IP Performance Working Group                                   M. Mathis
Internet-Draft                                               Google, Inc
Intended status: Experimental                                  A. Morton
Expires: April 24, 2014                                        AT&T Labs
                                                        October 21, 2013

                  Model Based Bulk Performance Metrics


   We introduce a new class of model based metrics designed to determine
   if a long network path can meet predefined end-to-end application
   performance targets by applying a suite of IP diagnostic tests to
   successive subpaths.  The subpath at a time tests are designed to
   exclude all known conditions which might prevent the full end-to-end
   path from meeting the user's target application performance.

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

   A natural consequence of this methodology is verifiable network
   measurement: measurements from any given vantage point can be
   verified by repeating them from other vantage points.

   Formatted: Mon Oct 21 15:42:35 PDT 2013

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   material or to cite them other than as "work in progress."

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Table of Contents

   1.  Introduction . . . . . . . . . . . . . . . . . . . . . . . . .  5
     1.1.  TODO . . . . . . . . . . . . . . . . . . . . . . . . . . .  6
   2.  Terminology  . . . . . . . . . . . . . . . . . . . . . . . . .  6
   3.  New requirements relative to RFC 2330  . . . . . . . . . . . .  9
   4.  Background . . . . . . . . . . . . . . . . . . . . . . . . . . 10
     4.1.  TCP properties . . . . . . . . . . . . . . . . . . . . . . 12
   5.  Common Models and Parameters . . . . . . . . . . . . . . . . . 14
     5.1.  Target End-to-end parameters . . . . . . . . . . . . . . . 14
     5.2.  Common Model Calculations  . . . . . . . . . . . . . . . . 15
     5.3.  Parameter Derating . . . . . . . . . . . . . . . . . . . . 16
   6.  Common testing procedures  . . . . . . . . . . . . . . . . . . 16
     6.1.  Traffic generating techniques  . . . . . . . . . . . . . . 16
       6.1.1.  Paced transmission . . . . . . . . . . . . . . . . . . 16
       6.1.2.  Constant window pseudo CBR . . . . . . . . . . . . . . 17
       6.1.3.  Scanned window pseudo CBR  . . . . . . . . . . . . . . 18
       6.1.4.  Concurrent or channelized testing  . . . . . . . . . . 18
       6.1.5.  Intermittent Testing . . . . . . . . . . . . . . . . . 19
       6.1.6.  Intermittent Scatter Testing . . . . . . . . . . . . . 20
     6.2.  Interpreting the Results . . . . . . . . . . . . . . . . . 20
       6.2.1.  Test outcomes  . . . . . . . . . . . . . . . . . . . . 20
       6.2.2.  Statistical criteria for measuring run_length  . . . . 21
       6.2.3.  Reordering Tolerance . . . . . . . . . . . . . . . . . 23
     6.3.  Test Qualifications  . . . . . . . . . . . . . . . . . . . 23
       6.3.1.  Verify the Traffic Generation Accuracy . . . . . . . . 23
       6.3.2.  Verify the absence of cross traffic  . . . . . . . . . 24
       6.3.3.  Additional test preconditions  . . . . . . . . . . . . 25
   7.  Diagnostic Tests . . . . . . . . . . . . . . . . . . . . . . . 25
     7.1.  Basic Data Rate and Run Length Tests . . . . . . . . . . . 25
       7.1.1.  Run Length at Paced Full Data Rate . . . . . . . . . . 26
       7.1.2.  run length at Full Data Windowed Rate  . . . . . . . . 26
       7.1.3.  Background Run Length Tests  . . . . . . . . . . . . . 26
     7.2.  Standing Queue tests . . . . . . . . . . . . . . . . . . . 26
       7.2.1.  Congestion Avoidance . . . . . . . . . . . . . . . . . 28
       7.2.2.  Bufferbloat  . . . . . . . . . . . . . . . . . . . . . 28
       7.2.3.  Non excessive loss . . . . . . . . . . . . . . . . . . 28
       7.2.4.  Duplex Self Interference . . . . . . . . . . . . . . . 28
     7.3.  Slowstart tests  . . . . . . . . . . . . . . . . . . . . . 29
       7.3.1.  Full Window slowstart test . . . . . . . . . . . . . . 29
       7.3.2.  Slowstart AQM test . . . . . . . . . . . . . . . . . . 29
     7.4.  Sender Rate Burst tests  . . . . . . . . . . . . . . . . . 29
     7.5.  Combined Tests . . . . . . . . . . . . . . . . . . . . . . 30
       7.5.1.  Sustained burst test . . . . . . . . . . . . . . . . . 30
       7.5.2.  Live Streaming Media . . . . . . . . . . . . . . . . . 31
   8.  Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
     8.1.  Near serving HD streaming video  . . . . . . . . . . . . . 32
     8.2.  Far serving SD streaming video . . . . . . . . . . . . . . 32

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     8.3.  Bulk delivery of remote scientific data  . . . . . . . . . 33
   9.  Validation . . . . . . . . . . . . . . . . . . . . . . . . . . 33
   10. Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . 34
   11. Informative References . . . . . . . . . . . . . . . . . . . . 35
   Appendix A.  Model Derivations . . . . . . . . . . . . . . . . . . 36
     A.1.  Aggregate Reno . . . . . . . . . . . . . . . . . . . . . . 37
     A.2.  CUBIC  . . . . . . . . . . . . . . . . . . . . . . . . . . 37
   Appendix B.  Version Control . . . . . . . . . . . . . . . . . . . 38
   Authors' Addresses . . . . . . . . . . . . . . . . . . . . . . . . 38

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1.  Introduction

   Model based bulk performance metrics evaluate an Internet path's
   ability to carry bulk data.  TCP models are used 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.  A
   targeted diagnostic suite is constructed such that independent tests
   of the subpaths will accurately predict if the full end-to-end path
   can deliver bulk data at the specified performance target,
   independent of the measurement vantage points or other details of the
   test procedures used to measure each subpath.

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

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

   Models are also used to compute the bounds on the packet delivery
   statistics for acceptable the IP performance.  The criteria for
   passing each test are determined from the end-to-end target
   performance and are independent of the subpath under test.  In
   addition to passing or failing, a test can be inconclusive if the
   precomputed traffic pattern was not authentically generated, test
   preconditions were not met or the measurement results were not
   statistically significant.

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

   This document describes a framework for deriving the traffic 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 out

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   of scope for this document.  We imagine Fully Specified Targeted
   Diagnostic Suites (FSTDS), that fully defines 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 specification for the
   traffic and delivery statistics for the diagnostic tests themselves,
   documentation of the models and any assumptions or derating used to
   derive the test parameters and a description of the test setup used
   to calibrate the models, as described in later sections.

   Section 2 defines terminology used throughout this document.

   It has been difficult to develop BTC metrics due to some overlooked
   requirements described in Section 3 and some intrinsic problems with
   using protocols for measurement, described in Section 4.

   In 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 three example TDS, one that might
   be representative of HD video, when served fairly close to the user,
   a second that might be representative of standard video, served from
   a greater distance, and a third that might be representative of an
   network designed to support high performance bulk download.

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

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

1.1.  TODO

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

   Formatted: Mon Oct 21 15:42:35 PDT 2013

2.  Terminology

   Terminology about paths, etc.  See [RFC2330] and

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

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

   Application Data Rate  General term for the data rate as seen by the
      application above the transport layer.  This is the payload data
      rate, and excludes TCP/IP (or other protocol) headers and
   Link Data Rate  General term for the data rate as seen by the link or
      lower layers.  It includes transport and IP headers, retransmits
      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
   end-to-end target parameters:  Application or transport performance
      goals for the end-to-end path.  They include the target data rate,
      RTT and MTU described below.
   Target Data Rate:  The application or ultimate user's performance
      goal.  When converted to link data rate, it must be slightly
      smaller than the actual link data rate, otherwise there is no
      margin for compensating for RTT or other path properties.  These
      test will be excessively brittle if the target data rate does not
      include any built in headroom.

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   Target RTT (Round Trip Time):  The baseline (minimum) RTT of the
      longest end-to-end path the over which the application expects to
      meet the target performance.  This must be specified considering
      authentic packets sizes: MTU sized packets on the forward path,
      header_overhead sized packets on the return (ACK) 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 Bytes per 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
      that might be inferred from the ACK stream, by looking at how much
      data the ACK stream reports was delivered per unit time.  See
      Section 4.1 for more details.
   [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
   described in more detail in Section 5.2.

   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 in normally the onset of queueing.
   target_pipe_size:  The number of packets in flight (the window size)
      needed to exactly meet the target rate, with a single stream and
      no cross traffic for the specified target data rate, RTT and MTU.
   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.
   target_run_length  Required run length computed from the target data
      rate, RTT and MTU.

   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.

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   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 (using appropriate packet sizes) between two
      measurement points.
   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/

   Tests can be classified into groups according to their applicability

   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 must be below the
      threshold computed by the model.  As such, they reflect parameters
      that can transition from passing to failing as a consequence of
      additional presented load or the actions of other network users.
      By definition, capacity tests also consume significant network
      resources (data capacity and/or buffer space), and the test
      schedules must be balanced by their cost.
   Monitoring tests  are design to capture the most important aspects of
      a capacity test, but without causing unreasonable ongoing load
      themselves.  As such they may miss some details of the network
      performance, but can serve as a useful reduced cost proxy for a
      capacity test.
   Engineering tests  evaluate how network algorithms (such as AQM and
      channel allocation) interact with 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 other 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 results in the
      presence of bursty cross traffic.  It is important that
      engineering tests be performed under a wide range of conditions,
      including both in situ and bench testing, and 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.

3.  New requirements relative to RFC 2330

   [Move this entire section to a future paper]

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   Model Based Metrics are designed to fulfill some additional
   requirement that were not recognized at the time RFC 2330 [RFC2330]
   was written.  These missing requirements may have significantly
   contributed to policy difficulties in the IP measurement space.  Some
   additional requirements are:
   o  Metrics must be actionable by the ISP - they have to be
      interpreted in terms of behaviors or properties at the IP or lower
      layers, that an ISP can test, repair and verify.
   o  Metrics must be vantage point invariant over a significant range
      of measurement point choices (e.g., measurement points as
      described in [I-D.morton-ippm-lmap-path]), including off path
      measurement points.  The only requirements on MP selection should
      be that the portion of the path that is not under test is
      effectively ideal (or is non ideal in calibratable ways) and the
      RTT between MPs is below some reasonable bound.
   o  Metrics must be repeatable by multiple parties.  It must be
      possible for different parties to make the same measurement and
      observe the same results.  In particular it is specifically
      important that both a consumer (or their delegate) and ISP be able
      to perform the same measurement and get the same result.

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

4.  Background

   [Move to a future paper, abridge here, ]

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

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      measured particles.  For network measurement you can not in
      general determine the relative "elasticity" of the measurement
      traffic and cross traffic, so you can not even gage the relative
      magnitude of their effects on each other.

   The MBM approach is to "open loop" TCP by precomputing traffic
   patterns that are typically generated by TCP operating at the given
   target parameters, and evaluating delivery statistics (losses, 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 network behavior of the subpath under test.
   These patterns are manipulated to probe the network to verify that it
   can deliver all of the traffic patterns that a transport protocol is
   likely to generate under normal operation at the target rate and RTT.

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

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

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

   A singleton [RFC2330] measurement is a pass/fail evaluation of a
   given path or subpath at a given performance.  Note that measurements
   to confirm that a link passes at one particular performance might not
   be be useful to predict if the link will pass at a different

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   A TDS does have several valuable properties, such as natural ways to
   define several different composition metrics [RFC5835].

   [Add text on algebra on metrics (A-Frame from [RFC2330]) and
   tomography.]  The Spatial Composition of fundamental IPPM metrics has
   been studied and standardized.  For example, the algebra to combine
   empirical assessments of loss ratio to estimate complete path
   performance is described in section 5.1.5. of [RFC6049].  We intend
   to use this and other composition metrics as necessary.

   We are developing a tool that can perform many of the tests described

4.1.  TCP properties

   [Move this entire section to a future paper]

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

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

   During slowstart the data rate is doubled 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 (the window is exactly twice the pipe
   size) so when the window is halved, the new window will be exactly
   the pipe size.

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

   Note that if the bottleneck data rate is significantly slower than
   the rest of the path, the slowstart bursts will not cause significant
   queues anywhere else along the path; they primarily exercise the
   queue at the dominant bottleneck.  Furthermore, although the
   interface rate bursts caused by the application are likely to be
   smaller than last burst of a slowstart, they are at a higher rate so
   they can exercise queues at arbitrary points along the "front path"
   from the data sender up to and including the queue at the bottleneck.

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

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

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

   To verify that a path can meet the performance target, it is

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   necessary to independently confirm that the entire path can tolerate
   bursts in the dimensions that are likely to be induced by the
   application and any data or ACK scheduling anywhere in the path.  Two
   common cases are the most important: slowstart bursts at twice the
   effective bottleneck data rate; and somewhat smaller sender interface
   rate bursts.

   The slowstart rate bursts must be at least as least as large
   target_pipe_size packets and should be twice as large (so the peak
   queue occupancy at the dominant bottleneck would be approximately

   There is no general model for how well the network needs to tolerate
   sender interface rate bursts.  All existing TCP implementations send
   full sized full rate bursts under some typically uncommon conditions,
   such as application pauses that approximately match the RTT, or when
   ACKs are lost or thinned.  Strawman: partial window bursts (some
   fraction of target_pipe_size) should be tolerated without
   significantly raising the loss probability.  Full target_pipe_size
   bursts may slightly increase the loss probability.  Interface rate
   bursts as large as twice target_pipe_size should not cause
   deterministic packet drops.

5.  Common Models and Parameters

5.1.  Target End-to-end parameters

   The target end to end parameters are the target data rate, target RTT
   and target MTU as defined in Section 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 the upper layer: payload bytes delivered to the application,
   above TCP.  They exclude overheads associated with TCP and IP
   headers, retransmitts and other protocols (e.g.  DNS).  In addition,
   other end-to-end parameters include the effective bottleneck data
   rate, the sender interface data rate and the TCP/IP header sizes

   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 [unlike earlier drafts].  If a subpath requires multiple
   connections in order to meet the specified performance, that must be

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   stated explicitly and the procedure described in Section 6.1.4

5.2.  Common Model Calculations

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

   target_pipe_size = target_rate * target_RTT / ( target_MTU -
   header_overhead )

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

   The reference target_run_length, is a very conservative model for the
   minimum required spacing between losses or ECN marks.  The reference
   target_run_length can derived as follows: assume the
   subpath_data_rate is infinitesimally larger than the target_data_rate
   plus the required header overheads.  Then target_pipe_size also
   predicts the onset of queueing.  If the transport protocol (e.g.
   TCP) has a window size that is larger than the target_pipe_size, the
   excess packets will raise the RTT, typically by forming a standing
   queue at the bottleneck.

   Assume the transport protocol is using standard Reno style Additive
   Increase, Multiplicative Decrease congestion control [RFC5681] and
   the receiver is using standard delayed ACKs.  With delayed ACKs there
   must be 2*target_pipe_size roundtrips between losses.  Otherwise the
   multiplicative window reduction triggered by a loss would cause the
   network to be underfilled.  We derive the number of packets between
   losses from the area under the AIMD sawtooth following [MSMO97].
   They must be no more frequent than every 1 in
   (3/2)*target_pipe_size*(2*target_pipe_size) packets.  This simplifies

   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 less
   conservative model is used, a fully specified TDS or FSTDS MUST
   document the actual method for computing target_run_length along with
   the rationale for the underlying assumptions and the ratio of chosen
   target_run_length to the reference target_run_length calculated

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   These two parameters, target_pipe_size and target_run_length,
   directly imply most of the individual parameters for the tests below.
   Target_pipe_size is the window size, the amount of circulating data
   required to meet the target data rate, and implies the scale of the
   bursts that the network might experience.  Target_run_length is the
   amount of data required between losses or ECN marks standard for
   standard congestion control.

   The individual parameters are for each diagnostic test is described
   below.  In a few case there are not well established models for what
   is considered correct network operation.  In many of these cases the
   problems might either be partially mitigated by future improvements
   to TCP implementations.

5.3.  Parameter Derating

   Since some aspects of the models are very conservative, this
   framework permits some latitude in derating test parameters.  Rather
   than trying to formalize more complicated models we permit some test
   parameters to be relaxed as long as they meet some additional
   procedural constraints:
   o  The TDS or FSTDS MUST document and justify the actual method used
      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 infinitessimally passes the derated
   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 include derating as a way to indicate flexibility in the

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.

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   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 [ABC] is present or delayed ack is disabled.
   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 time scale.
      At the finest time scale the average rate is the same as the
      sender interface rate, at a medium scale the average rate is twice
      the effective bottleneck link rate and at the longest time scales
      the average rate is the target data rate.

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

6.1.2.  Constant window pseudo CBR

   Implement pseudo constant bit rate by running a standard protocol
   such as TCP with a fixed bound on the window size.  The rate is only
   maintained in average over each RTT, and is subject to limitations of
   the transport protocol.

   The bound on the window size is computed from the target_data_rate
   and the actual RTT of the test path.

   If the transport protocol fails to maintain the test rate within
   prescribed data rates, the test MUST NOT be considered passing.  If
   there is a signature of a network problem (e.g. the run length is too
   small) then the test can be considered to fail.  Since packet loss
   and ECN marks are required to reduce the data rate for standard
   transport protocols, the test specification must include suitable
   allowances in the prescribed data rates.  If there is not sufficient
   signature of a network problem, then failing to make the prescribed
   data rate must be considered inconclusive.  Otherwise there are some
   cases where tester failures might cause false negative test results.

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6.1.3.  Scanned window pseudo CBR

   Same as the 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 for every 2*target_pipe_size delivered
   packets.  This mimics the additive increase phase of standard
   congestion avoidance and normally separates the the window increases
   by approximately twice the target_RTT.

   There are two versions of this test: one built by applying a window
   clamp to standard congestion control and one one 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
   looses 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 (attempts) to
   maintain 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.
   It is only appropriate for engineering testing under laboratory
   conditions.  The Windowed Ping tools implemented such a test [WPING].
   This tool has been updated and is under test.[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 his 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 stream 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 solve 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

   All standard [RFC 5681] and de facto standard [CUBIC] congestion
   control algorithms have scaling limits, in the sense that as a
   network over a fixed RTT and MTU gets faster all congestion control
   algorithms get less accurate.  In general their noise immunity drops
   (a single packet drop should have less effect as individual packets

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   become smaller relative to the window size) and the control frequency
   of the AIMD sawtooth also drops, meaning that as TCP is using more
   total capacity it gets less information about the state of the
   network and other traffic.  These properties are a direct consequence
   of the original Reno design and are implicitly required by the
   requirement that all transport protocols be "TCP friendly"
   [Guidelines] There are a number of reason to want to specify
   performance in term of multiple concurrent flows.  Although there are
   a number of downsides to @@@@

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

   Note that the current proposal for httpbis [SPDY] is specifically
   designed to work best with a single TCP connection per client server
   pair, because it uses adaptive compression which requires sending
   separate compression dictionaries per connection.  As long as TCP can
   use IW10 and some of the transport parameter can be cached, multiple
   connections provide a negative gain, due to the replicated
   compression overhead.

   The specification to use multiple connections is not recommended for
   data rates below several Mb/s, which can be attained with run lengths
   under 10000.  Since run length goes as the square of the data rates,
   at higher rates (see Section 8.3) the run lengths can be unfeasibly
   large, and multiple connection might be the only feasible approach.

6.1.5.  Intermittent Testing

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

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

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   properties (e.g. a modem retraining to an out of contract lower

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

6.1.6.  Intermittent Scatter Testing

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

6.2.  Interpreting the Results

6.2.1.  Test outcomes

   A singleton is a pass/fail measurement of a subpath.  If any subpath
   fails any test then the end-to-end path is also expected to fail to
   attain the target performance under some conditions.

   In addition we use "inconclusive outcome" to indicate that a test
   failed to attain the required test conditions.  A test is
   inconclusive if the precomputed traffic pattern was not authentically
   generated, test preconditions were not met or the measurement results
   were not statistically significantly.

   This is important to the extent that the diagnostic tests use
   protocols which themselves include built in control systems which
   might interfere with some aspect of the test.  For example consider a
   test that is implemented by adding rate controls and loss
   instrumentation to TCP: meeting the run length specification while
   failing to attain the specified data rate 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 run length
   measurement.  (Note that if the measured run length was too small,
   the test can be considered to have failed because it doesn't really
   matter that the test didn't attain the required data rate).

   The vantage independence properties of Model Based Metrics depends on
   the accuracy of the distinction between conclusive (pass or fail) and
   inconclusive tests.  One way to view inconclusive tests is that they
   reflect situations where the signature is ambiguous between problems
   with the the subpath and problems with the diagnostic test itself.
   One of the goals for evolving diagnostic test designs will be to keep

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   sharpening this distinction.

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

   Note that procedures that attempt to sweep the target parameter space
   to find the bounds on some parameter (for example to find the highest
   data rate for a subpath) are likely to break the location independent
   properties of Model Based Metrics, because the boundary between
   passing and inconclusive is extremely likely to be RTT sensitive,
   because TCP's ability to compensate for problems scales with the
   number of round trips per second.

6.2.2.  Statistical criteria for measuring run_length

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

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

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

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

   H0:  no more than one defect 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_defect_probability.  Based on
   analysis of typical values and practical limits on measurement

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   duration, we choose four times the H0 probability:

   H1:  one or more defects 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], which also starts with a pair of
   hypothesis specified as above:

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

   Acceptance line:  Xa = -h1 + sn
   Rejection line:  Xr = h2 + sn
   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 error.

   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, in the add-on package for Cross-Validation via Sequential
   Testing (CVST) [http://www.r-project.org/] [Rtool] [CVST] .

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

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

6.2.3.  Reordering Tolerance

   All tests must be instrumented for reordering [RFC4737].

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

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

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

   NB: Traditional TCP implementations were not compatible with this
   metric, however newer implementations still need to be evaluated

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

6.3.  Test Qualifications

   This entire section might be summarized as "needs to be specified in
   a FSTDS"

   Things to monitor before, during and after a test.

6.3.1.  Verify the Traffic Generation Accuracy

   [Excess detail for this doc.  To be summarized]

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

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

6.3.2.  Verify the absence of cross traffic

   [Excess detail for this doc.  To be summarized]

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

   @@@@ Need to distinguish between ISP managed sharing and unmanaged
   sharing. e.g.  WiFi

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

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

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

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

   One possible method is an adaptation of: www-didc.lbl.gov/papers/
   SCNM-PAM03.pdf D Agarwal etal.  "An Infrastructure for Passive
   Network Monitoring of Application Data Streams".  Use the same

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   technique as that paper to trigger the capture of SNMP statistics for
   the link.

6.3.3.  Additional test preconditions

   [Excess detail for this doc.  To be summarized]

   Send pre-load traffic as needed to activate radios with a sleep mode,
   or other "reactive network" elements (term defined in

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

7.  Diagnostic Tests

   The diagnostic tests are organized by which properties are being
   tested: run length, standing queues; slowstart bursts; sender rate
   bursts; and combined tests.  The combined tests reduce overhead at
   the expense of conflating the signatures of multiple failures.

7.1.  Basic Data Rate and Run Length Tests

   We propose several versions of the basic data rate and run length
   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 auditing process.  The third, background run length, 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 run length is
   larger than the target_run_length.

   Fail: it is statistically significant that the observed run length 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

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   Section 6.3 or neither run length statistical hypothesis was
   confirmed in the allotted test duration.

7.1.1.  Run Length 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).

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

7.1.2.  run length at Full Data Windowed Rate

   Confirm that the observed run length is at least the
   target_run_length while sending at an average rate 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

   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.  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 Run Length 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.

   Existing loss metrics such as [RFC 6673] might be appropriate for
   measuring background run length.

7.2.  Standing Queue tests

   These test confirm that the bottleneck is well behaved across the
   onset of packet loss, which typically follows after the onset of

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   queueing.  Well behaved generally means lossless for transient
   queues, but once the queue has been sustained for a sufficient period
   of time (or a sufficient queue depth) there should be a small number
   of losses to signal to the transport protocol that it should reduce
   its window.  Losses that are too early can prevent the transport from
   averaging at the target_data_rate.  Losses that are too late indicate
   that the queue might be subject to bufferbloat [Bufferbloat] and
   inflict excess queuing delays on all flows sharing the bottleneck.
   Excess losses make loss recovery problematic for the transport
   protocol.  Non-linear or erratic RTT fluctuations suggest poor
   interactions between the channel acquisition systems and the
   transport self clock.  All of the tests in this section use the same
   basic scanning algorithm but score the link on the basis of how well
   it avoids each of these problems.

   For some technologies the data might not be subject to increasing
   delays, in which case the data rate will vary with the window size
   all the way up to the onset of 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 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 the window size.  The
   precise transition from one region to the other can be identified by
   the maximum network power, defined to be the ratio data rate over the

   For technologies that do not have conventional queues, start the scan
   at a window equal to the test_window, i.e. starting at the target
   rate, instead of the power point.

   If there is random background loss (e.g. bit errors, etc), precise
   determination of the onset of packet loss may require multiple scans.
   Above the onset of loss, all transport protocols are expected to
   experience periodic losses.  For the stiffened transport case they
   will be determined by the AQM algorithm in the network or the details
   of how the the window increase function responds to loss.  For the

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   standard transport case the details of periodic losses are typically
   dominated by the behavior of the transport protocol itself.

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 power point
   (or test_window) and the first loss or ECN mark.  If this test is
   implemented using a standards congestion control algorithm with a
   clamp, it can be used in situ in the production internet as a
   capacity test.  For an example of such a test see [NPAD].

7.2.2.  Bufferbloat

   This test confirms that there is some mechanism to limit buffer
   occupancy (e.g. prevents bufferbloat).  Note that this is not
   strictly a requirement for single stream bulk performance, however if
   there is no mechanism to limit buffer occupancy then a single stream
   with sufficient data to deliver is likely to cause the problems
   described in [RFC 2309] and [Bufferbloat].  This may cause only minor
   symptoms for the dominant flow, but has the potential to make the
   link unusable for all other flows and applications.

   Pass if the onset of loss is before a standing queue has introduced
   more delay than than twice target_RTT, or other well defined 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.
   Note that 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.

7.2.3.  Non excessive loss

   This test confirm that the onset of loss is not excessive.  Pass if
   losses are bound by the the fluctuations in the cross traffic, such
   that transient load (bursts) do not cause dips in aggregate raw
   throughput. e.g. pass as long as the losses are no more bursty than
   are expected from a simple drop tail queue.  Although this test could
   be made more precise it is really included here for pedantic

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.  Fail 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 needs further testing.

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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.

   They are deemed inconclusive if the elapsed time to send the data
   burst is not less than half of the time to receive the ACKs. (i.e.
   sending data too fast is ok, but sending it slower than twice the
   actual bottleneck rate as indicated by the ACKs is deemed
   inconclusive).  Space the bursts such that the average data rate is
   equal to the target_data_rate.

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 run length is larger than the
   target_run_length.  Fail if it is statistically significant that the
   observed run length 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 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)

   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 nominally out
   of scope.

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

   Current standards permit TCP to send a full window bursts following
   an application pause.  Congestion Window Validation [RFC 2861], is
   not required, but even if was it does not take effect until an
   application pause is longer than an RTO.  Since this is standard
   behavior, it is desirable that the network be able to deliver it,
   otherwise application pauses will cause unwarranted losses.

   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 efficiently deliver
   all data as a series of bursts.

   For this reason, this is the only test for which we explicitly
   encourage detrateing.  A TDS should 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. @@@@ Needs more
   work and experimentation.

7.5.  Combined Tests

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

7.5.1.  Sustained burst test

   Send target_pipe_size*derate sender interface rate bursts every
   target_RTT*derate, for derate between 0 and 1.  Verify that the
   observed run length meets target_run_length.  Key observations:
   o  This test is subpath RTT invariant, as long as the tester can
      generate the required pattern.
   o  The subpath under test is expected to go idle for some fraction of
      the time: (subpath_data_rate-target_rate)/subpath_data_rate.
      Failing to do so suggests a problem with the procedure.

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   o  This test is more strenuous than the slowstart tests: they are not
      needed if the link passes this test with derate=1.
   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
      smaller than the target_RTT.  Offsetting this performance
      underestimation is part of the rationale behind permitting
      derating in general.
   o  This test can be implemented with standard instrumented TCP[RFC
      4898], using a specialized measurement application at one end and
      a minimal service at the other end [RFC 863, RFC 864].  It may
      require tweaks to the TCP implementation.
   o  This test is efficient to implement, since it does not require
      per-packet timers, and can make use of TSO in modern NIC hardware.
   o  This test is not totally sufficient: the standing window
      engineering tests are also needed to be sure that the link is well
      behaved at and beyond the onset of congestion.
   o  This one test can be proven to be the one capacity test to
      supplant them all.

7.5.2.  Live Streaming Media

   Model Based Metrics can be implemented as a side effect of serving
   any non-throughput maximizing traffic, such as streaming media, by
   applying some additional controls to the traffic.  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 determined by all of the individual
   tests described above, for a specific TDS.

   If the serving RTT is less than the target_RTT, this constraint is
   most easily implemented by clamping the transport window size to
   test_window=target_data_rate*serving_RTT/target_MTU.  This
   test_window size 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, assuming burst size derating equal to the serving_RTT
   divided by the target_RTT.

   Note that if the application tolerates fluctuations in its actual
   data rate (say by use of a playout buffer) 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.  Since the serving RTT is
   smaller than the target_RTT, the worst case bursts that might be
   generated under these conditions are smaller than called for by
   Section 7.4

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8.  Examples

   In this section we present TDS for a couple of performance

   Tentatively: 5 Mb/s*50 ms, 1 Mb/s*50ms, 250kbp*100mS

8.1.  Near serving HD streaming video

   Today the best quality HD video requires slightly less than 5 Mb/s
   [HDvideo].  Since it is desirable to serve such content locally, we
   assume that the content will be within 50 mS, which is enough to
   cover continental Europe or either US coast.

                         5 Mb/s over a 50 ms path

                | End to End Parameter | Value | units   |
                | target_rate          | 5     | Mb/s    |
                | target_RTT           | 50    | ms      |
                | traget_MTU           | 1500  | bytes   |
                | target_pipe_size     | 22    | packets |
                | target_run_length    | 1452  | packets |

                                  Table 1

   This example uses the most conservative TCP model and no derating.

8.2.  Far serving SD streaming video

   Standard Quality video typically fits in 1 Mb/s [SDvideo].  This can
   be reasonably delivered via longer paths with larger.  We assume

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                         5 Mb/s over a 50 ms path

                | End to End Parameter | Value | units   |
                | target_rate          | 1     | Mb/s    |
                | target_RTT           | 100   | ms      |
                | traget_MTU           | 1500  | bytes   |
                | target_pipe_size     | 9     | packets |
                | target_run_length    | 243   | packets |

                                  Table 2

   This example uses the most conservative TCP model and no derating.

8.3.  Bulk delivery of remote scientific data

   This example corresponds to 100 Mb/s bulk scientific data over a
   moderately long RTT.  Note that the target_run_length is infeasible
   for most networks.

                        100 Mb/s over a 200 ms path

               | End to End Parameter | Value   | units   |
               | target_rate          | 100     | Mb/s    |
               | target_RTT           | 200     | ms      |
               | traget_MTU           | 1500    | bytes   |
               | target_pipe_size     | 1741    | packets |
               | target_run_length    | 9093243 | packets |

                                  Table 3

9.  Validation

   This document permits alternate models and parameter derating, as
   described in Section 5.2 and Section 5.3.  In exchange for this
   latitude in the modelling process it requires the ability to
   demonstrate authentic applications and protocol implementations
   meeting the target end-to-end performance goals over infrastructure
   that infinitessimally passes the TDS.

   The validation process relies on constructing a test network such
   that all of the individual load tests pass only infinitessimally, and

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   proving that an authentic application running over a real TCP
   implementation (or other protocol as appropriate) can be expected to
   meet the end-to-end target parameters on such a network.

   For example using our example in our HD streaming video TDS described
   in Section 8.1, the bottleneck data rate should be 5 Mb/s, the per
   packet random background loss probability should be 1/1453, for a run
   length of 1452 packets, the bottleneck queue should be 22 packets and
   the front path should have just enough buffering to withstand 22
   packet line 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 23 packet slowstart 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 ral application using a stock TCP implementation in the vendor's
   default configuration to attain 5 Mb/s over an 50 mS path.

   @@@@ Need to better specify the workload: both short and long flows.

   The difficult part of this process is arranging for each subpath to
   infinitesimally pass the individual tests.  We suggest two
   approaches: constraining resources in devices by configuring them not
   to use all available buffer space or data rate; and preloading
   subpaths with cross traffic.  Note that is it important that a single
   environment is constructed that infinitessimally passes all tests,
   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.

   If a TDS validated according to these procedures is used to inform
   public dialog, the validation experiment itself should also be public
   with sufficient precision for the experiment to be replicated by
   other researchers.  All components should either be open source of
   fully specified proprietary implementations that are available to the
   research community.

   TODO: paper proving the validation process.

10.  Acknowledgements

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

   Meredith Whittaker for improving the clarity of the communications.

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11.  Informative References

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

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

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

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

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

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

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

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

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

              Hamon, D., "Git Repository for Model Based Metrics",
              Sept 2013, <https://github.com/m-lab/MBM>.

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

   [BScope]   Broswerscope, "Browserscope Network tests", Sept 2012,

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

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

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

   [LMCUBIC]  Ledesma Goyzueta, R. and Y. Chen, "A Deterministic Loss
              Model Based Analysis of CUBIC, IEEE International
              Conference on Computing, Networking and Communications
              (ICNC), E-ISBN : 978-1-4673-5286-4", January 2013.

Appendix A.  Model Derivations

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

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

   These models provide estimates that 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 raw parameter.

   Although this document gives a lot of latitude for calculating
   target_run_length, people designing suites of tests need to consider
   the effect of their choices on the ongoing conversation and tussle
   about the relevance of "TCP friendliness" as an appropriate model for
   capacity allocation.  Choosing a target_run_length that is
   substantially smaller than the reference target_run_length specified
   in Section 5.2 is equivalent to saying that it is appropriate for the
   transport research community to abandon "TCP friendliness" as a
   fairness model and to develop more aggressive Internet transport

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   protocols, and for applications to continue (or even increase) the
   number of connections that they open concurrently.

A.1.  Aggregate Reno

   In Section 5.2 it is assumed that the target rate is the same as the
   link rate, and any excess window causes a standing queue at the
   bottleneck.  This might be representative of a non-shared access
   link.  An alternative situation would be a heavily aggregated subpath
   where individual flows do not significantly contribute to the
   queueing delay, and losses are determined monitoring the average data
   rate, for example by the use of a virtual queue as in [AFD].  In such
   a scheme the RTT is constant and TCP's AIMD congestion control causes
   the data rate to fluctuate in a sawtooth.  If the traffic is being
   controlled in a manner that is consistent with the metrics here, goal
   would be to make the actual average rate equal to the

   We can derive a model for Reno TCP and delayed ACK under the above
   set of assumptions: for some value of Wmin, the window will sweep
   from Wmin to 2*Wmin in 2*Wmin RTT.  Between losses each sawtooth
   delivers (1/2)(Wmin+2*Wmin)(2Wmin) packets in 2*Wmin round trip
   times.  However, 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.

   (@@@@ something is wrong above) Substituting these together we get:

   target_run_length = (8/3)(target_pipe_size^2)

   Note that this is always 88% of the reference run length.


   CUBIC has three operating regions.  The model for the expected value
   of window size derived in [LMCUBIC] assumes operation in the
   "concave" region only, which is a non-TCP friendly region for long-
   lived flows.  The authors make the following assumptions: packet loss
   probability, p, is independent and periodic, losses occur one at a
   time, and they are true losses due to tail drop or corruption.  This
   definition of p aligns very well with our definition of
   target_run_length and the requirement for progressive loss (AQM).

   Although CUBIC window increase depends on continuous time, the
   authors transform the time to reach the maximum Window size in terms
   of RTT and a parameter for the multiplicative rate decrease on
   observing loss, beta (whose default value is 0.2 in CUBIC).  The

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   expected value of Window size, E[W], is also dependent on C, a
   parameter of CUBIC that determines its window-growth aggressiveness
   (values from 0.01 to 4).

   E[W] = ( C*(RTT/p)^3 * ((4-beta)/beta) )^-4

   and, further assuming Poisson arrival, the mean throughput, x, is

   x = E[W]/RTT

   We note that under these conditions (deterministic single losses),
   the value of E[W] is always greater than 0.8 of the maximum window
   size ~= reference_run_length. (as far as I can tell)

   Commentary on the consequence of the choice.

Appendix B.  Version Control

   Formatted: Mon Oct 21 15:42:35 PDT 2013

Authors' Addresses

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

   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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