IP Performance Working Group                                   M. Mathis
Internet-Draft                                               Google, Inc
Intended status: Experimental                                  A. Morton
Expires: September 10, 2015                                    AT&T Labs
                                                           March 9, 2015

                  Model Based Bulk Performance Metrics


   We introduce a new class of model based metrics designed to determine
   if an end-to-end Internet path can meet predefined bulk transport
   performance targets 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, to accurately
   detect if any part of the infrastructure will prevent the full end-
   to-end paths traversing them from meeting the specified target

   The 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 performance.  The success
   criteria also does not depend on the details of the subpath,
   endsystems 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 diagnostic tests directly, but provides
   a framework for designing suites of diagnostics tests that are
   tailored to confirming that infrastructure can meet the target

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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Copyright Notice

   Copyright (c) 2015 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

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Table of Contents

   1.  Introduction . . . . . . . . . . . . . . . . . . . . . . . . .  5
     1.1.  TODO . . . . . . . . . . . . . . . . . . . . . . . . . . .  7
   2.  Terminology  . . . . . . . . . . . . . . . . . . . . . . . . .  7
   3.  New requirements relative to RFC 2330  . . . . . . . . . . . . 11
   4.  Background . . . . . . . . . . . . . . . . . . . . . . . . . . 12
     4.1.  TCP properties . . . . . . . . . . . . . . . . . . . . . . 13
     4.2.  Diagnostic Approach  . . . . . . . . . . . . . . . . . . . 14
   5.  Common Models and Parameters . . . . . . . . . . . . . . . . . 15
     5.1.  Target End-to-end parameters . . . . . . . . . . . . . . . 16
     5.2.  Common Model Calculations  . . . . . . . . . . . . . . . . 16
     5.3.  Parameter Derating . . . . . . . . . . . . . . . . . . . . 17
   6.  Common testing procedures  . . . . . . . . . . . . . . . . . . 18
     6.1.  Traffic generating techniques  . . . . . . . . . . . . . . 18
       6.1.1.  Paced transmission . . . . . . . . . . . . . . . . . . 18
       6.1.2.  Constant window pseudo CBR . . . . . . . . . . . . . . 19
       6.1.3.  Scanned window pseudo CBR  . . . . . . . . . . . . . . 19
       6.1.4.  Concurrent or channelized testing  . . . . . . . . . . 20
     6.2.  Interpreting the Results . . . . . . . . . . . . . . . . . 21
       6.2.1.  Test outcomes  . . . . . . . . . . . . . . . . . . . . 21
       6.2.2.  Statistical criteria for estimating run_length . . . . 22
       6.2.3.  Reordering Tolerance . . . . . . . . . . . . . . . . . 24
     6.3.  Test Preconditions . . . . . . . . . . . . . . . . . . . . 25
   7.  Diagnostic Tests . . . . . . . . . . . . . . . . . . . . . . . 25
     7.1.  Basic Data Rate and Delivery Statistics Tests  . . . . . . 26
       7.1.1.  Delivery Statistics at Paced Full Data Rate  . . . . . 26
       7.1.2.  Delivery Statistics at Full Data Windowed Rate . . . . 27
       7.1.3.  Background Delivery Statistics Tests . . . . . . . . . 27
     7.2.  Standing Queue Tests . . . . . . . . . . . . . . . . . . . 27
       7.2.1.  Congestion Avoidance . . . . . . . . . . . . . . . . . 29
       7.2.2.  Bufferbloat  . . . . . . . . . . . . . . . . . . . . . 29
       7.2.3.  Non excessive loss . . . . . . . . . . . . . . . . . . 30
       7.2.4.  Duplex Self Interference . . . . . . . . . . . . . . . 30
     7.3.  Slowstart tests  . . . . . . . . . . . . . . . . . . . . . 30
       7.3.1.  Full Window slowstart test . . . . . . . . . . . . . . 31
       7.3.2.  Slowstart AQM test . . . . . . . . . . . . . . . . . . 31
     7.4.  Sender Rate Burst tests  . . . . . . . . . . . . . . . . . 31
     7.5.  Combined and Implicit Tests  . . . . . . . . . . . . . . . 32
       7.5.1.  Sustained Bursts Test  . . . . . . . . . . . . . . . . 32
       7.5.2.  Streaming Media  . . . . . . . . . . . . . . . . . . . 33
   8.  An Example . . . . . . . . . . . . . . . . . . . . . . . . . . 34
   9.  Validation . . . . . . . . . . . . . . . . . . . . . . . . . . 36
   10. Security Considerations  . . . . . . . . . . . . . . . . . . . 37
   11. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 37
   12. IANA Considerations  . . . . . . . . . . . . . . . . . . . . . 38
   13. References . . . . . . . . . . . . . . . . . . . . . . . . . . 38
     13.1. Normative References . . . . . . . . . . . . . . . . . . . 38

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     13.2. Informative References . . . . . . . . . . . . . . . . . . 38
   Appendix A.  Model Derivations . . . . . . . . . . . . . . . . . . 40
     A.1.  Queueless Reno . . . . . . . . . . . . . . . . . . . . . . 41
   Appendix B.  Complex Queueing  . . . . . . . . . . . . . . . . . . 42
   Appendix C.  Version Control . . . . . . . . . . . . . . . . . . . 43
   Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 43

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1.  Introduction

   Bulk performance metrics evaluate an Internet path's ability to carry
   bulk data.  Model based bulk performance metrics rely on mathematical
   TCP models to design a targeted diagnostic suite (TDS) of IP
   performance tests which can be applied independently to each subpath
   of the full end-to-end path.  These targeted diagnostic suites allow
   independent tests of subpaths to accurately detect if any subpath
   will prevent the full end-to-end path from delivering bulk data at
   the specified performance target, independent of the measurement
   vantage points or other details of the test procedures used for each

   The end-to-end target performance is determined by the needs of the
   user or application, outside the scope of this document.  For bulk
   data transport, the primary 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
   entire end-to-end path over which the data traverses, these
   parameters must also be specified in advance.  They may reflect a
   specific real path through the Internet or an idealized path
   representing a typical user community.  The target values for these
   three parameters, Data Rate, RTT and MTU, inform the mathematical
   models used to design the TDS.

   Each IP diagnostic test in a TDS consists of a precomputed traffic
   pattern and statistical criteria for evaluating packet delivery.

   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
   computed in advance based on the three target parameters of the end-
   to-end path and independent of the properties of individual subpaths.
   As much as possible the measurement traffic is generated
   deterministically in ways that minimize the extent to which test
   methodology, measurement points, measurement vantage or path
   partitioning affect the details of the measurement traffic.

   Mathematical models are also used to compute the bounds on the packet
   delivery statistics for acceptable IP performance.  Since these
   statistics, such as packet loss, are typically aggregated from all
   subpaths of the end-to-end path, the end-to-end statistical bounds
   need to be apportioned as a separate bound for each subpath.  Note
   that links that are expected to be bottlenecks are expected to
   contribute a larger fraction of the total packet loss and/or delay.
   In compensation, other links have to be constrained to contribute

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   less packet loss and delay.  The criteria for passing each test of a
   TDS is an apportioned share of the total bound determined by the
   mathematical model from the end-to-end target performance.

   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.

   This document describes a framework for deriving traffic patterns and
   delivery statistics for model based metrics.  It does not fully
   specify any measurement techniques.  Important details such as packet
   type-p selection, sampling techniques, vantage selection, etc. are
   not specified here.  We imagine Fully Specified Targeted Diagnostic
   Suites (FSTDS), that define all of these details.  We use TDS to
   refer to the subset of such a specification that is in scope for this
   document.  A TDS includes the target parameters, documentation of the
   models and assumptions used to derive the diagnostic test parameters,
   specifications for the traffic and delivery statistics for the tests
   themselves, and a description of a test setup that can be used to
   validate the tests and models.

   Section 2 defines terminology used throughout this document.

   It has been difficult to develop Bulk Transport Capacity [RFC3148]
   metrics due to some overlooked requirements described in Section 3
   and some intrinsic problems with using protocols for measurement,
   described in Section 4.

   In Section 5 we describe the models and common parameters used to
   derive the targeted diagnostic suite.  In Section 6 we describe
   common testing procedures.  Each subpath is evaluated using suite of
   far simpler and more predictable diagnostic tests described in
   Section 7.  In Section 8 we present an example TDS that might be
   representative of HD video, and illustrate how MBM 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.

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

   Future documents may define model based metrics for other traffic

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   classes and application types, such as real time streaming media.

1.1.  TODO

   This section to be removed prior to publication.

   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 Mar 9 14:37:24 PDT 2015

2.  Terminology

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

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

   [data] sender:  Host sending data and receiving ACKs.
   [data] receiver:  Host receiving data and sending ACKs.
   subpath:  A portion of the full path.  Note that there is no
      requirement that subpaths be non-overlapping.
   Measurement Point:  Measurement points as described in [RFC7398].
   test path:  A path between two measurement points that includes a
      subpath of the end-to-end path under test, and could include
      infrastructure between the measurement points and the subpath.
   [Dominant] Bottleneck:  The Bottleneck that generally dominates
      traffic 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.
   cross traffic:  Other, potentially interfering, traffic competing for
      network resources (bandwidth and/or queue capacity).

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

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   Application Data Rate:  General term for the data rate as seen by the
      application above the transport layer.  This is the payload data
      rate, and excludes transport and lower level headers(TCP/IP or
      other protocols) and as well as retransmissions and other data
      that does not contribute to the total quantity of data delivered
      to the application.
   Link Data Rate:  General term for the data rate as seen by the link
      or lower layers.  The link data rate includes transport and IP
      headers, retransmissions and other transport layer overhead.  This
      document is agnostic as to whether the link data rate includes or
      excludes framing, MAC, or other lower layer overheads, except that
      they must be treated uniformly.
   end-to-end target parameters:  Application or transport performance
      goals for the end-to-end path.  They include the target data rate,
      RTT and MTU described below.
   Target Data Rate:  The application data rate, typically the ultimate
      user's performance goal.
   Target RTT (Round Trip Time):  The baseline (minimum) RTT of the
      longest end-to-end 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 authentic packets
      sizes: MTU sized packets on the forward path, ACK sized packets
      (typically header_overhead) on the return path.
   Target MTU (Maximum Transmission Unit):  The maximum MTU supported by
      the end-to-end path the over which the application expects to meet
      the target performance.  Assume 1500 Byte packet unless otherwise
      specified.  If some subpath forces a smaller MTU, then it becomes
      the target MTU, and all model calculations and subpath tests must
      use the same smaller MTU.
   Effective Bottleneck Data Rate:  This is the bottleneck data rate
      inferred from the ACK stream, by looking at how much data the ACK
      stream reports delivered per unit time.  If the path is thinning
      ACKs or batching packets the effective bottleneck rate can be much
      higher than the average link rate.  See Section 4.1 and Appendix B
      for more details.
   [sender | interface] rate:  The burst data rate, constrained by the
      data sender's interfaces.  Today 1 or 10 Gb/s are typical.
   Header_overhead:  The IP and TCP header sizes, which are the portion
      of each MTU not available for carrying application payload.
      Without loss of generality this is assumed to be the size for
      returning acknowledgements (ACKs).  For TCP, the Maximum Segment
      Size (MSS) is the Target MTU minus the header_overhead.

   Basic parameters common to models and subpath tests.  They are

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   described in more detail in Section 5.2.  Note that these are mixed
   between application transport performance (excludes headers) and link
   IP performance (includes headers).

   pipe size:  A general term for number of packets needed in flight
      (the window size) to exactly fill some network path or subpath.
      This is the window size which is normally the onset of queueing.
   target_pipe_size:  The number of packets in flight (the window size)
      needed to exactly meet the target rate, with a single stream and
      no cross traffic for the specified application target data rate,
      RTT, and MTU.  It is the amount of circulating data required to
      meet the target data rate, and 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 (to be) delivered between losses or ECN
      marks.  Nominally one over the loss or ECN marking probability, if
      there are independently and identically distributed.
   target_run_length:  The target_run_length is an estimate of the
      minimum number of good 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

   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 9.
   subpath_data_rate:  The maximum IP data rate supported by a subpath.
      This typically includes TCP/IP overhead, including headers,
      retransmits, etc.
   test_path_RTT:  The RTT between two measurement points using
      appropriate data and ACK packet sizes.
   test_path_pipe:  The amount of data necessary to fill a test path.
      Nominally the test path RTT times the subpath_data_rate (which
      should be part of the end-to-end subpath).
   test_window:  The window necessary to meet the target_rate over a
      subpath.  Typically test_window=target_data_rate*test_RTT/
      (target_MTU - header_overhead).

   Tests can be classified into groups according to their applicability.

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   Capacity tests:  determine if a network subpath has sufficient
      capacity to deliver the target performance.  As long as the test
      traffic is within the proper envelope for the target end-to-end
      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
      buffer space), and the test schedules must be balanced by their
   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.
   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.

   General Terminology:

   Targeted Diagnostic Test (TDS):  A set of IP Diagnostics designed to
      determine if a subpath can sustain flows at a specific
      target_data_rate over a path that has a target_RTT using
      target_MTU sided packets.
   Fully Specified Targeted Diagnostic Test:  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
   apportioned:  To divide and allocate, as in budgeting packet loss
      rates across multiple subpaths to accumulate below a specified
      end-to-end loss rate.
   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
      dependences, such that the resulting dependency graph is acyclic.

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   Bulk performance metrics:  Bulk performance metrics evaluate an
      Internet path's ability to carry bulk data, such as transporting
      large files, streaming (non-real time) video, and at some scales,
      web images and content.  (For very fast network, web performance
      is dominated by pure RTT effects).  The metrics presented in this
      document reflect the evolution of [RFC3148].
   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.  Our goal here is to mimic the range of common patterns
      (burst sizes and rates, etc), without tieing our applicability to
      specific applications, implementations or technologies, which are
      sure to become stale.
   delivery Statistics:  Raw 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.
   IP performance tests:  Measurements or diagnostic tests to determine
      delivery statistics.

3.  New requirements relative to RFC 2330

   Model Based Metrics are designed to fulfill some additional
   requirement 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.  Ideally
      they should also be differentiable: the metrics of a subpath
      should be
   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
      portion of the test path that is not under test between the MP and
      the part that under tests is effectively ideal, or is non ideal in
      ways that can be calibrated out of the measurements and the test
      RTT between the MPs is below some reasonable bound.
   o  Metrics 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

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      independence is key to this requirement.

4.  Background

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

   These properties are a consequence of the equilibrium behavior
   intrinsic to how all throughput optimizing protocols interact with
   the Internet.  The protocols rely on control systems based on
   multiple network estimators 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 non-linear, 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 delivery statistics (akin to
   the network estimators) do not affect the traffic or traffic patterns
   (bursts), which computed on the basis of the target performance.  In
   order for a network to pass, the resulting delivery statistics and
   corresponding network estimators have to be such that they would not
   cause the control systems slow the traffic below the target rate.

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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 systems is
   that it is entirely self clocked: The data transmissions are a
   reflection of the ACKs that were delivered by the network, the ACKs
   are a reflection of the data arriving from the network.

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

   During slowstart the data rate is doubled 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.

   Although the interface rate bursts are typically smaller than last
   burst of a slowstart, they are at a higher data rate so they

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   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 performance target, 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 delivery
      statistics.  (Implicitly derated by selecting the burst size).
   o  Infrequent sender interface rate full target_pipe_size bursts that
      do affect the delivery statistics.  (Target_run_length may be

4.2.  Diagnostic Approach

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

   By opening the protocol control loops, we remove most sources of
   temporal and spatial correlation in the traffic delivery statistics,
   such that each subpath's contribution to the end-to-end statistics
   can be assumed to be independent and stationary (The delivery
   statistics depend on the fine structure of the data transmissions,
   but not on long time scale state imbedded in the sender, receiver or
   other network components.)  Therefore each subpath's contribution to
   the end-to-end delivery statistics can be assumed to be independent,
   and spatial composition techniques such as [RFC5835] and [RFC6049]

   In typical networks, the dominant bottleneck contributes the majority
   of the packet loss and ECN marks.  Often the rest of the path makes
   insignificant contribution to these properties.  A TDS should
   apportion the end-to-end budget for the specified parameters
   (primarily packet loss and ECN marks) to each subpath or group of

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   subpaths.  For example the dominant bottleneck may be permitted to
   contribute 90% of the loss budget, while the rest of the path is only
   permitted to contribute 10%.

   A TDS or FSTDS MUST apportion all relevant packet delivery statistics
   between successive subpaths, such that the spatial composition of the
   apportioned metrics will yield end-to-end statics which are within
   the bounds determined by the models.

   A network 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
   1.  The raw link rate is higher than the target data rate.  See
       Section 7.1 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 7.1 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 7.3.
   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 7.4.
   5.  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 7.2.4.
   6.  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 7.2.

   Note that conditions 1 through 4 require load tests for confirmation,
   and thus need to be monitored on an ongoing basis.  Conditions 5 and
   6 require engineering tests.  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

5.  Common Models and Parameters

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5.1.  Target End-to-end parameters

   The target end-to-end parameters are the target data rate, target RTT
   and target MTU as defined in Section 2.  These parameters are
   determined by the needs of the application or the ultimate end user
   and the end-to-end Internet path 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 2 include the
   effective bottleneck data rate, the sender interface data rate and
   the TCP/IP header sizes (overhead).

   The target data rate must be smaller than all link data rates by
   enough headroom to carry the transport protocol overhead, explicitly
   including retransmissions and an allowance for fluctuations in the
   actual data rate, needed to meet the specified average rate.
   Specifying a target 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 real physical test, for in
   situ testing 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 6.1.4 applies.

5.2.  Common Model Calculations

   The end-to-end target parameters are used to derive the
   target_pipe_size and the reference target_run_length.

   The target_pipe_size, is the average window size in packets needed to
   meet the target rate, for the specified target RTT and MTU.  It is
   given by:

   target_pipe_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

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   conservative.  The alternate models described in Appendix A generally
   yield smaller run_lengths (higher acceptable loss or ECN marking
   rates), but may not apply in all situations.  A FSTDS that uses an
   alternate model MUST compare it to the reference target_run_length
   computed here.

   Reference target_run_length is derived as follows: assume the
   subpath_data_rate is infinitesimally larger than the target_data_rate
   plus the required header_overhead.  Then target_pipe_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 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_pipe_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_pipe_size)*(2*target_pipe_size) packets,
   which simplifies to:

   target_run_length = 3*(target_pipe_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_pipe_size and target_run_length,
   directly imply most of the individual parameters for the tests in
   Section 7.

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:

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   o  The TDS or FSTDS MUST document and justify the actual method used
      compute the derated metric parameters.
   o  The validation procedures described in Section 9 must be used to
      demonstrate the feasibility of meeting the performance targets
      with infrastructure that infinitesimally passes the derated tests.
   o  The validation process itself must be documented is such a way
      that other researchers can duplicate the validation experiments.

   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.

6.  Common testing procedures

6.1.  Traffic generating techniques

6.1.1.  Paced transmission

   Paced (burst) transmissions: send bursts of data on a timer to meet a
   particular target rate and pattern.  In all cases the specified data
   rate can either be the application or link rates.  Header overheads
   must be included in the calculations as appropriate.
   Headway:  Time interval between packets or bursts, 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
   Paced single packets:  Send individual packets at the specified rate
      or headway.
   Burst:  Send sender interface rate bursts on a timer.  Specify any 3
      of: average rate, packet size, burst size (number of packets) and
      burst headway (burst start to start).  These bursts are typically
      sent as back-to-back packets at the testers interface rate.
   Slowstart bursts:  Send 4 packet sender interface rate bursts at an
      average data rate equal to twice effective bottleneck link rate
      (but not more than the sender interface rate).  This corresponds
      to the average rate during a TCP slowstart when Appropriate Byte
      Counting [RFC3465] is present or delayed ack is disabled.  Note
      that if the effective bottleneck link rate is more than half of
      the sender interface rate, slowstart rate bursts become sender
      interface rate bursts.
   Repeated Slowstart bursts:  Slowstart bursts are typically part of
      larger scale pattern of repeated bursts, such as sending
      target_pipe_size packets as slowstart bursts on a target_RTT
      headway (burst start to burst start).  Such a stream has three
      different average rates, depending on the averaging interval.  At
      the finest time scale the average rate is the same as the sender

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      interface rate, at a medium scale the average rate is twice the
      effective bottleneck link rate and at the longest time scales the
      average rate is equal to the target data rate.

   Note that in conventional measurement theory, exponential
   distributions are often used to eliminate many sorts of correlations.
   For the procedures above, the correlations are created by the network
   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.

6.1.2.  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.  See the discussion of test outcomes in
   Section 6.2.1.

   Since constant window pseudo CBR testing is sensitive to RTT
   fluctuations it can not accurately control the data rate in
   environments with fluctuating delays.

6.1.3.  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_pipe_size delivered
   packets.  This mimics the additive increase phase of standard TCP
   congestion avoidance when delayed ACKs are in effect.  It normally

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   separates the the window increases by approximately twice the

   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 7.2 describe how to the partition the
   scans into regions and how to interpret the results.

6.1.4.  Concurrent or channelized testing

   The procedures described in this document are only directly
   applicable to single stream performance measurement, e.g. one TCP
   connection.  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 pin individual flows to
   specific channels to minimize reordering or other problems and
   second, TCP itself has scaling limits.  Although the former problem
   might be overcome through different design decisions, the later
   problem is more deeply rooted.

   All congestion control algorithms that are philosophically aligned
   with the standard [RFC5681] (e.g. claim some level of TCP
   friendliness) 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
   uniform response to congestion.

   There are a number of reasons to want to specify performance in term

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   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.  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
   connection might be the only feasible approach.

   If multiple connections 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 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.

6.2.  Interpreting the Results

6.2.1.  Test outcomes

   To perform an exhaustive test of an end-to-end network path, each
   test of the TDS is applied to each subpath of an end-to-end path.  If
   any subpath fails any test then an application running over the end-
   to-end path can also be expected to fail to attain the target
   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.

   For example consider a test that implements Constant Window Pseudo
   CBR (Section 6.1.2) 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 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
   delivery statistics.

   Note that for load tests, if the observed delivery statistics fail to
   meet the targets, 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.

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   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 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 the boundary between passing and inconclusive is at all
   sensitive to RTT.

   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 data delivery statistics for deeper
   study of the behavior of the network path and to measure the tools
   themselves.  Raw delivery statistics can help to drive tool
   evolution.  Under some conditions it might be possible to reevaluate
   the raw data for satisfying alternate performance targets.  However
   it is important to guard against sampling bias and other implicit
   feedback which can cause false results and exhibit measurement point
   vantage sensitivity.

6.2.2.  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 probabilities with the targets
   as the sample size grows?  How large a sample is needed to say that
   the measurements of packet transfer indicate a particular run length
   is present?

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   The generalized measurement can be described as recursive testing:
   send packets (individually or in patterns) and observe the packet
   delivery performance (loss ratio or other metric, any marking we

   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_pipe_size)^2 packets

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

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

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

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

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

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

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

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

   The SPRT specifies simple stopping rules:

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

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

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

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

6.2.3.  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.

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   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.

6.3.  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 of 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.

7.  Diagnostic Tests

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

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   There are a number of test details which are not fully defined here.
   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.

7.1.  Basic Data Rate and Delivery Statistics Tests

   We propose several versions of the basic data rate and 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 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 6.2.2 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 6.3 or neither run length statistical hypothesis was
   confirmed in the allotted test duration.

7.1.1.  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 6.1.1 with a
   burst size of 1 (single packets) or 2 (packet pairs).

   The test is considered to be inconclusive if the packet transmission

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   can not be accurately controlled for any reason.

   RFC 6673 [RFC6673] is appropriate for measuring delivery statistics
   at full data rate.

7.1.2.  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_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_pipe_size
   packets in target_RTT time without any losses or ECN marks at least
   once per two target_pipe_size round trips, in addition to meeting the
   run length statistical test.

7.1.3.  Background 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 delivery

7.2.  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

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   can prevent the transport from averaging at the target_data_rate.
   Losses that are too late indicate that the queue might be subject to
   bufferbloat [wikiBloat] and inflict excess queuing delays on all
   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 on the basis of how well it avoids each of these

   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 6.1.3 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_pipe_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 link 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

   For technologies that do not have conventional queues, start the scan
   at a window equal to the test_window=target_data_rate*test_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

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   likely to be relatively widely spaced and the details are typically
   dominated by the behavior of the transport protocol itself.  For the
   stiffened transport protocols case (with non-standard, aggressive
   congestion control algorithms) the details of periodic losses will be
   dominated by how the the window increase function responds to loss.

7.2.1.  Congestion Avoidance

   A link 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 link 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.

7.2.2.  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 performance, 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 link 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.

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7.2.3.  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
   restated as non-decreasing link throughput at the onset of loss,
   which is easy to meet as long as discarding packets in not more
   expensive than delivering them.  (Note when there is a transient drop
   in link throughput, outside of a standing queue test, a link 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].

7.2.4.  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 drains 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 link data rate.  This bound must be
   smaller than target_RTT/2 to avoid reverting to stop and wait
   behavior. (e.g.  Packets have to be released at least twice per RTT,
   to avoid stop and wait behavior.)

7.3.  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

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   equal to the target_data_rate.

7.3.1.  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_pipe_size
   total packets.

   Accumulate packet delivery statistics as described in Section 6.2.2
   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.

7.3.2.  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.

7.4.  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

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   effective and can be expected to be dominant some time in the future.

   Current standards permit TCP to send a full window bursts following
   an application pause.  (Congestion Window Validation [RFC2861], is
   not required, but even if was, it does not take effect until an
   application pause is longer than an RTO.)  Since full window bursts
   are consistent with standard behavior, it is desirable that the
   network be able to deliver such bursts, otherwise application pauses
   will cause unwarranted losses.  Note that the AIMD sawtooth requires
   a peak window that is twice target_pipe_size, so the worst case burst
   may be 2*target_pipe_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_pipe_size, and how much each burst size is permitted to reduce
   the run length, relative to to the target_run_length.

7.5.  Combined and Implicit Tests

   Combined tests efficiently confirm multiple network properties in a
   single test, possibly as a side effect of normally 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.

7.5.1.  Sustained Bursts Test

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

   Send target_pipe_size bursts of packets at server interface rate with
   target_RTT headway (burst start to burst start).  Verify that the

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   observed delivery statistics meets the target_run_length.

   Key observations:
   o  The subpath under test is expected to go idle for some fraction of
      the time: (subpath_data_rate-target_rate)/subpath_data_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_pipe_size*derate packet bursts
      every target_RTT*derate.
   o  When not derated, this test is the most strenuous load test.
   o  A link that passes this test is likely to be able to sustain
      higher rates (close to subpath_data_rate) 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 link is well
      behaved at and beyond the onset of congestion.
   o  Assuming the link 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.

7.5.2.  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 7.1.2 and Section 7.4 or Section 7.5.1.  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 7.4 and the sender rate burst sizes are
   implicitly derated by the serving_window_clamp divided by the
   target_pipe_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.)

   Note that it is important that the target_data_rate be above the
   actual average rate needed by the application so it can recover after
   transient pauses caused by congestion or the application itself.

   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.

8.  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_pipe_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 7.5.1.  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-ends loss budget,
   independent subpath tests could be implemented by apportioning the
   loss rate 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 hight loss rates 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 links 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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9.  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 links that
   will prevent authentic applications using state-of-the-art protocol
   implementations from meeting the specified performance targets.  This
   correctness criteria is potentially difficult to prove, because it
   implicitly requires validating a TDS against all possible links 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 end-to-end target parameters over such a
   network.  The workloads should include multiple types of streaming
   media and transaction oriented short flows (e.g. synthetic web
   traffic ).

   For example, for the HD streaming video TDS described in Section 8,
   the link layer bottleneck data rate should be exactly the header
   overhead above 2.5 Mb/s, the per packet random background loss
   probability 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

10.  Security Considerations

   Measurement is often used to inform business and policy decisions,
   and as a consequence is potentially subject to manipulation for
   illicit gains.  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 local to an ISP and end-to-end.
   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.

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

12.  IANA Considerations

   This document has no actions for IANA.

13.  References

13.1.  Normative References

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

13.2.  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
              for TCP", RFC 4015, February 2005.

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   [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.

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

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

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

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

   [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.

              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.

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


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              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
   very conservative assumptions: that all window above target_pipe_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

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   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 loss probability spans at least 8 orders
   of magnitude.  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 might represent 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 link 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
   queueing case where Wmin = Target_pipe_size, we want the average of
   Wmin and 2*Wmin to be the target_pipe_size, so the average rate is
   the target rate.  Thus we want Wmin = (2/3)*target_pipe_size.

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

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   Substituting these together we get:

   target_run_length = (4/3)(target_pipe_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
   effective bottleneck data rate, 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 links in a long path:
   they cause large and faster bursts.

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

   Holding data or ACKs for channel allocation or other reasons (such as

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   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 Mar 9 14:37:24 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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