the bottleneck IP capacity.  See
      Section 4.1 and Appendix B for more details.
   sender interface rate:  The IP rate which corresponds to the IP
      capacity of the data sender's interface.  Due to sender efficiency
      algorithms including technologies such as TCP segmentation offload
      (TSO), nearly all moderns servers deliver data in bursts at full
      interface link rate.  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.

   Basic parameters common to models and subpath tests are defined here
   are described in more detail in Section 5.2.  Note that these are
   mixed between application transport performance (excludes headers)
   and IP performance (which include TCP headers and retransmissions as
   part of the IP payload).

   Window [size]:  The total quantity of data plus the data represented
      by ACKs circulating in the network is referred to as the window.
      See Section 4.1.  Sometimes used with other qualifiers (congestion
      window, cwnd or receiver window) to indicate which mechanism is
      controlling the window.
   pipe size:  A general term for number of packets needed in flight
      (the window size) to exactly fill some network path or subpath.
      It corresponds to the window size which maximizes network power,
      the observed data rate divided by the observed RTT.  Often used
      with additional qualifiers to specify which path, or under what
      conditions, etc.
   target_window_size:  The average number of packets in flight (the
      window size) needed to meet the Target Data Rate, for the
      specified Target RTT, and MTU.  It implies the scale of the bursts
      that the network might experience.
   run length:  A general term for the observed, measured, or specified
      number of packets that are (expected to be) delivered between
      losses or ECN Congestion Experienced (CE) marks.  Nominally one
      over the sum of the loss and ECN CE marking probabilities, if
      there are independently and identically distributed.
   target_run_length:  The target_run_length is an estimate of the
      minimum number of non-congestion marked packets needed between
      losses or ECN Congestion Experienced (CE) marks necessary to
      attain the target_data_rate over a path with the specified
      target_RTT and target_MTU, as computed by a mathematical model of
      TCP congestion control.  A reference calculation is shown in
      Section 5.2 and alternatives in Appendix A

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   reference target_run_length:  target_run_length computed precisely by
      the method in Section 5.2.  This is likely to be more slightly
      conservative than required by modern TCP implementations.

   Ancillary parameters used for some tests:

   derating:  Under some conditions the standard models are too
      conservative.  The modeling framework permits some latitude in
      relaxing or "derating" some test parameters as described in
      Section 5.3 in exchange for a more stringent TIDS validation
      procedures, described in Section 10.
   subpath_IP_capacity:  The IP capacity of a specific subpath.
   test path:  A subpath of a complete path under test.
   test_path_RTT:  The RTT observed between two measurement points using
      packet sizes that are consistent with the transport protocol.
      Generally MTU sized packets of the forward path, header_overhead
      sized packets on the return path.
   test_path_pipe:  The pipe size of a test path.  Nominally the test
      path RTT times the test path IP_capacity.
   test_window:  The window necessary to meet the target_rate over a
      test path.  Typically test_window=target_data_rate*test_path_RTT/
      (target_MTU - header_overhead).

   The terminology below is used to define temporal patterns for test
   stream.  These patterns are designed to mimic TCP behavior, as
   described in Section 4.1.
   packet headway:  Time interval between packets, specified from the
      start of one to the start of the next. e.g.  If packets are sent
      with a 1 mS headway, there will be exactly 1000 packets per
   burst headway:  Time interval between bursts, specified from the
      start of the first packet one burst to the start of the first
      packet of the next burst. e.g.  If 4 packet bursts are sent with a
      1 mS burst headway, there will be exactly 4000 packets per second.
   paced single packets:  Send individual packets at the specified rate
      or packet headway.
   paced bursts:  Send bursts on a timer.  Specify any 3 of: average
      data rate, packet size, burst size (number of packets) and burst
      headway (burst start to start).  By default the bursts are assumed
      full sender interface rate, such that the packet headway within
      each burst is the minimum supported by the sender's interface.
      Under some conditions it is useful to explicitly specify the
      packet headway within each burst.
   slowstart rate:  Mimic TCP slowstart by sending 4 packet paced bursts
      at an average data rate equal to twice the implied bottleneck IP
      capacity (but not more than the sender interface rate).  This is a
      two level burst pattern described in more detail in Section 6.1.
      If the implied bottleneck IP capacity is more than half of the

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      sender interface rate, slowstart rate becomes sender interface
   slowstart burst:  Mimic one round of TCP slowstart by sending a
      specified number of packets packets in a two level burst pattern
      that resembles slowstart.
   repeated slowstart bursts:  Repeat Slowstart bursts once per
      target_RTT.  For TCP each burst would be twice as large as the
      prior burst, and the sequence would end at the first ECN CE mark
      or lost packet.  For measurement, all slowstart bursts would be
      the same size (nominally target_window_size but other sizes might
      be specified), and the ECN CE marks and lost packets are counted.

   The tests described in this note can be grouped according to their

   Capacity tests:  Capacity tests determine if a network subpath has
      sufficient capacity to deliver the Target Transport Performance.
      As long as the test stream is within the proper envelope for the
      Target Transport Performance, the average packet losses or ECN
      Congestion Experienced (CE) marks must be below the threshold
      computed by the model.  As such, capacity tests reflect parameters
      that can transition from passing to failing as a consequence of
      cross traffic, additional presented load or the actions of other
      network users.  By definition, capacity tests also consume
      significant network resources (data capacity and/or queue buffer
      space), and the test schedules must be balanced by their cost.
   Monitoring tests:  Monitoring tests are designed to capture the most
      important aspects of a capacity test, but without presenting
      excessive ongoing load themselves.  As such they may miss some
      details of the network's performance, but can serve as a useful
      reduced-cost proxy for a capacity test, for example to support
      ongoing monitoring.
   Engineering tests:  Engineering tests evaluate how network algorithms
      (such as AQM and channel allocation) interact with TCP-style self
      clocked protocols and adaptive congestion control based on packet
      loss and ECN Congestion Experienced (CE) marks.  These tests are
      likely to have complicated interactions with cross traffic and
      under some conditions can be inversely sensitive to load.  For
      example a test to verify that an AQM algorithm causes ECN CE marks
      or packet drops early enough to limit queue occupancy may
      experience a false pass result in the presence of cross traffic.
      It is important that engineering tests be performed under a wide
      range of conditions, including both in situ and bench testing, and
      over a wide variety of load conditions.  Ongoing monitoring is
      less likely to be useful for engineering tests, although sparse in
      situ testing might be appropriate.

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4.  Background

   At the time the IPPM WG was chartered, sound Bulk Transport Capacity
   measurement was known to be well beyond our capabilities.  Even at
   the time that Framework for IP Performance Metrics [RFC3148] was
   written we knew that we didn't fully understand the problem.  Now, by
   hindsight we understand why BTC 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, such that transport
      protocols change the packet transfer statistics (raise the packet
      loss ratio and/or RTT) to conform to their behavior.  By design
      TCP congestion control keeps raising the data rate until the
      network gives some indication that it is full by dropping or ECN
      CE marking packets.  If TCP successfully fills the network (e.g.
      the bottleneck is 100% utilized) the packet loss and ECN CE marks
      are mostly determined by TCP and how hard TCP drives the network
      at that rate and not by the network itself.
   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
      subpath may pass a short RTT local test even though it fails when
      the subpath is extended by an effectively perfect network to some
      larger RTT.
   o  TCP has an extreme form of the 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 bounds on the relative momentum of
      the measurement and measured particles.  For network measurement
      you can not in general determine the relative "mass" of either the
      test stream or the cross traffic, so you can not gauge the
      relative magnitude of the uncertainty that might be introduced by
      any interaction.

   These properties are a consequence of the equilibrium behavior
   intrinsic to how all throughput maximizing protocols interact with
   the Internet.  These protocols rely on control systems based on
   estimated network parameters to regulate the quantity of data sent
   into the network.  The sent data in turn alters the network
   properties observed by the estimators, such that there are circular
   dependencies between every component and every property.  Since some
   of these properties are nonlinear, the entire system is nonlinear,
   and any change anywhere causes difficult to predict changes in every

   Model Based Metrics overcome these problems by making the measurement
   system open loop: the packet transfer statistics (akin to the network

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   estimators) do not affect the traffic or traffic patterns (bursts),
   which computed on the basis of the Target Transport Performance.  In
   order for a network to pass, the resulting packet transfer statistics
   and corresponding network estimators have to be such that they would
   not cause the control systems slow the traffic below the Target Data

4.1.  TCP properties

   TCP and SCTP are self clocked protocols that carry the vast majority
   of all Internet data.  Their dominant behavior is to have an
   approximately fixed quantity of data and acknowledgements (ACKs)
   circulating in the network.  The data receiver reports arriving data
   by returning ACKs to the data sender, the data sender typically
   responds by sending exactly the same quantity of data back into the
   network.  The total 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
   window by sending slightly more or less data in response to each ACK.
   The fundamentally important property of this system is that it is
   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 protocol features cause bursts of data, even in idealized
   networks that can be modeled as simple queueing systems.

   During slowstart the IP rate is doubled on each RTT by sending twice
   as much data as was delivered to the receiver during the prior RTT.
   Each returning ACK causes the sender to transmit twice the data the
   ACK reported arriving at the receiver.  For slowstart to be able to
   fill 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 or ECN CE mark.  For example, with classic Reno
   congestion control, an optimal slowstart has to end with a burst that
   is twice the bottleneck rate for one RTT in duration.  This burst
   causes a queue which is equal to the pipe size (i.e. the window is
   twice the pipe size) so when the window is halved in response to the
   first packet loss, the new window will be the pipe size.

   Note that if the bottleneck IP rate is less that half of the capacity
   of the front path (which is almost always the case), the slowstart
   bursts will not by themselves cause significant queues anywhere else
   along the front path; they primarily exercise the queue at the
   dominant bottleneck.

   Several common efficiency algorithms also cause bursts.  The self
   clock is typically applied to groups of packets: the receiver's

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   delayed ACK algorithm generally sends only one ACK per two data
   segments.  Furthermore the modern senders use TCP segmentation
   offload (TSO) to reduce CPU overhead.  The sender's software stack
   builds supersized TCP segments that the TSO hardware splits into MTU
   sized segments on the wire.  The net effect of TSO, delayed ACK and
   other efficiency algorithms is to send bursts of segments at full
   sender interface rate.

   Note that these efficiency algorithms are almost always in effect,
   including during slowstart, such that slowstart typically has a two
   level burst structure.  Section 6.1 describes slowstart in more

   Additional sources of bursts include TCP's initial window [RFC6928],
   application pauses, channel allocation mechanisms and network devices
   that schedule ACKs.  Appendix B describes these last two items.  If
   the application pauses (stops reading or writing data) for some
   fraction of an RTT, many TCP implementations catch up to their
   earlier window size by sending a burst of data at the full sender
   interface rate.  To fill a network with a realistic application, the
   network has to be able to tolerate sender interface rate bursts large
   enough to restore the prior window following application pauses.

   Although the sender interface rate bursts are typically smaller than
   the last burst of a slowstart, they are at a higher IP rate so they
   potentially exercise queues at arbitrary points along the front path
   from the data sender up to and including the queue at the dominant
   bottleneck.  There is no model for how frequent or what sizes of
   sender rate bursts the network should tolerate.

   In conclusion, to verify that a path can meet a Target Transport
   Performance, it is necessary to independently confirm that the path
   can tolerate bursts at the scales that can be caused by these
   mechanisms.  Three cases are believed to be sufficient:

   o  Two level slowstart bursts sufficient to get connections started
   o  Ubiquitous sender interface rate bursts caused by efficiency
      algorithms.  We assume 4 packet bursts to be the most common case,
      since it matches the effects of delayed ACK during slowstart.
      These bursts should be assumed not to significantly affect packet
      transfer statistics.
   o  Infrequent sender interface rate bursts that are full
      target_window_size.  Target_run_length may be derated for these
      large fast bursts.

   If a subpath can meet the required packet loss ratio for bursts at
   all of these scales then it has sufficient buffering at all potential

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   bottlenecks to tolerate any of the bursts that are likely introduced
   by TCP or other transport protocols.

4.2.  Diagnostic Approach

   A complete path of a given RTT and MTU, which are equal to or smaller
   than the Target RTT and equal to or larger than the Target MTU
   respectively, is expected to be able to attain a specified Bulk
   Transport Capacity when all of the following conditions are met:
   1.  The IP capacity is above the Target Data Rate by sufficient
       margin to cover all TCP/IP overheads.  This can be confirmed by
       the tests described in Section 8.1 or any number of IP capacity
       tests adapted to implement MBM.
   2.  The observed packet transfer statistics are better than required
       by a suitable TCP performance model (e.g. fewer packet losses or
       ECN CE marks).  See Section 8.1 or any number of low rate packet
       loss tests outside of MBM.
   3.  There is sufficient buffering at the dominant bottleneck to
       absorb a slowstart bursts large enough to get the flow out of
       slowstart at a suitable window size.  See Section 8.3.
   4.  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 any other mechanisms.  See Section 8.4.
   5.  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 [RFC7567].  See Section 8.2.
   6.  When there is a standing queue at a bottleneck for a shared media
       subpath (e.g. half duplex), there must be a suitable bounds on
       the interaction between ACKs and data, for example due to the
       channel arbitration mechanism.  See Section 8.2.4.

   Note that conditions 1 through 4 require capacity tests for
   validation, and thus may need to be monitored on an ongoing basis.
   Conditions 5 and 6 require engineering tests, which are best
   performed in controlled environments such as a bench test.  They
   won't generally fail due to load, but may fail in the field due to
   configuration errors, etc. and should be spot checked.

   We are developing a tool that can perform many of the tests described
   here [MBMSource].

4.3.  New requirements relative to RFC 2330

   Model Based Metrics are designed to fulfill some additional
   requirements that were not recognized at the time RFC 2330 was
   written [RFC2330].  These missing requirements may have significantly
   contributed to policy difficulties in the IP measurement space.  Some

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   additional requirements are:
   o  IP 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 should be spatially composable, such that measures of
      concatenated paths should be predictable from subpaths.
   o  Metrics must be vantage point invariant over a significant range
      of measurement point choices, including off path measurement
      points.  The only requirements on MP selection should be that the
      RTT between the MPs is below some reasonable bound, and that the
      effects of the "test leads" connecting MPs to the subpath under
      test can be can be calibrated out of the measurements.  The latter
      might be be accomplished if the test leads are effectively ideal
      or their properties can be deducted from the measurements between
      the MPs.  While many of tests require that the test leads have at
      least as much IP capacity as the subpath under test, some do not,
      for example Background Packet Transfer Tests described in
      Section 8.1.3.
   o  Metric measurements must be repeatable by multiple parties with no
      specialized access to MPs or diagnostic infrastructure.  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.  Note
      that vantage independence is key to meeting this requirement.

5.  Common Models and 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 3.  These parameters are
   determined by the needs of the application or the ultimate end user
   and the complete Internet path over which the application is expected
   to operate.  The target parameters are in units that make sense to
   upper layers: payload bytes delivered to the application, above TCP.
   They exclude overheads associated with TCP and IP headers,
   retransmits and other protocols (e.g.  DNS).

   Other end-to-end parameters defined in Section 3 include the
   effective bottleneck data rate, the sender interface data rate and
   the TCP and IP header sizes.

   The target_data_rate must be smaller than all subpath IP capacities
   by enough headroom to carry the transport protocol overhead,
   explicitly including retransmissions and an allowance for
   fluctuations in TCP's actual data rate.  Specifying a

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   target_data_rate with insufficient headroom is likely to result in
   brittle measurements having little predictive value.

   Note that the target parameters can be specified for a hypothetical
   path, for example to construct TIDS designed for bench testing in the
   absence of a real application; or for a live in situ test of
   production infrastructure.

   The number of concurrent connections is explicitly not a parameter to
   this model.  If a subpath requires multiple connections in order to
   meet the specified performance, that must be stated explicitly and
   the procedure described in Section 6.4 applies.

5.2.  Common Model Calculations

   The Target Transport Performance is used to derive the
   target_window_size and the reference target_run_length.

   The target_window_size, is the average window size in packets needed
   to meet the target_rate, for the specified target_RTT and target_MTU.
   It is given by:

   target_window_size = ceiling( target_rate * target_RTT / ( target_MTU
   - header_overhead ) )

   Target_run_length is an estimate of the minimum required number of
   unmarked packets that must be delivered between losses or ECN
   Congestion Experienced (CE) marks, as computed by a mathematical
   model of TCP congestion control.  The derivation here follows
   [MSMO97], and by design is quite conservative.

   Reference target_run_length is derived as follows: assume the
   subpath_IP_capacity is infinitesimally larger than the
   target_data_rate plus the required header_overhead.  Then
   target_window_size also predicts the onset of queueing.  A larger
   window will cause a standing queue at the bottleneck.

   Assume the transport protocol is using standard Reno style Additive
   Increase, Multiplicative Decrease (AIMD) congestion control [RFC5681]
   (but not Appropriate Byte Counting [RFC3465]) and the receiver is
   using standard delayed ACKs.  Reno increases the window by one packet
   every pipe_size worth of ACKs.  With delayed ACKs this takes 2 Round
   Trip Times per increase.  To exactly fill the pipe, losses must be no
   closer than when the peak of the AIMD sawtooth reached exactly twice
   the target_window_size otherwise the multiplicative window reduction
   triggered by the loss would cause the network to be underfilled.
   Following [MSMO97] the number of packets between losses must be the
   area under the AIMD sawtooth.  They must be no more frequent than

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   every 1 in ((3/2)*target_window_size)*(2*target_window_size) packets,
   which simplifies to:

   target_run_length = 3*(target_window_size^2)

   Note that this calculation is very conservative and is based on a
   number of assumptions that may not apply.  Appendix A discusses these
   assumptions and provides some alternative models.  If a different
   model is used, a fully specified TIDS or FSTIDS MUST document the
   actual method for computing target_run_length and ratio between
   alternate target_run_length and the reference target_run_length
   calculated above, along with a discussion of the rationale for the
   underlying assumptions.

   These two parameters, target_window_size and target_run_length,
   directly imply most of the individual parameters for the tests in
   Section 8.

5.3.  Parameter Derating

   Since some aspects of the models are very conservative, the MBM
   framework permits some latitude in derating test parameters.  Rather
   than trying to formalize more complicated models we permit some test
   parameters to be relaxed as long as they meet some additional
   procedural constraints:
   o  The TIDS or FSTIDS MUST document and justify the actual method
      used to compute the derated metric parameters.
   o  The validation procedures described in Section 10 must be used to
      demonstrate the feasibility of meeting the Target Transport
      Performance with infrastructure that infinitesimally passes the
      derated tests.
   o  The validation process for a FSTIDS itself must be documented is
      such a way that other researchers can duplicate the validation

   Except as noted, all tests below assume no derating.  Tests where
   there is not currently a well established model for the required
   parameters explicitly include derating as a way to indicate
   flexibility in the parameters.

5.4.  Test Preconditions

   Many tests have preconditions which are required to assure their
   validity.  Examples include: the presence or nonpresence of cross
   traffic on specific subpaths; negotiating ECN; and appropriate
   preloading to put reactive network elements into the proper states
   [RFC7312].  If preconditions are not properly satisfied for some
   reason, the tests should be considered to be inconclusive.  In

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   general it is useful to preserve diagnostic information as to why the
   preconditions were not met, and any test data that was collected even
   if it is not useful for the intended test.  Such diagnostic
   information and partial test data may be useful for improving the
   test in the future.

   It is important to preserve the record that a test was scheduled,
   because otherwise precondition enforcement mechanisms can introduce
   sampling bias.  For example, canceling tests due to cross traffic on
   subscriber access links might introduce sampling bias in tests of the
   rest of the network by reducing the number of tests during peak
   network load.

   Test preconditions and failure actions MUST be specified in a FSTIDS.

6.  Generating test streams

   Many important properties of Model Based Metrics, such as vantage
   independence, are a consequence of using test streams that have
   temporal structures that mimic TCP or other transport protocols
   running over a complete path.  As described in Section 4.1, self
   clocked protocols naturally have burst structures related to the RTT
   and pipe size of the complete path.  These bursts naturally get
   larger (contain more packets) as either the Target RTT or Target Data
   Rate get larger, or the Target MTU gets smaller.  An implication of
   these relationships is that test streams generated by running self
   clocked protocols over short subpaths may not adequately exercise the
   queueing at any bottleneck to determine if the subpath can support
   the full Target Transport Performance over the complete path.

   Failing to authentically mimic TCP's temporal structure is part the
   reason why simple performance tools such as iperf, netperf, nc, etc
   have the reputation of yielding false pass results over short test
   paths, even when some subpath has a flaw.

   The definitions in Section 3 are sufficient for most test streams.
   We describe the slowstart and standing queue test streams in more

   In conventional measurement practice stochastic processes are used to
   eliminate many unintended correlations and sample biases.  However
   MBM tests are designed to explicitly mimic temporal correlations
   caused by network or protocol elements themselves and are intended to
   accurately reflect implementation behavior.  Some portion of the
   system, such as traffic arrival (test scheduling) are naturally
   stochastic.  Other details, such as protocol processing times, are
   technically nondeterministic and might be modeled stochastically, but

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   are only a tiny part of the overall behavior which is dominated by
   implementation specific deterministic effects.  Furthermore, it is
   known that sampling bias is a real problem for some protocol
   implementations.  For example TCP's RTT estimator used to determine
   the Retransmit Time Out (RTO), can be fooled by periodic cross
   traffic or start-stop applications.

   At some point in the future it may make sense to introduce fine
   grained noise sources into the models used for generating test
   streams, but they are not warranted at this time.

6.1.  Mimicking slowstart

   TCP slowstart has a two level burst structure as shown in Figure 2.
   The fine structure is caused by the interaction between the ACK clock
   and TCP efficiency algorithms.  Each ACK passing through the return
   path triggers a small data burst.  These bursts are typically full
   sender interface rate, with the same headway as the returning ACKs,
   but having twice as much data as the ACK reported was delivered to
   the receiver.  Due to variations in delayed ACK and algorithms such
   as Appropriate Byte Counting [RFC3465], different pairs of senders
   and receivers produce different burst patterns.  Without loss of
   generality, we assume each ACK causes 4 packet bursts at an average
   headway equal to the ACK headway, and corresponding to sending at an
   average rate equal to twice the effective bottleneck IP rate.  This
   fine structure defines one slowstart rate burst.

   For a transport protocol the slowstart bursts are repeated every
   target_RTT.  Each slowstart burst is twice as large as the previous
   burst, and slowstart ends on the first lost packet or ECN mark.  For
   diagnostic tests described below we preserve the fine structure but
   manipulate the burst size and headway to measure the ability of the
   dominant bottleneck to absorb and smooth slowstart bursts.

   Note that a stream of repeated slowstart bursts has three different
   average rates, depending on the averaging interval.  At the finest
   time scale (a few packet times at the sender interface) the peak of
   the average IP rate is the same as the sender interface rate; at a
   medium timescale (a few packet times at the dominant bottleneck) the
   peak of the average IP rate is twice the implied bottleneck IP
   capacity; and at time scales longer than the target_RTT and when the
   burst size is equal to the target_window_size the average rate is
   equal to the target_data_rate.  This pattern corresponds to repeating
   the last RTT of TCP slowstart when delayed ACK and sender side byte
   counting are present but without the limits specified in Appropriate
   Byte Counting [RFC3465].

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   time -->    ( - = one packet)
   Packet stream:

   ----  ----  ----  ----  ----                     ----  ---- ...

   |<>| sender interface rate bursts (typically 3 or 4 packets)
   |<--->| burst headway (determined by ACK headway)
   |<------------------------>| slowstart burst size(from the window)
   |<---------------------------------------------->| slowstart headway
   \____________ _____________/                     \______ __ ...
                V                                          V
         One slowstart burst                   Repeated slowstart bursts

   Multiple levels of Slowstart Bursts

                                 Figure 2

6.2.  Constant window pseudo CBR

   Implement pseudo constant bit rate by running a standard protocol
   such as TCP with a fixed window size, such that it is self clocked.
   Data packets arriving at the receiver trigger acknowledgements (ACKs)
   which travel back to the sender where they trigger additional
   transmissions.  The window size is computed from the target_data_rate
   and the actual RTT of the test path.  The rate is only maintained in
   average over each RTT, and is subject to limitations of the transport

   Since the window size is constrained to be an integer number of
   packets, for small RTTs or low data rates there may not be
   sufficiently precise control over the data rate.  Rounding the window
   size up (the default) is likely to be result in data rates that are
   higher than the target rate, but reducing the window by one packet
   may result in data rates that are too small.  Also cross traffic
   potentially raises the RTT, implicitly reducing the rate.  Cross
   traffic that raises the RTT nearly always makes the test more
   strenuous.  A FSTIDS specifying a constant window CBR tests MUST
   explicitly indicate under what conditions errors in the data rate
   causes tests to inconclusive.

   Since constant window pseudo CBR testing is sensitive to RTT
   fluctuations it is less accurate at controling the data rate in
   environments with fluctuating delays.

6.3.  Scanned window pseudo CBR

   Scanned window pseudo CBR is similar to the constant window CBR
   described above, except the window is scanned across a range of sizes

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   designed to include two key events, the onset of queueing and the
   onset of packet loss or ECN CE marks.  The window is scanned by
   incrementing it by one packet every 2*target_window_size delivered
   packets.  This mimics the additive increase phase of standard Reno
   TCP congestion avoidance when delayed ACKs are in effect.  Normally
   the window increases separated by intervals slightly longer than
   twice the target_RTT.

   There are two ways to implement this test: one built by applying a
   window clamp to standard congestion control in a standard protocol
   such as TCP and the other built by stiffening a non-standard
   transport protocol.  When standard congestion control is in effect,
   any losses or ECN CE marks cause the transport to revert to a window
   smaller than the clamp such that the scanning clamp loses control the
   window size.  The NPAD pathdiag tool is an example of this class of
   algorithms [Pathdiag].

   Alternatively a non-standard congestion control algorithm can respond
   to losses by transmitting extra data, such that it maintains the
   specified window size independent of losses or ECN CE marks.  Such a
   stiffened transport explicitly violates mandatory Internet congestion
   control [RFC5681] and is not suitable for in situ testing.  It is
   only appropriate for engineering testing under laboratory conditions.
   The Windowed Ping tool implements such a test [WPING].  The tool
   described in the paper has been updated.[mpingSource]

   The test procedures in Section 8.2 describe how to the partition the
   scans into regions and how to interpret the results.

6.4.  Concurrent or channelized testing

   The procedures described in this document are only directly
   applicable to single stream measurement, e.g. one TCP connection or
   measurement stream.  In an ideal world, we would disallow all
   performance claims based multiple concurrent streams, but this is not
   practical due to at least two different issues.  First, many very
   high rate link technologies are channelized and at last partially pin
   the flow to channel mapping to minimize packet reordering within
   flows.  Second, TCP itself has scaling limits.  Although the former
   problem might be overcome through different design decisions, the
   later problem is more deeply rooted.

   All congestion control algorithms that are philosophically aligned
   with the standard [RFC5681] (e.g. claim some level of TCP
   compatibility, friendliness or fairness) have scaling limits, in the
   sense that as a long fast network (LFN) with a fixed RTT and MTU gets
   faster, these congestion control algorithms get less accurate and as
   a consequence have difficulty filling the network[CCscaling].  These

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   properties are a consequence of the original Reno AIMD congestion
   control design and the requirement in [RFC5681] that all transport
   protocols have similar responses to congestion.

   There are a number of reasons to want to specify performance in term
   of multiple concurrent flows, however this approach is not
   recommended for data rates below several megabits per second, which
   can be attained with run lengths under 10000 packets on many paths.
   Since the required run length goes as the square of the data rate, at
   higher rates the run lengths can be unreasonably large, and multiple
   flows might be the only feasible approach.

   If multiple flows are deemed necessary to meet aggregate performance
   targets then this MUST be stated both the design of the TIDS and in
   any claims about network performance.  The IP diagnostic tests MUST
   be performed concurrently with the specified number of connections.
   For the the tests that use bursty test streams, the bursts should be
   synchronized across streams.

7.  Interpreting the Results

7.1.  Test outcomes

   To perform an exhaustive test of a complete network path, each test
   of the TIDS is applied to each subpath of the complete path.  If any
   subpath fails any test then a standard transport protocol running
   over the complete path can also be expected to fail to attain the
   Target Transport Performance under some conditions.

   In addition to passing or failing, a test can be deemed to be
   inconclusive for a number of reasons.  Proper instrumentation and
   treatment of inconclusive outcomes is critical to the accuracy and
   robustness of Model Based Metrics.  Tests can be inconclusive if the
   precomputed traffic pattern or data rates were not accurately
   generated; the measurement results were not statistically
   significant; and others causes such as failing to meet some required
   preconditions for the test.  See Section 5.4

   For example consider a test that implements Constant Window Pseudo
   CBR (Section 6.2) by adding rate controls and detailed IP packet
   transfer instrumentation to TCP (e.g.  [RFC4898]).  TCP includes
   built in control systems which might interfere with the sending data
   rate.  If such a test meets the required packet transfer statistics
   (e.g. run length) while failing to attain the specified data rate it
   must be treated as an inconclusive result, because we can not a
   priori determine if the reduced data rate was caused by a TCP problem
   or a network problem, or if the reduced data rate had a material

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   effect on the observed packet transfer statistics.

   Note that for capacity tests, if the observed packet transfer
   statistics meet the statistical criteria for failing (accepting
   hypnosis H1 in Section 7.2), the test can can be considered to have
   failed because it doesn't really matter that the test didn't attain
   the required data rate.

   The really important new properties of MBM, such as vantage
   independence, are a direct consequence of opening the control loops
   in the protocols, such that the test stream does not depend on
   network conditions or IP packets received.  Any mechanism that
   introduces feedback between the path's measurements and the test
   stream generation is at risk of introducing nonlinearities that spoil
   these properties.  Any exceptional event that indicates that such
   feedback has happened should cause the test to be considered

   One way to view inconclusive tests is that they reflect situations
   where a test outcome is ambiguous between limitations of the network
   and some unknown limitation of the IP diagnostic test itself, which
   may have been caused by some uncontrolled feedback from the network.

   Note that procedures that attempt to sweep the target parameter space
   to find the limits on some parameter such as target_data_rate are at
   risk of breaking the location independent properties of Model Based
   Metrics, if any part of the boundary between passing and inconclusive
   results is sensitive to RTT (which is normally the case).

   One of the goals for evolving TIDS designs will be to keep sharpening
   distinction between inconclusive, passing and failing tests.  The
   criteria for for passing, failing and inconclusive tests MUST be
   explicitly stated for every test in the TIDS or FSTIDS.

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

   It may be useful to keep raw packet transfer statistics and ancillary
   metrics [RFC3148] for deeper study of the behavior of the network
   path and to measure the tools themselves.  Raw packet transfer
   statistics can help to drive tool evolution.  Under some conditions
   it might be possible to reevaluate the raw data for satisfying
   alternate Target Transport Performance.  However it is important to
   guard against sampling bias and other implicit feedback which can
   cause false results and exhibit measurement point vantage
   sensitivity.  Simply applying different delivery criteria based on a
   different Target Transport Performance is insufficient if the test

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   traffic patterns (bursts, etc) does not match the alternate Target
   Transport Performance.

7.2.  Statistical criteria for estimating run_length

   When evaluating the observed run_length, we need to determine
   appropriate packet stream sizes and acceptable error levels for
   efficient measurement.  In practice, can we compare the empirically
   estimated packet loss and ECN Congestion Experienced (CE) marking
   ratios 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:
   send packets (individually or in patterns) and observe the packet
   delivery performance (packet loss ratio or other metric, any marking
   we define).

   As each packet is sent and measured, we have an ongoing estimate of
   the performance in terms of the ratio of packet loss or ECN CE mark
   to total packets (i.e. an empirical probability).  We continue to
   send until conditions support a conclusion or a maximum sending limit
   has been reached.

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

   H0:  no more than one mark in target_run_length =
      3*(target_window_size)^2 packets

   and we can stop sending packets if on-going measurements support
   accepting H0 with the specified Type I error = alpha (= 0.05 for

   We also have an alternative Hypothesis to evaluate: if performance is
   significantly lower than the target_mark_probability.  Based on
   analysis of typical values and practical limits on measurement
   duration, we choose four times the H0 probability:

   H1:  one or more marks in (target_run_length/4) packets

   and we can stop sending packets if measurements support rejecting H0
   with the specified Type II error = beta (= 0.05 for example), thus
   preferring the alternate hypothesis H1.

   H0 and H1 constitute the Success and Failure outcomes described
   elsewhere in the memo, and while the ongoing measurements do not

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   support either hypothesis the current status of measurements is

   The problem above is formulated to match the Sequential Probability
   Ratio Test (SPRT) [StatQC].  Note that as originally framed the
   events under consideration were all manufacturing defects.  In
   networking, ECN CE marks and lost packets are not defects but
   signals, indicating that the transport protocol should slow down.

   The Sequential Probability Ratio Test also starts with a pair of
   hypothesis specified as above:

   H0:  p0 = one defect in target_run_length
   H1:  p1 = one defect in target_run_length/4
   As packets are sent and measurements collected, the tester evaluates
   the cumulative defect count against two boundaries representing H0
   Acceptance or Rejection (and acceptance of H1):

   Acceptance line:  Xa = -h1 + s*n
   Rejection line:  Xr = h2 + s*n
   where n increases linearly for each packet sent and

   h1 =  { log((1-alpha)/beta) }/k
   h2 =  { log((1-beta)/alpha) }/k
   k  =  log{ (p1(1-p0)) / (p0(1-p1)) }
   s  =  [ log{ (1-p0)/(1-p1) } ]/k
   for p0 and p1 as defined in the null and alternative Hypotheses
   statements above, and alpha and beta as the Type I and Type II

   The SPRT specifies simple stopping rules:

   o  Xa < defect_count(n) < Xb: continue testing
   o  defect_count(n) <= Xa: Accept H0
   o  defect_count(n) >= Xb: Accept H1

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

   Using the equations above, we can calculate the minimum number of
   packets (n) needed to accept H0 when x defects are observed.  For
   example, when x = 0:

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   Xa = 0  = -h1 + s*n
   and  n = h1 / s

7.3.  Reordering Tolerance

   All tests must be instrumented for packet level reordering [RFC4737].
   However, there is no consensus for how much reordering should be
   acceptable.  Over the last two decades the general trend has been to
   make protocols and applications more tolerant to reordering (see for
   example [RFC4015]), in response to the gradual increase in reordering
   in the network.  This increase has been due to the deployment of
   technologies such as multi threaded routing lookups and Equal Cost
   MultiPath (ECMP) routing.  These techniques increase parallelism in
   network and are critical to enabling overall Internet growth to
   exceed Moore's Law.

   Note that transport retransmission strategies can trade off
   reordering tolerance vs how quickly they can repair losses vs
   overhead from spurious retransmissions.  In advance of new
   retransmission strategies we propose the following strawman:
   Transport protocols should be able to adapt to reordering as long as
   the reordering extent is not more than the maximum of one quarter
   window or 1 mS, whichever is larger.  Within this limit on reorder
   extent, there should be no bound on reordering density.

   By implication, recording which is less than these bounds should not
   be treated as a network impairment.  However [RFC4737] still applies:
   reordering should be instrumented and the maximum reordering that can
   be properly characterized by the test (e.g. bound on history buffers)
   should be recorded with the measurement results.

   Reordering tolerance and diagnostic limitations, such as the size of
   the history buffer used to diagnose packets that are way out-of-
   order, MUST be specified in a FSTIDS.

8.  IP Diagnostic Tests

   The IP diagnostic tests below are organized by traffic pattern: basic
   data rate and packet transfer statistics, standing queues, slowstart
   bursts, and sender rate bursts.  We also introduce some combined
   tests which are more efficient when networks are expected to pass,
   but conflate diagnostic signatures when they fail.

   There are a number of test details which are not fully defined here.
   They must be fully specified in a FSTIDS.  From a standardization
   perspective, this lack of specificity will weaken this version of
   Model Based Metrics, however it is anticipated that this it be more

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   than offset by the extent to which MBM suppresses the problems caused
   by using transport protocols for measurement. e.g. non-specific MBM
   metrics are likely to have better repeatability than many existing
   BTC like metrics.  Once we have good field experience, the missing
   details can be fully specified.

8.1.  Basic Data Rate and Packet Transfer Tests

   We propose several versions of the basic data rate and packet
   transfer statistics test.  All measure the number of packets
   delivered between losses or ECN Congestion Experienced (CE) marks,
   using a data stream that is rate controlled at or below the

   The tests below differ in how the data rate is controlled.  The data
   can be paced on a timer, or window controlled at full Target Data
   Rate.  The first two tests implicitly confirm that sub_path has
   sufficient raw capacity to carry the target_data_rate.  They are
   recommend for relatively infrequent testing, such as an installation
   or periodic auditing process.  The third, background packet transfer
   statistics, is a low rate test designed for ongoing monitoring for
   changes in subpath quality.

   All rely on the data receiver accumulating packet transfer statistics
   as described in Section 7.2 to score the outcome:

   Pass: it is statistically significant that the observed interval
   between losses or ECN CE marks is larger than the target_run_length.

   Fail: it is statistically significant that the observed interval
   between losses or ECN CE marks is smaller than the target_run_length.

   A test is considered to be inconclusive if it failed to meet the data
   rate as specified below, meet the qualifications defined in
   Section 5.4 or neither run length statistical hypothesis was
   confirmed in the allotted test duration.

8.1.1.  Delivery Statistics at Paced Full Data Rate

   Confirm that the observed run length is at least the
   target_run_length while relying on timer to send data at the
   target_rate using the procedure described in in Section 6.1 with a
   burst size of 1 (single packets) or 2 (packet pairs).

   The test is considered to be inconclusive if the packet transmission
   can not be accurately controlled for any reason.

   RFC 6673 [RFC6673] is appropriate for measuring packet transfer

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   statistics at full data rate.

8.1.2.  Delivery Statistics at Full Data Windowed Rate

   Confirm that the observed run length is at least the
   target_run_length while sending at an average rate approximately
   equal to the target_data_rate, by controlling (or clamping) the
   window size of a conventional transport protocol to a fixed value
   computed from the properties of the test path, typically
   test_window=target_data_rate*test_path_RTT/target_MTU.  Note that if
   there is any interaction between the forward and return path,
   test_window may need to be adjusted slightly to compensate for the
   resulting inflated RTT.  However see the discussion in Section 8.2.4.

   Since losses and ECN CE marks cause transport protocols to reduce
   their data rates, this test is expected to be less precise about
   controlling its data rate.  It should not be considered inconclusive
   as long as at least some of the round trips reached the full
   target_data_rate without incurring losses or ECN CE marks.  To pass
   this test the network MUST deliver target_window_size packets in
   target_RTT time without any losses or ECN CE marks at least once per
   two target_window_size round trips, in addition to meeting the run
   length statistical test.

8.1.3.  Background Packet Transfer Statistics Tests

   The background run length is a low rate version of the target target
   rate test above, designed for ongoing lightweight monitoring for
   changes in the observed subpath run length without disrupting users.
   It should be used in conjunction with one of the above full rate
   tests because it does not confirm that the subpath can support raw
   data rate.

   RFC 6673 [RFC6673] is appropriate for measuring background packet
   transfer statistics.

8.2.  Standing Queue Tests

   These engineering tests confirm that the bottleneck is well behaved
   across the onset of packet loss, which typically follows after the
   onset of queueing.  Well behaved generally means lossless for
   transient queues, but once the queue has been sustained for a
   sufficient period of time (or reaches a sufficient queue depth) there
   should be a small number of losses to signal to the transport
   protocol that it should reduce its window.  Losses that are too early
   can prevent the transport from averaging at the target_data_rate.
   Losses that are too late indicate that the queue might be subject to
   bufferbloat [wikiBloat] and inflict excess queuing delays on all

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   flows sharing the bottleneck queue.  Excess losses (more than half of
   the window) at the onset of congestion make loss recovery problematic
   for the transport protocol.  Non-linear, erratic or excessive RTT
   increases suggest poor interactions between the channel acquisition
   algorithms and the transport self clock.  All of the tests in this
   section use the same basic scanning algorithm, described here, but
   score the link or subpath on the basis of how well it avoids each of
   these problems.

   For some technologies the data might not be subject to increasing
   delays, in which case the data rate will vary with the window size
   all the way up to the onset of load induced packet loss or ECN CE
   marks.  For theses technologies, the discussion of queueing does not
   apply, but it is still required that the onset of losses or ECN CE
   marks be at an appropriate point and progressive.

   Use the procedure in Section 6.3 to sweep the window across the onset
   of queueing and the onset of loss.  The tests below all assume that
   the scan emulates standard additive increase and delayed ACK by
   incrementing the window by one packet for every 2*target_window_size
   packets delivered.  A scan can typically be divided into three
   regions: below the onset of queueing, a standing queue, and at or
   beyond the onset of loss.

   Below the onset of queueing the RTT is typically fairly constant, and
   the data rate varies in proportion to the window size.  Once the data
   rate reaches the subpath IP rate, the data rate becomes fairly
   constant, and the RTT increases in proportion to the increase in
   window size.  The precise transition across the start of queueing can
   be identified by the maximum network power, defined to be the ratio
   data rate over the RTT.  The network power can be computed at each
   window size, and the window with the maximum are taken as the start
   of the queueing region.

   For technologies that do not have conventional queues, start the scan
   at a window equal to the test_window=target_data_rate*test_path_RTT/
   target_MTU, i.e. starting at the target rate, instead of the power

   If there is random background loss (e.g. bit errors, etc), precise
   determination of the onset of queue induced packet loss may require
   multiple scans.  Above the onset of queuing loss, all transport
   protocols are expected to experience periodic losses determined by
   the interaction between the congestion control and AQM algorithms.
   For standard congestion control algorithms the periodic losses are
   likely to be relatively widely spaced and the details are typically
   dominated by the behavior of the transport protocol itself.  For the
   stiffened transport protocols case (with non-standard, aggressive

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   congestion control algorithms) the details of periodic losses will be
   dominated by how the the window increase function responds to loss.

8.2.1.  Congestion Avoidance

   A subpath passes the congestion avoidance standing queue test if more
   than target_run_length packets are delivered between the onset of
   queueing (as determined by the window with the maximum network power)
   and the first loss or ECN CE mark.  If this test is implemented using
   a standards congestion control algorithm with a clamp, it can be
   performed in situ in the production internet as a capacity test.  For
   an example of such a test see [Pathdiag].

   For technologies that do not have conventional queues, use the
   test_window in place of the onset of queueing. i.e.  A subpath passes
   the congestion avoidance standing queue test if more than
   target_run_length packets are delivered between start of the scan at
   test_window and the first loss or ECN CE mark.

8.2.2.  Bufferbloat

   This test confirms that there is some mechanism to limit buffer
   occupancy (e.g. that prevents bufferbloat).  Note that this is not
   strictly a requirement for single stream bulk transport capacity,
   however if there is no mechanism to limit buffer queue occupancy then
   a single stream with sufficient data to deliver is likely to cause
   the problems described in [RFC7567], and [wikiBloat].  This may cause
   only minor symptoms for the dominant flow, but has the potential to
   make the subpath unusable for other flows and applications.

   Pass if the onset of loss occurs before a standing queue has
   introduced more delay than than twice target_RTT, or other well
   defined and specified limit.  Note that there is not yet a model for
   how much standing queue is acceptable.  The factor of two chosen here
   reflects a rule of thumb.  In conjunction with the previous test,
   this test implies that the first loss should occur at a queueing
   delay which is between one and two times the target_RTT.

   Specified RTT limits that are larger than twice the target_RTT must
   be fully justified in the FSTIDS.

8.2.3.  Non excessive loss

   This test confirm that the onset of loss is not excessive.  Pass if
   losses are equal or less than the increase in the cross traffic plus
   the test stream window increase on the previous RTT.  This could be
   restated as non-decreasing subpath throughput at the onset of loss,
   which is easy to meet as long as discarding packets is not more

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   expensive than delivering them.  (Note when there is a transient drop
   in subpath throughput, outside of a standing queue test, a subpath
   that passes other queue tests in this document will have sufficient
   queue space to hold one RTT worth of data).

   Note that conventional Internet policers will not pass this test,
   which is correct.  TCP often fails to come into equilibrium at more
   than a small fraction of the available capacity, if the capacity is
   enforced by a policer.  [Citation Pending].

8.2.4.  Duplex Self Interference

   This engineering test confirms a bound on the interactions between
   the forward data path and the ACK return path.

   Some historical half duplex technologies had the property that each
   direction held the channel until it completely drained its queue.
   When a self clocked transport protocol, such as TCP, has data and
   ACKs passing in opposite directions through such a link, the behavior
   often reverts to stop-and-wait.  Each additional packet added to the
   window raises the observed RTT by two packet times, once as it passes
   through the data path, and once for the additional delay incurred by
   the ACK waiting on the return path.

   The duplex self interference test fails if the RTT rises by more than
   a fixed bound above the expected queueing time computed from trom the
   excess window divided by the subpath IP Capacity.  This bound must be
   smaller than target_RTT/2 to avoid reverting to stop and wait
   behavior. (e.g.  Data packets and ACKs both have to be released at
   least twice per RTT.)

8.3.  Slowstart tests

   These tests mimic slowstart: data is sent at twice the effective
   bottleneck rate to exercise the queue at the dominant bottleneck.

8.3.1.  Full Window slowstart test

   This is a capacity test to confirm that slowstart is not likely to
   exit prematurely.  Send slowstart bursts that are target_window_size
   total packets.

   Accumulate packet transfer statistics as described in Section 7.2 to
   score the outcome.  Pass if it is statistically significant that the
   observed number of good packets delivered between losses or ECN CE
   marks is larger than the target_run_length.  Fail if it is
   statistically significant that the observed interval between losses
   or ECN CE marks is smaller than the target_run_length.

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   It is deemed inconclusive if the elapsed time to send the data burst
   is not less than half of the time to receive the ACKs. (i.e. sending
   data too fast is ok, but sending it slower than twice the actual
   bottleneck rate as indicated by the ACKs is deemed inconclusive).
   The headway for the slowstart bursts should be the target_RTT.

   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.

8.3.2.  Slowstart AQM test

   Do a continuous slowstart (send data continuously at twice the
   implied IP bottleneck capacity), until the first loss, stop, allow
   the network to drain and repeat, gathering statistics on how many
   packets were delivered before the loss, the pattern of losses,
   maximum observed RTT and window size.  Justify the results.  There is
   not currently sufficient theory justifying requiring any particular
   result, however design decisions that affect the outcome of this
   tests also affect how the network balances between long and short
   flows (the "mice vs elephants" problem).  The queue at the time of
   the first loss should be at least one half of the target_RTT.

   This is an engineering test: It must be performed on a quiescent
   network or testbed, since cross traffic has the potential to change
   the results.

8.4.  Sender Rate Burst tests

   These tests determine how well the network can deliver bursts sent at
   sender's interface rate.  Note that this test most heavily exercises
   the front path, and is likely to include infrastructure may be out of
   scope for an access ISP, even though the bursts might be caused by
   ACK compression, thinning or channel arbitration in the access ISP.
   See Appendix B.

   Also, there are a several details that are not precisely defined.
   For starters there is not a standard server interface rate. 1 Gb/s
   and 10 Gb/s are common today, but higher rates will become cost
   effective and can be expected to be dominant some time in the future.

   Current standards permit TCP to send a full window bursts following
   an application pause.  (Congestion Window Validation [RFC2861], is
   not required, but even if was, it does not take effect until an
   application pause is longer than an RTO.)  Since full window bursts
   are consistent with standard behavior, it is desirable that the
   network be able to deliver such bursts, otherwise application pauses
   will cause unwarranted losses.  Note that the AIMD sawtooth requires

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   a peak window that is twice target_window_size, so the worst case
   burst may be 2*target_window_size.

   It is also understood in the application and serving community that
   interface rate bursts have a cost to the network that has to be
   balanced against other costs in the servers themselves.  For example
   TCP Segmentation Offload (TSO) reduces server CPU in exchange for
   larger network bursts, which increase the stress on network buffer
   memory.  Some newer TCP implementations can pace traffic at scale
   [TSO_pacing][TSO_fq_pacing].  It remains to be determined if and how
   quickly these changes will be deployed.

   There is not yet theory to unify these costs or to provide a
   framework for trying to optimize global efficiency.  We do not yet
   have a model for how much the network should tolerate server rate
   bursts.  Some bursts must be tolerated by the network, but it is
   probably unreasonable to expect the network to be able to efficiently
   deliver all data as a series of bursts.

   For this reason, this is the only test for which we encourage
   derating.  A TIDS could include a table of pairs of derating
   parameters: burst sizes and how much each burst size is permitted to
   reduce the run length, relative to to the target_run_length.

8.5.  Combined and Implicit Tests

   Combined tests efficiently confirm multiple network properties in a
   single test, possibly as a side effect of normal content delivery.
   They require less measurement traffic than other testing strategies
   at the cost of conflating diagnostic signatures when they fail.
   These are by far the most efficient for monitoring networks that are
   nominally expected to pass all tests.

8.5.1.  Sustained Bursts Test

   The sustained burst test implements a combined worst case version of
   all of the capacity tests above.  It is simply:

   Send target_window_size bursts of packets at server interface rate
   with target_RTT burst headway (burst start to burst start).  Verify
   that the observed packet transfer statistics meets the

   Key observations:
   o  The subpath under test is expected to go idle for some fraction of
      the time: (subpath_IP_capacity-target_rate/
      Failing to do so indicates a problem with the procedure and an

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      inconclusive test result.
   o  The burst sensitivity can be derated by sending smaller bursts
      more frequently.  E.g. send target_window_size*derate packet
      bursts every target_RTT*derate.
   o  When not derated, this test is the most strenuous capacity test.
   o  A subpath that passes this test is likely to be able to sustain
      higher rates (close to subpath_IP_capacity) for paths with RTTs
      significantly smaller than the target_RTT.
   o  This test can be implemented with instrumented TCP [RFC4898],
      using a specialized measurement application at one end [MBMSource]
      and a minimal service at the other end [RFC0863] [RFC0864].
   o  This test is efficient to implement, since it does not require
      per-packet timers, and can make use of TSO in modern NIC hardware.
   o  If a subpath is known to pass the Standing Queue engineering tests
      (particularly that it has a progressive onset of loss at an
      appropriate queue depth), then the Sustained Burst Test is
      sufficient to assure that the subpath under test will not impair
      Bulk Transport Capacity at the target performance under all
      conditions.  See Section 8.2 for a discussion of the standing
      queue tests.

   Note that this test is clearly independent of the subpath RTT, or
   other details of the measurement infrastructure, as long as the
   measurement infrastructure can accurately and reliably deliver the
   required bursts to the subpath under test.

8.5.2.  Streaming Media

   Model Based Metrics can be implicitly implemented as a side effect
   any non-throughput maximizing application, such as streaming media,
   with some additional controls and instrumentation in the servers.
   The essential requirement is that the data rate be constrained such
   that even with arbitrary application pauses and bursts the data rate
   and burst sizes stay within the envelope defined by the individual
   tests described above.

   If the application's serving_data_rate is less than or equal to the
   target_data_rate and the serving_RTT (the RTT between the sender and
   client) is less than the target_RTT, this constraint is most easily
   implemented by clamping the transport window size to be no larger


   Under the above constraints the serving_window_clamp will limit the
   both the serving data rate and burst sizes to be no larger than the
   procedures in Section 8.1.2 and Section 8.4 or Section 8.5.1.  Since

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   the serving RTT is smaller than the target_RTT, the worst case bursts
   that might be generated under these conditions will be smaller than
   called for by Section 8.4 and the sender rate burst sizes are
   implicitly derated by the serving_window_clamp divided by the
   target_window_size at the very least.  (Depending on the application
   behavior, the data might be significantly smoother than specified by
   any of the burst tests.)

   In an alternative implementation the data rate and bursts might be
   explicitly controlled by a programmable traffic shaper or pacing at
   the sender.  This would provide better control over transmissions but
   it is substantially more complicated to implement and would be likely
   to have a higher CPU overhead.

   Note that these techniques can be applied to any content delivery
   that can be subjected to a reduced data rate in order to inhibit TCP
   equilibrium behavior.

9.  An Example

   In this section a we illustrate a TIDS designed to confirm that an
   access ISP can reliably deliver HD video from multiple content
   providers to all of their customers.  With modern codecs, minimal HD
   video (720p) generally fits in 2.5 Mb/s.  Due to their geographical
   size, network topology and modem designs the ISP determines that most
   content is within a 50 mS RTT from their users (This is a sufficient
   to cover continental Europe or either US coast from a single serving

                        2.5 Mb/s over a 50 ms path

                | End-to-End Parameter | value | units   |
                | target_rate          | 2.5   | Mb/s    |
                | target_RTT           | 50    | ms      |
                | target_MTU           | 1500  | bytes   |
                | header_overhead      | 64    | bytes   |
                | target_window_size   | 11    | packets |
                | target_run_length    | 363   | packets |

                                  Table 1

   Table 1 shows the default TCP model with no derating, and as such is
   quite conservative.  The simplest TIDS would be to use the sustained
   burst test, described in Section 8.5.1.  Such a test would send 11

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   packet bursts every 50mS, and confirming that there was no more than
   1 packet loss per 33 bursts (363 total packets in 1.650 seconds).

   Since this number represents is the entire end-to-end loss budget,
   independent subpath tests could be implemented by apportioning the
   packet loss ratio across subpaths.  For example 50% of the losses
   might be allocated to the access or last mile link to the user, 40%
   to the interconnects with other ISPs and 1% to each internal hop
   (assuming no more than 10 internal hops).  Then all of the subpaths
   can be tested independently, and the spatial composition of passing
   subpaths would be expected to be within the end-to-end loss budget.

   Testing interconnects has generally been problematic: conventional
   performance tests run between measurement points adjacent to either
   side of the interconnect, are not generally useful.  Unconstrained
   TCP tests, such as iperf [iperf] are usually overly aggressive
   because the RTT is so small (often less than 1 mS).  With a short RTT
   these tools are likely to report inflated numbers because for short
   RTTs these tools can tolerate very high packet loss ratios and can
   push other cross traffic off of the network.  As a consequence they
   are useless for predicting actual user performance, and may
   themselves be quite disruptive.  Model Based Metrics solves this
   problem.  The same test pattern as used on other subpaths can be
   applied to the interconnect.  For our example, when apportioned 40%
   of the losses, 11 packet bursts sent every 50mS should have fewer
   than one loss per 82 bursts (902 packets).

10.  Validation

   Since some aspects of the models are likely to be too conservative,
   Section 5.2 permits alternate protocol models and Section 5.3 permits
   test parameter derating.  If either of these techniques are used, we
   require demonstrations that such a TIDS can robustly detect subpaths
   that will prevent authentic applications using state-of-the-art
   protocol implementations from meeting the specified Target Transport
   Performance.  This correctness criteria is potentially difficult to
   prove, because it implicitly requires validating a TIDS against all
   possible subpaths and subpaths.  The procedures described here are
   still experimental.

   We suggest two approaches, both of which should be applied: first,
   publish a fully open description of the TIDS, including what
   assumptions were used and and how it was derived, such that the
   research community can evaluate the design decisions, test them and
   comment on their applicability; and second, demonstrate that an
   applications running over an infinitesimally passing testbed do meet
   the performance targets.

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   An infinitesimally passing testbed resembles a epsilon-delta proof in
   calculus.  Construct a test network such that all of the individual
   tests of the TIDS pass by only small (infinitesimal) margins, and
   demonstrate that a variety of authentic applications running over
   real TCP implementations (or other protocol as appropriate) meets the
   Target Transport Performance over such a network.  The workloads
   should include multiple types of streaming media and transaction
   oriented short flows (e.g. synthetic web traffic).

   For example, for the HD streaming video TIDS described in Section 9,
   the IP capacity should be exactly the header overhead above 2.5 Mb/s,
   the per packet random background loss ratio should be 1/363, for a
   run length of 363 packets, the bottleneck queue should be 11 packets
   and the front path should have just enough buffering to withstand 11
   packet interface rate bursts.  We want every one of the TIDS tests to
   fail if we slightly increase the relevant test parameter, so for
   example sending a 12 packet bursts should cause excess (possibly
   deterministic) packet drops at the dominant queue at the bottleneck.
   On this infinitesimally passing network it should be possible for a
   real application using a stock TCP implementation in the vendor's
   default configuration to attain 2.5 Mb/s over an 50 mS path.

   The most difficult part of setting up such a testbed is arranging for
   it to infinitesimally pass the individual tests.  Two approaches:
   constraining the network devices not to use all available resources
   (e.g. by limiting available buffer space or data rate); and
   preloading subpaths with cross traffic.  Note that is it important
   that a single environment be constructed which infinitesimally passes
   all tests at the same time, otherwise there is a chance that TCP can
   exploit extra latitude in some parameters (such as data rate) to
   partially compensate for constraints in other parameters (queue
   space, or viceversa).

   To the extent that a TIDS is used to inform public dialog it should
   be fully publicly documented, including the details of the tests,
   what assumptions were used and how it was derived.  All of the
   details of the validation experiment should also be published with
   sufficient detail for the experiments to be replicated by other
   researchers.  All components should either be open source of fully
   described proprietary implementations that are available to the
   research community.

11.  Security Considerations

   Measurement is often used to inform business and policy decisions,
   and as a consequence is potentially subject to manipulation.  Model
   Based Metrics are expected to be a huge step forward because

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   equivalent measurements can be performed from multiple vantage
   points, such that performance claims can be independently validated
   by multiple parties.

   Much of the acrimony in the Net Neutrality debate is due by the
   historical lack of any effective vantage independent tools to
   characterize network performance.  Traditional methods for measuring
   Bulk Transport Capacity are sensitive to RTT and as a consequence
   often yield very different results when run local to an ISP or
   internconnect and when run over a customer's complete path.  Neither
   the ISP nor customer can repeat the other's measurements, leading to
   high levels of distrust and acrimony.  Model Based Metrics are
   expected to greatly improve this situation.

   This document only describes a framework for designing Fully
   Specified Targeted IP Diagnostic Suite.  Each FSTIDS MUST include its
   own security section.

12.  Acknowledgements

   Ganga Maguluri suggested the statistical test for measuring loss
   probability in the target run length.  Alex Gilgur for helping with
   the statistics.

   Meredith Whittaker for improving the clarity of the communications.

   Ruediger Geib provided feedback which greatly improved the document.

   This work was inspired by Measurement Lab: open tools running on an
   open platform, using open tools to collect open data.  See

13.  IANA Considerations

   This document has no actions for IANA.

14.  References

14.1.  Normative References

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119, March 1997.

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14.2.  Informative References

   [RFC0863]  Postel, J., "Discard Protocol", STD 21, RFC 863, May 1983.

   [RFC0864]  Postel, J., "Character Generator Protocol", STD 22,
              RFC 864, May 1983.

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

   [RFC2861]  Handley, M., Padhye, J., and S. Floyd, "TCP Congestion
              Window Validation", RFC 2861, June 2000.

   [RFC3148]  Mathis, M. and M. Allman, "A Framework for Defining
              Empirical Bulk Transfer Capacity Metrics", RFC 3148,
              July 2001.

   [RFC3465]  Allman, M., "TCP Congestion Control with Appropriate Byte
              Counting (ABC)", RFC 3465, February 2003.

   [RFC4015]  Ludwig, R. and A. Gurtov, "The Eifel Response Algorithm
              for TCP", RFC 4015, February 2005.

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

   [RFC4898]  Mathis, M., Heffner, J., and R. Raghunarayan, "TCP
              Extended Statistics MIB", RFC 4898, May 2007.

   [RFC5136]  Chimento, P. and J. Ishac, "Defining Network Capacity",
              RFC 5136, February 2008.

   [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.

   [RFC6673]  Morton, A., "Round-Trip Packet Loss Metrics", RFC 6673,
              August 2012.

   [RFC6928]  Chu, J., Dukkipati, N., Cheng, Y., and M. Mathis,
              "Increasing TCP's Initial Window", RFC 6928, DOI 10.17487/

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              RFC6928, April 2013,

   [RFC7312]  Fabini, J. and A. Morton, "Advanced Stream and Sampling
              Framework for IP Performance Metrics (IPPM)", RFC 7312,
              August 2014.

   [RFC7398]  Bagnulo, M., Burbridge, T., Crawford, S., Eardley, P., and
              A. Morton, "A Reference Path and Measurement Points for
              Large-Scale Measurement of Broadband Performance",
              RFC 7398, February 2015.

   [RFC7567]  Baker, F., Ed. and G. Fairhurst, Ed., "IETF
              Recommendations Regarding Active Queue Management",
              BCP 197, RFC 7567, DOI 10.17487/RFC7567, July 2015,

              Almes, G., Kalidindi, S., Zekauskas, M., and A. Morton, "A
              One-Way Loss Metric for IPPM", draft-ietf-ippm-2680-bis-05
              (work in progress), August 2015.

   [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.

              Fan, X., Mathis, M., and D. Hamon, "Git Repository for
              mping: An IP Level Performance Diagnostic", Sept 2013,

              Hamon, D., Stuart, S., and H. Chen, "Git Repository for
              Model Based Metrics", Sept 2013,

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

   [iperf]    Wikipedia Contributors, "iPerf", Wikipedia, The Free
              Encyclopedia , cited March 2015, <http://en.wikipedia.org/

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

   [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.

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

   [AFD]      Pan, R., Breslau, L., Prabhakar, B., and S. Shenker,
              "Approximate fairness through differential dropping",
              SIGCOMM Comput. Commun. Rev.  33, 2, April 2003.

              Wikipedia, "Bufferbloat", http://en.wikipedia.org/w/
              index.php?title=Bufferbloat&oldid=608805474, March 2015.

              Fernando, F., Doyle, J., and S. Steven, "Scalable laws for
              stable network congestion control", Proceedings of
              Conference on Decision and
              Control, http://www.ee.ucla.edu/~paganini, December 2001.

              Corbet, J., "TSO sizing and the FQ scheduler",
              LWN.net https://lwn.net/Articles/564978/, Aug 2013.

              Dumazet, E. and Y. Chen, "TSO, fair queuing, pacing:
              three's a charm", Proceedings of IETF 88, TCPM WG https://
              Nov 2013.

Appendix A.  Model Derivations

   The reference target_run_length described in Section 5.2 is based on
   very conservative assumptions: that all window above
   target_window_size contributes to a standing queue that raises the
   RTT, and that classic Reno congestion control with delayed ACKs are
   in effect.  In this section we provide two alternative calculations
   using different assumptions.

   It may seem out of place to allow such latitude in a measurement
   standard, but this section provides offsetting requirements.

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   The estimates provided by these models make the most sense if network
   performance is viewed logarithmically.  In the operational Internet,
   data rates span more than 8 orders of magnitude, RTT spans more than
   3 orders of magnitude, and packet loss ratio spans at least 8 orders
   of magnitude if not more.  When viewed logarithmically (as in
   decibels), these correspond to 80 dB of dynamic range.  On an 80 dB
   scale, a 3 dB error is less than 4% of the scale, even though it
   represents a factor of 2 in untransformed parameter.

   This document gives a lot of latitude for calculating
   target_run_length, however people designing a TIDS should consider
   the effect of their choices on the ongoing tussle about the relevance
   of "TCP friendliness" as an appropriate model for Internet capacity
   allocation.  Choosing a target_run_length that is substantially
   smaller than the reference target_run_length specified in Section 5.2
   strengthens the argument that it may be appropriate to abandon "TCP
   friendliness" as the Internet fairness model.  This gives developers
   incentive and permission to develop even more aggressive applications
   and protocols, for example by increasing the number of connections
   that they open concurrently.

A.1.  Queueless Reno

   In Section 5.2 models were derived based on the assumption that the
   subpath IP rate matches the target rate plus overhead, such that the
   excess window needed for the AIMD sawtooth causes a fluctuating queue
   at the bottleneck.

   An alternate situation would be a bottleneck where there is no
   significant queue and losses are caused by some mechanism that does
   not involve extra delay, for example by the use of a virtual queue as
   done in Approximate Fair Dropping [AFD].  A flow controlled by such a
   bottleneck would have a constant RTT and a data rate that fluctuates
   in a sawtooth due to AIMD congestion control.  Assume the losses are
   being controlled to make the average data rate meet some goal which
   is equal or greater than the target_rate.  The necessary run length
   to meet the target_rate can be computed as follows:

   For some value of Wmin, the window will sweep from Wmin packets to
   2*Wmin packets in 2*Wmin RTT (due to delayed ACK).  Unlike the
   queueing case where Wmin = target_window_size, we want the average of
   Wmin and 2*Wmin to be the target_window_size, so the average data
   rate is the target rate.  Thus we want Wmin =

   Between losses each sawtooth delivers (1/2)(Wmin+2*Wmin)(2Wmin)
   packets in 2*Wmin round trip times.

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   Substituting these together we get:

   target_run_length = (4/3)(target_window_size^2)

   Note that this is 44% of the reference_run_length computed earlier.
   This makes sense because under the assumptions in Section 5.2 the
   AMID sawtooth caused a queue at the bottleneck, which raised the
   effective RTT by 50%.

Appendix B.  The effects of ACK scheduling

   For many network technologies simple queueing models don't apply: the
   network schedules, thins or otherwise alters the timing of ACKs and
   data, generally to raise the efficiency of the channel allocation
   algorithms when confronted with relatively widely spaced small ACKs.
   These efficiency strategies are ubiquitous for half duplex, wireless
   and broadcast media.

   Altering the ACK stream by holding or thinning ACKs typically has two
   consequences: it raises the implied bottleneck IP capacity, making
   the fine grained slowstart bursts either faster or larger and it
   raises the effective RTT by the average time that the ACKs and data
   are delayed.  The first effect can be partially mitigated by
   reclocking ACKs once they are beyond the bottleneck on the return
   path to the sender, however this further raises the effective RTT.

   The most extreme example of this sort of behavior would be a half
   duplex channel that is not released as long as the endpoint currently
   holding the channel has more traffic (data or ACKs) to send.  Such
   environments cause self clocked protocols under full load to revert
   to extremely inefficient stop and wait behavior.  The channel
   constrains the protocol to send an entire window of data as a single
   contiguous burst on the forward path, followed by the entire window
   of ACKs on the return path.

   If a particular return path contains a subpath or device that alters
   the timing of the ACK stream, then the entire front path from the
   sender up to the bottleneck must be tested at the burst parameters
   implied by the ACK scheduling algorithm.  The most important
   parameter is the Implied Bottleneck IP Capacity, which is the average
   rate at which the ACKs advance snd.una.  Note that thinning the ACK
   stream (relying on the cumulative nature of seg.ack to permit
   discarding some ACKs) requires larger sender interface bursts to
   offset the longer times between ACK in order to maintain the average
   data rate.

   It is important to note that due to ubiquitous self clocking in

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   Internet protocols, ill conceived channel allocation mechanisms
   increases the queueing stress on the front path because they cause
   larger full sender rate data bursts.

   Holding data or ACKs for channel allocation or other reasons (such as
   forward error correction) always raises the effective RTT relative to
   the minimum delay for the path.  Therefore it may be necessary to
   replace target_RTT in the calculation in Section 5.2 by an
   effective_RTT, which includes the target_RTT plus a term to account
   for the extra delays introduced by these mechanisms.

Appendix C.  Version Control

   This section to be removed prior to publication.

   Formatted: Mon Oct 19 15:59:51 PDT 2015

Authors' Addresses

   Matt Mathis
   Google, Inc
   1600 Amphitheater Parkway
   Mountain View, California  94043

   Email: mattmathis@google.com

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

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

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