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Transport-Services                                            A. Petlund
Internet-Draft                                Simula Research Laboratory
Intended status: Informational                         February 14, 2014
Expires: August 18, 2014


                   Transport Services and low latency
              draft-petlund-latency-transport-services-00

Abstract

   This document categorises different classes of network latency,
   discusses possible metrics for determining the characteristics of
   latency-sensitive flows and addresses the use of transport services
   as a means for achieving transport latency reduction.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

   Internet-Drafts are working documents of the Internet Engineering
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   This Internet-Draft will expire on August 18, 2014.

Copyright Notice

   Copyright (c) 2014 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
     1.1.  Requirements Language . . . . . . . . . . . . . . . . . .   2
   2.  Time-dependent applications . . . . . . . . . . . . . . . . .   2
     2.1.  On the characteristics of latency-sensitive traffic . . .   3
   3.  Challenges in identifying traffic characteristics . . . . . .   4
   4.  Examples of choices of protocols and options that influence
       latency . . . . . . . . . . . . . . . . . . . . . . . . . . .   6
     4.1.  Protocols . . . . . . . . . . . . . . . . . . . . . . . .   6
     4.2.  Options and mechanisms  . . . . . . . . . . . . . . . . .   6
   5.  Discussion  . . . . . . . . . . . . . . . . . . . . . . . . .   7
   6.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .   7
   7.  Security Considerations . . . . . . . . . . . . . . . . . . .   7
   8.  References  . . . . . . . . . . . . . . . . . . . . . . . . .   7
     8.1.  Normative References  . . . . . . . . . . . . . . . . . .   7
     8.2.  Informative References  . . . . . . . . . . . . . . . . .   7
   Author's Address  . . . . . . . . . . . . . . . . . . . . . . . .   8

1.  Introduction

   Modern operating systems provide a myriad of different protocols and
   options to tweak the network performance.  Even for veterans within
   the field of transport protocols it is hard to stay fully up to date
   on all possibilities and combinations of options that may help reduce
   latency for a networked application.  Also, care needs to be taken so
   that the transport protocols and options chosen will not be
   disruptive to other services or to an application if it changes
   network behaviour.  For application developers in general to be able
   to select the best possible subset of mechanisms and protocols to
   support their time-dependent networked application, a measure of
   abstraction is required.  This document discusses different classes
   of network latency with examples of how to reduce the delay for each
   class.  It also makes suggestions for how an application can specify
   its intended behaviour to the transport services as a foundation for
   optimising the underlying services with regard to latency.

1.1.  Requirements Language

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
   document are to be interpreted as described in RFC 2119 [RFC2119].

2.  Time-dependent applications

   While completion time for bulk data transfer is deemed important, the
   focus of industry and research has lately shifted towards
   understanding the diversity of Internet traffic today.  Many flows



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   are in some way application limited, that is, their sending pattern
   is not exclusively determined by congestion control, but also by the
   timing of data received from the application.  Such applications are
   in many cases time-dependent, since the events driving the data
   production is triggered by real-life interaction or events [AP09].

   While downloading applications dominated the Internet for a long
   time, overprovisioning in backbone networks has allowed interactive
   applications to succeed.  Audio and video conferences that previously
   required leased lines are conducted over the Internet.  Multiplayer
   games that are played over the Internet are also prevalent.  Even Web
   applications generate increasingly interactive Internet traffic as
   more web pages are generated dynamically and contain interactively
   updated elements.  The flows of such time-dependent applications now
   represent a large proportion of the total number of flows in the
   Internet.  These flows have to tackle a large variety of access
   network technologies, all with different characteristics.  Depending
   on the type of application, "low latency" may carry several meanings
   from a transport viewpoint.  The next section elaborates on different
   classes of latency.

2.1.  On the characteristics of latency-sensitive traffic

   The scenarios described in this section are derived from a set of
   known latency-creating factors from which networked applications
   suffer.  These categories are not exclusive, an application may
   suffer from more than one of them.  They are, however,
   distinguishable at the transport layer and may require different
   avoidance techniques.  The main categories of application latency
   that have been identified so far are:

   (1)  Real-time interaction for applications with a lasting duration:

        (a)  Per-packet latency: applications that have signalling-like
             communication driven by actions or events.  Each small
             message is equally important and congestion-control is not
             applied as there is never a queue building on the sender
             side.  Examples include sensors triggered by events or
             online gaming traffic.

        (b)  Per-burst latency: interactive elements are sent in bursts
             over a persistent connection.  Collapsing the congestion
             window (cwnd) between bursts will reduce the per-burst
             completion time and increase the delivery latency for a
             burst while keeping the cwnd open may cause overestimation
             of the available bandwidth.  Examples include video
             streaming over persistent connections and financial




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             applications synchronised by trading synchronisation
             barriers.

        (c)  Per-burst latency with multiple reconnects: is the above
             scenario, but where the streams makes new connections with
             regular intervals.  This behaviour aggravates the bursty
             scenario by adding connection initialisation latency and
             start-up latency to each newly initialised connection.
             Examples include several methods of adaptive TCP-based
             video streaming and Web-based Content Management Systems.

   (2)  Start-up latency: the time it takes for a connection to find its
        correct send-rate.  The faster the correct bandwidth can be
        determined and the flow assume the correct send rate, the lower
        the latency.  Examples include non-adaptive high-quality video-
        on-demand delivery and Cloud-based office applications.

   (3)  Flow completion time: when a browser opens a range of different
        connections when loading a specific webpage, the latency
        experienced by the user is determined by the completion time of
        the subflows.  Very often, such flows are very small, often not
        more then one packet, thus motivating measures like increasing
        the initial window.  Examples include heavily styled web pages,
        messaging systems and news tickers, but this problem affects
        also interactive web browsing.

   There is also the latency induced by extra RTTs needed to set up a
   connection, for instance to initiate a security protocol or to
   negotiate options.

   There are flows that fluctuate between the behaviours described
   above.  In such cases, care must be taken to not blindly apply
   mechanisms that will reduce the latency for one of the cases, but
   increase latency for others.  In addition, some particular
   applications suffer more when there is a large variation in latency
   (jitter) than from a somewhat higher mean latency.  This includes
   applications with real-time interaction, such as on-line games.  In
   general, latency is characterised by a number of features including
   its higher order moments and distribution.  The most important set of
   features varies between different latency-sensitive applications.
   There is a need to consider all of these traffic behaviours to
   properly address the topic of latency for transport services.

3.  Challenges in identifying traffic characteristics

   It can be hard to reliably identify the flow characteristics from the
   viewpoint of the transport layer.  At the time when a flow starts,
   the transport can make no assumptions about what its traffic patterns



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   can be [MF14] (AP: unless guessing from 5-tuple).  In order for the
   transport to make qualified decision on which protocols and options
   to apply for a given flow to reduce latency, more information must be
   provided to the transport:

   (1)  The transport service tries to identify the traffic
        characteristics of the flow.  This is a possibility for long-
        lasting flows that can be instrumented by the transport service.
        Such monitoring is, however, challenging since the experienced
        behaviour is dependent network characteristics like RTT and
        bottleneck capacity.

   (2)  The application informs the transport layer about its intended
        behaviour.  For short flows and reducing startup latency, this
        is the only option as no information are yet available about the
        flow's characteristics.

   For the transport layer to be able to identify relevant traffic
   characteristics, it is useful to review the metrics that are
   available to the transport layer.  Examples of metrics are:

   (1)  Packets in flight: "FLIGHT SIZE", according to the TCP
        congestion control specification [RFC5681], is the number of
        packets that have been transmitted, but not yet acknowledged.
        For efficient retransmissions, in reliable protocols, this is an
        important metric as a flow with less than 4 packets in flight
        cannot trigger a fast retransmission.  This leads to high
        recovery delays for many application-limited flows.  Packets in
        flight is, however, not a static indicator of traffic behaviour
        as it is dependent on the RTT of the connection.

   (2)  Packet intertransmission time: the rate by which the application
        delivers data to the transport layer over time gives an
        indication of the traffic characteristics of the application.  A
        constantly application-limited flow will not need a tuned
        congestion control, but will be sensitive to recovery delays as
        discussed in the bullet about "Packets in flight".

   (3)  Payload size: the payload size is application-specific and does
        not relate to any network phenomena.  Time-dependent, event
        driven traffic often send packets with payload sizes less than
        the maximum transmission unit (MTU)[AP09].  For greedy streams,
        the packets will all fill an MTU.

   (4)  Stream duration: Analysis of Internet traffic shows that a
        majority of the flows are very short in duration[MF14].  When a
        browser loads a webpage, dozens of connections are usually
        opened, each transferring a small part of the webpage content.



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        Streams carrying event-based or interactive communication, on
        the contrary, are usually persistent and longer-lasting.  Thus,
        measuring whether a stream terminates within a short time
        interval (less than one second) can provide information that may
        help predict the continued behaviour of the flow.

   (5)  Burstiness: if a flow has a traffic pattern with bursts of
        activity followed by periods of inactivity, getting up to speed
        quickly in the active periods will be important to the
        application.  The size of each burst is also relevant to the
        choices made by the transport layer.

   (6)  Send queue backlog: a flow that is network limited will build a
        send queue while waiting for data to be transferred.  By
        monitoring the send queue size, the transport layer can get
        relevant information about the flow.

   The combination of information provided by the above listed metrics
   may help the transport services to make qualified decisions on the
   flow characteristics and choose the right services for reducing
   latency.

4.  Examples of choices of protocols and options that influence latency

   List examples of protocols and options that influence latency.

4.1.  Protocols

   Examples of protocols with a short discussion on latency
   implications.

   o  TCP:

   o  UDP:

   o  SCTP:

   o  Other: DCCP ++?

4.2.  Options and mechanisms

   Examples of protocol options and mechanisms with a short discussion
   on their influence on latency.

   o  Nagle's algorithm (delays small packets)

   o  Delayed ACKs (delays feedback)




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   o  Limited transmit

   o  Early retransmit

   o  RTO restart

   o  New CWV

   o  TFO

   o  PRSCTP

5.  Discussion

   Whether properties should be submitted by the applications in
   addition to services is an item for discussion and should be treated
   in future revisions of the document.

6.  IANA Considerations

   This memo includes no request to IANA.

7.  Security Considerations

   This document does not raise any new security issues.

8.  References

8.1.  Normative References

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

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

8.2.  Informative References

   [AP09]     Petlund, A., "Improving Latency for interactive, thin-
              stream applications over reliable transport", Thesis
              Unipub, Kristian Ottosens hus, Pb.  33 Blindern, 0313
              Oslo, December 2009.

   [MF14]     Fuchs, M., "Time-Dependent Thin Transport Layer Streams:
              Characterization, Empirical Observation and Protocol
              Support", Master Thesis University of Kaiserslautern,
              Germany, January 2014.




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Author's Address

   Andreas Petlund
   Simula Research Laboratory
   Rolfsbukta 4 B,
   Fornebu  1364
   Norway

   Phone: +47 99 27 36 22
   Email: apetlund@simula.no









































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