IP Performance Working Group                                   M. Mathis
Internet-Draft                                               Google, Inc
Intended status: Experimental                                  A. Morton
Expires: January 7, 2016                                       AT&T Labs
                                                            July 6, 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 Bulk
   Transport Performance target 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 any path traversing it from meeting the
   specified Target Transport Performance.

   The IP diagnostic tests consist of precomputed traffic patterns and
   statistical criteria for evaluating packet delivery.  The traffic
   patterns are precomputed to mimic TCP or other transport protocol
   over a long path but are constructed in such a way that they are
   independent of the actual details of the subpath under test, end
   systems or applications.  Likewise the success criteria depends on
   the packet delivery statistics of the subpath, as evaluated against a
   protocol model applied to the Target Transport Performance.  The
   success criteria also does not depend on the details of the subpath,
   end systems or application.  This makes the measurements open loop,
   eliminating most of the difficulties encountered by traditional bulk
   transport metrics.

   Model based metrics exhibit several important new properties not
   present in other Bulk 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 results from multiple Measurement Points.

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

Status of this Memo

   This Internet-Draft is submitted in full conformance with the

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   provisions of BCP 78 and BCP 79.

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Table of Contents

   1.  Introduction . . . . . . . . . . . . . . . . . . . . . . . . .  5
     1.1.  Version Control  . . . . . . . . . . . . . . . . . . . . .  6
   2.  Overview . . . . . . . . . . . . . . . . . . . . . . . . . . .  7
   3.  Terminology  . . . . . . . . . . . . . . . . . . . . . . . . .  9
   4.  Background . . . . . . . . . . . . . . . . . . . . . . . . . . 14
     4.1.  TCP properties . . . . . . . . . . . . . . . . . . . . . . 16
     4.2.  Diagnostic Approach  . . . . . . . . . . . . . . . . . . . 17
     4.3.  New requirements relative to RFC 2330  . . . . . . . . . . 18
   5.  Common Models and Parameters . . . . . . . . . . . . . . . . . 18
     5.1.  Target End-to-end parameters . . . . . . . . . . . . . . . 18
     5.2.  Common Model Calculations  . . . . . . . . . . . . . . . . 19
     5.3.  Parameter Derating . . . . . . . . . . . . . . . . . . . . 20
     5.4.  Test Preconditions . . . . . . . . . . . . . . . . . . . . 21
   6.  Traffic generating techniques  . . . . . . . . . . . . . . . . 21
     6.1.  Paced transmission . . . . . . . . . . . . . . . . . . . . 21
     6.2.  Constant window pseudo CBR . . . . . . . . . . . . . . . . 23
     6.3.  Scanned window pseudo CBR  . . . . . . . . . . . . . . . . 24
     6.4.  Concurrent or channelized testing  . . . . . . . . . . . . 24
   7.  Interpreting the Results . . . . . . . . . . . . . . . . . . . 25
     7.1.  Test outcomes  . . . . . . . . . . . . . . . . . . . . . . 25
     7.2.  Statistical criteria for estimating run_length . . . . . . 27
     7.3.  Reordering Tolerance . . . . . . . . . . . . . . . . . . . 29
   8.  Diagnostic Tests . . . . . . . . . . . . . . . . . . . . . . . 29
     8.1.  Basic Data Rate and Packet Delivery Tests  . . . . . . . . 30
       8.1.1.  Delivery Statistics at Paced Full Data Rate  . . . . . 30
       8.1.2.  Delivery Statistics at Full Data Windowed Rate . . . . 31
       8.1.3.  Background Packet Delivery Statistics Tests  . . . . . 31
     8.2.  Standing Queue Tests . . . . . . . . . . . . . . . . . . . 31
       8.2.1.  Congestion Avoidance . . . . . . . . . . . . . . . . . 33
       8.2.2.  Bufferbloat  . . . . . . . . . . . . . . . . . . . . . 33
       8.2.3.  Non excessive loss . . . . . . . . . . . . . . . . . . 33
       8.2.4.  Duplex Self Interference . . . . . . . . . . . . . . . 34
     8.3.  Slowstart tests  . . . . . . . . . . . . . . . . . . . . . 34
       8.3.1.  Full Window slowstart test . . . . . . . . . . . . . . 35
       8.3.2.  Slowstart AQM test . . . . . . . . . . . . . . . . . . 35
     8.4.  Sender Rate Burst tests  . . . . . . . . . . . . . . . . . 35
     8.5.  Combined and Implicit Tests  . . . . . . . . . . . . . . . 36
       8.5.1.  Sustained Bursts Test  . . . . . . . . . . . . . . . . 36
       8.5.2.  Streaming Media  . . . . . . . . . . . . . . . . . . . 37
   9.  An Example . . . . . . . . . . . . . . . . . . . . . . . . . . 38
   10. Validation . . . . . . . . . . . . . . . . . . . . . . . . . . 40
   11. Security Considerations  . . . . . . . . . . . . . . . . . . . 41
   12. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 41
   13. IANA Considerations  . . . . . . . . . . . . . . . . . . . . . 42
   14. References . . . . . . . . . . . . . . . . . . . . . . . . . . 42
     14.1. Normative References . . . . . . . . . . . . . . . . . . . 42

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     14.2. Informative References . . . . . . . . . . . . . . . . . . 42
   Appendix A.  Model Derivations . . . . . . . . . . . . . . . . . . 44
     A.1.  Queueless Reno . . . . . . . . . . . . . . . . . . . . . . 45
   Appendix B.  Complex Queueing  . . . . . . . . . . . . . . . . . . 46
   Appendix C.  Version Control . . . . . . . . . . . . . . . . . . . 47
   Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 47

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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
   performance target over an Internet path.  Each test in the Targeted
   Diagnostic Suite (TDS) measures some aspect of IP packet transfer
   that is required to meet the Target Transport Performance.  For
   example a TDS may have separate diagnostic tests to verify that there
   is: sufficient IP capacity (rate); sufficient queue space to deliver
   typical transport bursts; and that the background packet loss ratio
   is small enough not to interfere with congestion control.  Unlike
   other metrics which yield measures of network properties, Model Based
   Metrics nominally yield pass/fail evaluations of the ability of
   standard transport protocols to meet a specific performance objective
   over some network path.

   This note describes the modeling framework to derive the IP
   diagnostic test parameters from the Target Transport Performance
   specified for TCP Bulk Transport Capacity.  Model Based Metrics is an
   alternative to the approach described in [RFC3148].  In the future,
   other Model Based Metrics may cover other applications and
   transports, such as VoIP over RTP.  In most cases the IP diagnostic
   tests can be implemented by combining existing IPPM metrics with
   additional controls for generating precomputed traffic patterns and
   statistical criteria for evaluating packet delivery.

   This approach, mapping Target Transport Performance to a targeted
   diagnostic suite (TDS) 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 other than that of the measurement itself.
   These problems are discussed at length in Section 4.

   A targeted suite of IP diagnostic tests does not have such
   difficulties.  They 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 bridge the gap between empirical IP
   measurements and expected TCP performance.

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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 Jul 6 13:49:30 PDT 2015

   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:
   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

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      *  load test -> capacity test

2.  Overview

   This document describes a modeling framework for deriving a Targeted
   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 Diagnostic Suites (FSTDS), that define all
   of these details.  We use Targeted Diagnostic Suite (TDS) 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 it
   implies about the requirements for IP packet delivery.  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 delivery and in a few
   cases, new mechanisms to implement precomputed traffic patterns.
   (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
   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.

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               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____________  | |  |
       | |    Traffic   |        |   Delivery  |  | |  |
       | |  Generation  |        |  Evaluation |  | |  |
       | |              |        |             |  | |  |
       | -------v--------        ------^--------  | |  |
       |   |    v   Test Traffic 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 bulk transport protocol 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 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 delivery 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

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   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
   TDS 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 diagnostics 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 TDS 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].

   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:

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   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 application 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 traffic 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
      MBM evaluations for paths that are different than the measured
   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 forces 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 Diagnostic Suite (TDS):  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 Diagnostic Suite:  A TDS 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
   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 ant the requirements described in Section 4

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   IP diagnostic tests:  Measurements or diagnostic tests to determine
      if packet delivery statistics meet some precomputed target.
   traffic patterns:  The temporal patterns or statistics 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 delivery statistics:  Raw, detailed or summary statistics
      about packet delivery properties of the IP layer including packet
      losses, ECN 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 they will accumulate to
      less than a specified end-to-end loss ratio.
   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, and if the measurement
      points are off path, may include "test leads" between the
      measurement points and the subpath.
   [Dominant] Bottleneck:  The Bottleneck that generally dominates
      packet delivery statistics for the entire path.  It typically
      determines a flow's self clock timing, packet loss and ECN marking
      rate.  See Section 4.1.
   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.

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   cross traffic:  Other, potentially interfering, traffic competing for
      network resources (bandwidth and/or queue capacity).

   Properties determined by the complete path and application.  They 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 Rate:  This is the IP rate of the data flowing through
      the dominant bottleneck in the forward path.  TCP and other
      protocols normally derive their self clocks from the timing of
      this data.  See Section 4.1 and Appendix B for more details.
   Implied Bottleneck IP Rate:  This is the bottleneck IP rate implied
      by the returning ACKs from the receiver.  It is determined by
      looking at how much application data the ACK stream reports
      delivered per unit time.  If the return path is thinning, batching
      or otherwise altering ACK timing TCP will derive its clock from
      the the implied bottleneck IP rate of the ACK stream, which in the
      short term, might be much different than the actual bottleneck IP
      rate.  In the case of thinned or batched ACKs front path must have
      sufficient buffering to smooth any data bursts to the IP capacity
      of the bottleneck.  If the return path is not altering the ACK
      stream, then the Implied Bottleneck IP Rate will be the same as
      the Bottleneck IP Rate.  See Section 4.1 and Appendix B for more
   [sender | interface] rate:  The IP rate which corresponds to the IP
      Capacity of the data sender's interface.  Due to issues of sender
      efficiency and technologies such as TCP offload engines, 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.

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   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 payload).

   Window:  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 marks.  Nominally one over the sum of the loss and
      ECN 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 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
   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 TDS validation
      procedures, described in Section 10.
   subpath_IP_capacity:  The IP capacity of a specific subpath.

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   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 tests described in this note can be grouped according to their

   capacity tests:  determine if a network subpath has sufficient
      capacity to deliver the Target Transport Performance.  As long as
      the test traffic is within the proper envelope for the Target
      Transport Performance, the average packet losses or ECN 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:  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:  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
      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 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.

4.  Background

   At the time the IPPM WG was chartered, sound Bulk Transport Capacity

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   measurement was known to be well beyond our capabilities.  Even at
   the time [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 network statistics (raise the packet loss
      ratio and/or RTT) to conform to their behavior.  By design TCP
      congestion control keep raising the data rate until the network
      gives some indication that it is full by dropping or ECN marking
      packets.  If TCP successfully fills the network the packet loss
      and ECN marks are mostly determined by TCP and how hard TCP drives
      the network 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 path is extended by a 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
      measurement traffic 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 traffic
   sent into the network.  The data traffic in turn alters network and
   the 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 parameter.

   Model Based Metrics overcome these problems by forcing the
   measurement system to be open loop: the packet delivery statistics
   (akin to the network 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 delivery statistics and corresponding network estimators have
   to be such that they would not cause the control systems slow the

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   traffic below the target data rate.

4.1.  TCP properties

   TCP and SCTP are self clocked protocols.  The dominant steady state
   behavior is to have an approximately fixed quantity of data and
   acknowledgements (ACKs) circulating in the network.  The receiver
   reports arriving data by returning ACKs to the data sender, the data
   sender 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 phenomena can cause bursts of data, even in idealized
   networks that can be modeled as simple queueing systems.

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

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

   Other sources of bursts include application pauses and channel
   allocation mechanisms.  Appendix B describes the treatment of channel
   allocation systems.  If the application pauses (stops reading or
   writing data) for some fraction of one RTT, state-of-the-art TCP
   catches up to the earlier window size by sending a burst of data at
   the full sender interface rate.  To fill such a network with a
   realistic application, the network has to be able to tolerate
   interface rate bursts from the data sender large enough to cover
   application pauses.

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   Although the interface rate bursts are typically smaller than the
   last burst of a slowstart, they are at a higher data 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 should be tolerated.

   To verify that a path can meet a Target Transport Performance, it is
   necessary to independently confirm that the path can tolerate bursts
   in the dimensions that can be caused by these mechanisms.  Three
   cases are likely to be sufficient:

   o  Slowstart bursts sufficient to get connections started properly.
   o  Frequent sender interface rate bursts that are small enough where
      they can be assumed not to significantly affect packet delivery
      statistics.  (Implicitly derated by limiting the burst size).
   o  Infrequent sender interface rate full target_window_size bursts
      that might affect the packet delivery statistics.
      (Target_run_length may be derated).

4.2.  Diagnostic Approach

   A complete path is expected to be able to sustain a Bulk TCP flow of
   a given data rate, MTU and RTT 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.  See Section 8.1 or any
       number of data rate tests outside of MBM.
   2.  The observed packet delivery statistics are better than required
       by a suitable TCP performance model (e.g. fewer losses or ECN
       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 rate burst 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.  See Section 8.2.
   6.  When there is a standing queue at a bottleneck for a shared media
       subpath (e.g. half duplex), there are suitable bounds on how the
       data and ACKs interact, for example due to the channel
       arbitration mechanism.  See Section 8.2.4.

   Note that conditions 1 through 4 require capacity tests for

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   validation, and thus may need to be monitored on an ongoing basis.
   Conditions 5 and 6 require engineering tests 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
   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 Delivery 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

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   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
   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 TDS 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 marks,
   as computed by a mathematical model of TCP congestion control.  The
   derivation here follows [MSMO97], and by design is quite

   Reference target_run_length is derived as follows: assume the
   subpath_IP_capacity is infinitesimally larger than the

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   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
   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 TDS or FSTDS 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 TDS or FSTDS 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.

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   o  The validation process for a FSTDS 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.  For example the presence or nonpresence of cross traffic
   on specific subpaths, or 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 general it is useful to preserve
   diagnostic information about 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 FSTDS.

6.  Traffic generating techniques

6.1.  Paced transmission

   Paced (burst) transmissions: send bursts of data on a timer to meet a
   particular target rate and pattern.  In all cases the specified data
   rate can either be the application or IP rates.  Header overheads
   must be included in the calculations as appropriate.
   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

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   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 sender interface rate bursts on a timer.  Specify
      any 3 of: average rate, packet size, burst size (number of
      packets) and burst headway (burst start to start).  The packet
      headway within a burst is typically assumed to be the minimum
      supported by the tester's interface. i.e.  Bursts are normally
      sent as back-to-back packets.  The packet headway within the
      bursts can also be explicitly specified.
   Slowstart burst:  Mimic TCP slowstart by sending 4 packet paced
      bursts at an average data rate equal to twice the implied
      bottleneck IP rate (but not more than the sender interface rate).
      If the implied bottleneck IP rate is more than half of the sender
      interface rate, slowstart rate bursts become sender interface rate
      bursts.  See the discussion and figure below.
   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 mark or
      lost packet.  For measurement, all slowstart bursts would be the
      same size (nominally target_window_size but other sizes might be
      specified).  See the discussion and figure below.

   The slowstart bursts mimic TCP slowstart under a particular set of
   implementation assumptions.  The burst headway shown in Figure 2
   reflects the TCP self clock derived from the data passing through the
   dominant bottleneck.  The slow start burst size is nominally
   target_window_size (so it might end with a bust that is less than 4
   packets).  The slowstart bursts are repeated every target_RTT.  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 rate is the same as the sender interface rate; at a
   medium scale (a few packet times at the dominant bottleneck) the peak
   of the average rate is twice the implied bottleneck IP rate; 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

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

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

   |<>| 4 packet sender interface rate bursts
   |<--->| Burst headway
   |<------------------------>| slowstart burst size
   |<---------------------------------------------->| slowstart headway
   \____________ _____________/                     \______ __ ...
                V                                          V
         One slowstart burst                   Repeated slowstart bursts

   Slowstart Burst Structure

                                 Figure 2

   Note that in conventional measurement practice, exponentially
   distributed intervals are often used to eliminate many sorts of
   correlations.  For the procedures above, the correlations are created
   by the network or protocol elements and accurately reflect their
   behavior.  At some point in the future, it will be desirable to
   introduce noise sources into the above pacing models, but they are
   not warranted at this time.

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 FSTDS specifying a constant window CBR tests MUST
   explicitly indicate under what conditions errors in the data cause
   tests to inconclusive.

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   Since constant window pseudo CBR testing is sensitive to RTT
   fluctuations it is less accurate at control 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
   designed to include two key events, the onset of queueing and the
   onset of packet loss or ECN 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 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 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 marks.  Such a
   stiffened transport explicitly violates mandatory Internet congestion
   control and is not suitable for in situ testing.  [RFC5681] 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

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   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
   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 TDS 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 traffic, the bursts should be
   synchronized across flows.

7.  Interpreting the Results

7.1.  Test outcomes

   To perform an exhaustive test of a complete network path, each test
   of the TDS 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

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   CBR (Section 6.2) by adding rate controls and detailed traffic
   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 delivery 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 effect on
   the observed packet delivery statistics.

   Note that for capacity tests, if the observed packet delivery
   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 traffic does not depend on
   network conditions or traffic received.  Any mechanism that
   introduces feedback between the paths measurements and the traffic
   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 inconclusive.

   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
   is sensitive to RTT (which is normally the case).

   One of the goals for evolving TDS 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 TDS or FSTDS.

   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 delivery statistics and ancillary
   metrics [RFC3148] for deeper study of the behavior of the network
   path and to measure the tools themselves.  Raw packet delivery

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   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
   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 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

   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 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 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:

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   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
   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 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] .

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   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:

   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 no 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 history
   buffer size, MUST be specified in a FSTDS.

8.  Diagnostic Tests

   The IP diagnostic tests below are organized by traffic pattern: basic
   data rate and packet delivery 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.

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   They must be fully specified in a FSTDS.  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
   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 Delivery Tests

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

   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 delivery
   statistics, is a low rate test designed for ongoing monitoring for
   changes in subpath quality.

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

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

   Fail: it is statistically significant that the observed interval
   between losses or ECN 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.

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   RFC 6673 [RFC6673] is appropriate for measuring packet delivery
   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.

   Since losses and ECN marks generally cause transport protocols to at
   least temporarily 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
   marks.  To pass this test the network MUST deliver target_window_size
   packets in target_RTT time without any losses or ECN 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 Delivery 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
   delivery 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

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   bufferbloat [wikiBloat] and inflict excess queuing delays on all
   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 losses or ECN marks.  For
   theses technologies, the discussion of queueing does not apply, but
   it is still required that the onset of losses or ECN 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

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   stiffened transport protocols case (with non-standard, aggressive
   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 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 inplace 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 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 [RFC2309], [I-D.ietf-aqm-recommendation]
   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 FSTDS.

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 traffic window increase on the previous RTT.  This could be

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   restated as non-decreasing subpath throughput at the onset of loss,
   which is easy to meet as long as discarding packets is not more
   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 traffic 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 forward path 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
   some 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 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.

   In general they are 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).  Space the bursts such that the average data rate is
   equal to the target_data_rate.

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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 delivery 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 marks
   is larger than the target_run_length.  Fail if it is statistically
   significant that the observed interval between losses or ECN marks is
   smaller than the target_run_length.

   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 slowstart_rate),
   until the first loss, stop, allow the network to drain and repeat,
   gathering statistics on the last packet delivered before the loss,
   the loss pattern, 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 and 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 would be best 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 very common today, but higher rates will become cost
   effective and can be expected to be dominant some time in the future.

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   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
   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

   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 TDS could include a table of pairs of derating
   parameters: what burst size to use as a fraction of the
   target_window_size, 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 delivery statistics meets the

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   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
      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  This test by itself is not sufficient: the standing window
      engineering tests are also needed to ensure that the subpath is
      well behaved at and beyond the onset of congestion.
   o  Assuming the subpath passes relevant standing window engineering
      tests (particularly that it has a progressive onset of loss at an
      appropriate queue depth) the passing sustained burst test is
      (believed to be) a sufficient verify that the subpath will not
      impair stream at the target performance under all conditions.
      Proving this statement will be subject of ongoing research.

   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 of
   serving any non-throughput maximizing traffic, such as streaming
   media, with some additional controls and instrumentation in the
   servers.  The essential requirement is that the traffic be
   constrained such that even with arbitrary application pauses, bursts
   and data rate fluctuations, the traffic stays 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

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   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
   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 traffic 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 host 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 TDS 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

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                        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 TDS would be to use the sustained
   burst test, described in Section 8.5.1.  Such a test would send 11
   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).

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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 TDS 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 TDS 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 TDS, 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 infinitessimally passing testbed do meet
   the performance targets.

   An infinitessimally passing testbed resembles a epsilon-delta proof
   in calculus.  Construct a test network such that all of the
   individual tests of the TDS 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

   For example, for the HD streaming video TDS 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 TDS 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 infinitessimally 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

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   preloading subpaths with cross traffic.  Note that is it important
   that a single environment be constructed which infinitessimally
   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 TDS 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

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
   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 Diagnostic Suite.  Each FSTDS 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.

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   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.

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.

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

   [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

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              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.

   [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.

   [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.

              Almes, G., Kalidindi, S., Zekauskas, M., and A. Morton, "A
              One-Way Loss Metric for IPPM", draft-ietf-ippm-2680-bis-02
              (work in progress), June 2015.

              Baker, F. and G. Fairhurst, "IETF Recommendations
              Regarding Active Queue Management",
              draft-ietf-aqm-recommendation-11 (work in progress),
              February 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.

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              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/

   [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.

Appendix A.  Model Derivations

   The reference target_run_length described in Section 5.2 is based on

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   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.

   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 TDS 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 it was assumed 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 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
   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
   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

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   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 rate is
   the target rate.  Thus we want Wmin = (2/3)*target_window_size.

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

   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.  Complex Queueing

   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
   when confronted with relatively widely spaced small ACKs.  These
   efficiency strategies are ubiquitous for half duplex, wireless and
   broadcast media.

   Altering the ACK stream generally has two consequences: it raises the
   implied bottleneck IP capacity, making slowstart burst at higher
   rates (possibly as high as the sender's interface rate) and it
   effectively raises the RTT by the average time that the ACKs and data
   were 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 end point 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, where they send an
   entire window of data as a single burst of the forward path, followed
   by the entire window of ACKs on the return path.  It is important to
   note that due to self clocking, ill conceived channel allocation
   mechanisms can increase the stress on upstream subpaths in a long
   path: they cause large and faster bursts.

   If a particular return path contains a subpath or device that alters
   the ACK stream, then the entire path from the sender up to the
   bottleneck must be tested at the burst parameters implied by the ACK

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   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 ACKs (relying on the
   cumulative nature of seg.ack to permit discarding some ACKs) is
   implies an effectively infinite Implied Bottleneck IP Capacity.

   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 Jul 6 13:49:30 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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