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Network Working Group                                             P. Kim
Internet-Draft                              Korea Polytechnic University
Intended status: Experimental
Expires: May 3, 2020
                                                       November 4, 2019


     Grasping Network Situation for Improving End-to-End Throughput
             draft-pskim-grasping-network-situation-00.txt

Abstract

   In this draft, a mechanism to grasp the network situation is proposed
   for improving end-to-end path throughput. The proposed mechanism is
   based on the active packet-train probing based estimation. The
   proposed mechanism defines three cases of the difference between the
   average output gap and the input gap, and then reflects fully them.
   Since three cases are handled respectively by appropriate
   corresponding manners, the proposed mechanism can be expected to
   reduce the detection error for the turning point. Therefore, through
   the proposed mechanism, the available bandwidth can be estimated more
   reliably.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
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   This Internet-Draft will expire on May 3, 2020.

Copyright Notice

   Copyright (c) 2019 IETF Trust and the persons identified as the
   document authors.  All rights reserved.

   This document is subject to BCP 78 and the IETF Trust's Legal
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   to this document.  Code Components extracted from this document must
   include Simplified BSD License text as described in Section 4.e of
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   described in the Simplified BSD License.

Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . . 2
   2.  A Grasping Network Situation via Bandwidth Estimation . . . . 3
   2.1 Existing Active Packet-train Probing Based Estimation . . . . 3
   2.2 An Alternative Active Packet-train Probing Based Estimation . 4
   3.  IANA Considerations . . . . . . . . . . . . . . . . . . . . . 6
   4.  References  . . . . . . . . . . . . . . . . . . . . . . . . . 6
   Author's Address  . . . . . . . . . . . . . . . . . . . . . . . . 7

1.  Introduction

   Traffic jams on narrow roads can be one of the main causes of traffic
   congestion, which also applies to communication networks. If there
   are more data traffic than the available network bandwidth,
   communication latency appears. This can adversely affect 5G-based
   Internet services such as self-driving cars, autonomous robots, etc.

   Communication latency is the term used to indicate any kind of delay
   that happens in data communication over a network. In particular,
   high latency creates bottlenecks in any network communication. It
   prevents the data from taking full advantage of the network pipe and
   effectively decreases the communication bandwidth. The impact of
   latency on network bandwidth can be temporary or persistent based on
   the source of the delays.

   Recently, in order to reduce the communication latency, grasping the
   network situation and adjusting the data transmission amount have
   been researched as shown in BBR(Congestion-based congestion control)
   [1] and ExLL(An ultra low-latency congestion protocol for mobile
   cellular network)[2]. BBR has been designed to prevent bottlenecks
   before they happen. For a given network connection, BBR uses recent
   measurements of the network's transmission rate and round-trip time
   to build an explicit model that includes both the maximum recent
   bandwidth available to that connection, and its minimum recent
   round-trip delay. ExLL has been designed to elaborate the allowed
   network bandwidth for an efficient low-latency transmission protocol.
   If data is sent only as much as the network bandwidth allowed by the
   mobile communication terminal, the data will not be unnecessarily
   accumulated. To do this, the pattern of packets received by the
   mobile communication terminal is observed.

   As shown in above observations, understanding the dynamic properties
   of the end-to-end Internet performance metrics such as available
   network bandwidth is beneficial for the proper resource management in

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   emerging wireless Internet services that required low-latency data
   transmission. Therefore, the area of end-to-end available bandwidth
   estimation has attracted considerable interest. As a result, several
   mechanisms for the available bandwidth estimation have been developed
   based on active measurements[3]. Among existing mechanisms for
   available bandwidth estimation, the active packet-train probing
   mechanism such as initial gap increasing and packet transmission rate
   was used successfully. The ultimate objective is to experimentally
   determine the input gap value at some point for which the average
   output gap is equal to the input gap. At this point, the probing
   packets are considered to interleave nicely with the competing
   traffic, and the average rate of the packet train equals the
   available bandwidth on the bottleneck link. This point is called the
   "turning point". At the turning point, the input gap value for which
   the average output gap is equal to the input gap is the right value
   to use for estimating the available bandwidth. However, there are
   some issues in the existing active packet-train probing mechanism.
   After performing a measurement, three cases can be defined according
   to the difference between the average output gap and the input gap.
   These three cases have respectively different relationship between
   the average rate of the probing packet train and the available
   bandwidth. However, the existing mechanism did not reflect fully
   these three cases in order to reduce the detection latency of the
   turning point. That is, two of three cases are handled in the same
   way, which can introduce the detection error for the turning point
   since these two cases handled in the same way are absolutely
   different. Thus, the available bandwidth can be estimated
   inaccurately although the measurement latency can be reduced.
   Therefore, to reduce the detection error of the turning point and
   enhance the accuracy of the available bandwidth estimation, a new
   mechanism is proposed based on the active packet-train probing
   mechanism. The proposed mechanism reflects fully three cases, while
   the existing mechanism reflected only two cases. Since three cases
   are handled respectively by appropriate corresponding manners, the
   proposed mechanism can be expected to reduce the detection error for
   the turning point. Therefore, the end-to-end available bandwidth can
   be estimated more reliably.

2.  A Grasping Network Situation via Bandwidth Estimation

2.1  Existing Active Packet-train Probing Based Estimation

   The active packet-train probing mechanism was proposed for the
   available bandwidth estimation and shown to be much faster than
   existing mechanisms with similar measurement accuracy but with
   shorter measurement latency. This mechanism is based on a single-hop
   gap model that captures the relationship between the competing
   traffic and the probing packet train. As a sequence of probing packet
   trains from the source travel through the network, packets belonging
   to the competing traffic may be inserted between them, thus

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   increasing the gap at the destination. As a result, the average
   output gap value at the destination may be a function of the
   competing traffic rate, making it possible to estimate the amount of
   competing traffic. That is, the average output gap can be used to
   determine the competing traffic bandwidth and hence the available
   bandwidth on the end-to-end path assuming that the bottleneck link
   bandwidth along the end-to-end path is known. At some point, the
   average output gap equals the input gap as gaps in a probing packet
   train increase. This point is called the "turning point". At the
   turning point, the input gap value for which the average output gap
   is equal to the input gap is the right value to use for estimating
   the available bandwidth.

   However, there are some issues in the existing active packet-train
   probing mechanism. After performing the measurement, three cases are
   defined according to the difference between the average output gap
   and the input gap. These three cases mean that the average output gap
   at the destination is (a) larger than, (b) equal to, (c) less than
   the input gap at the source. These three cases have respectively
   different relationship between the average rate of the probing packet
   train and the available bandwidth. However, the existing  mechanism
   did not reflect fully these three cases in order to reduce the
   measurement latency. That is, both (b) and (c) cases are handled in
   the same way, which can introduce the detection error for the turning
   point since (b) and (c) cases are absolutely different. Therefore,
   the available bandwidth can be estimated inaccurately although the
   measurement latency can be reduced.

   In this draft, a new mechanism for available bandwidth estimation
   mechanism is proposed to improve the estimation accuracy compared
   with the existing mechanism. As mentioned before, since (b) and (c)
   cases handled in the same way are absolutely different, they should
   be handled by respectively.

2.2  An Alternative Active Packet-train Probing Based Estimation

   As shown in [3], the end-to-end available bandwidth is defined
   as the difference between the bottleneck link bandwidth along an
   end-to-end path and the competing traffic. The bottleneck link
   bandwidth in the path determines the end-to-end bandwidth which is
   the maximum IP layer rate that the path can transfer from source to
   destination. In other words, the bandwidth of a path establishes an
   upper bound on the IP layer throughput that a user can expect to get
   from that path. There are diverse measurement mechanisms for the
   bottleneck link bandwidth. Therefore, the bottleneck link bandwidth
   can measured from one of existing mechanisms.

   There are several important probing parameters such as probing
   packet size, number of probing packet in packet train, and input gap
   to get correct measurement. Among them, input gap in a probing

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   packet train is the most important parameter to control for accurate
   available bandwidth estimation. The source sends a sequence of
   probing packet trains with adjusting input gap. The difference
   between the average output gap and the input gap is observed for
   each train. Then, the turning point is detected for estimating the
   available bandwidth.

   After performing a measurement, three cases are defined according to
   the difference between the average output gap and the input gap.
   Three cases are called 'Red', 'Yellow', 'Green' cases which have
   respectively different relationship between the average rate of the
   probing packet train and the available bandwidth as follows:

   - Red : The average rate of the packet train is more than the
     available bandwidth with the following condition:

     average output gap > input gap +  delta/2.

   - Yellow : The average rate of the packet train is similar to the
     available bandwidth with the following condition:

     |average output gap - input gap | < delta.

   - Green :  The average rate of the packet train is less than the
     available bandwidth with the following condition:

     average output gap < input gap - delta/2.

   Above three cases are handled respectively as follows:

   (1) Handling of 'Red' case

   The measurement is repeated with the increased input gap. After
   then, three cases observed once again. For each case, the
   measurement is repeated with adjusting input gap as follows:

   - Red : increased input gap
   - Yellow : same input gap as previous measurement
   - Green : decreased input gap

   In the existing mechanism, the measurement is repeated with the same
   input gap as previous measurement for 'Green' case.

   (2) Handling of 'Yellow' case

   The measurement is repeated with the same input gap as previous
   measurement. After then, three cases are observed once again and
   then handled respectively as follows:
   - Red : measurement with increased input gap
   - Yellow : measurement finished (turning point detected)

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   - Green : measurement with decreased input gap

   In the existing mechanism, the measurement is finished for 'Green'
   case.

   (3) Handling of 'Green' case

   The measurement is repeated with the decreased input gap. In the
   existing mechanism, the measurement is repeated with the same input
   gap in this case. After then, three cases are observed once again
   and then handled respectively as follows:

   - Red : measurement with increased input gap
   - Yellow : measurement finished (turning point detected)
   - Green : measurement with decreased input gap

   In the existing mechanism, the measurement is finished for 'Green'
   case.

   As shown in three cases, the proposed mechanism handles 'Yellow' and
   'Green' cases respectively while the existing mechanism handles them
   in the same way.

   When the turning point is detected, the measurement is finished and
   then the end-to-end available bandwidth can be estimated as follows.
   The end-to-end available bandwidth is obtained by subtracting the
   competing traffic bandwidth from the bottleneck link bandwidth.

   As mentioned before, the bottleneck link bandwidth can be measured
   from one of existing mechanisms. Then, the competing traffic
   bandwidth can be computed using the average output gap and the input
   gap at the turning point, and the bottleneck link bandwidth.

3.  IANA Considerations

   This document has no IANA actions.

4.  References

   [1] N. Cardwell et al., "BBR v2 : A Model-based Congestion Control",
       ICCRG, IETF 104, Mar 2019

   [2] S. Park et al., "ExLL: An Extremely Low-latency Congestion
       Control for Mobile Cellular Networks," Proc. of the 14th
       International Conference on Emerging Networking EXperiments and
       Technologies(CoNEXT'18) pp. 307-319, 2018.

   [3] N. Hu and P. Steenkiste, "Evaluation and characterization of
       available bandwidth probing techniques," IEEE JSAC, Vol. 21, No.
       6, pp. 879-894, 2003.

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Author's Address

   Pyungsoo Kim
   Department of Electronics Engineering,
   Korea Polytechnic University,
   2121 Jungwang-Dong, Shiheung City,
   Gyeonggi-Do  429-793
   KOREA

   Phone: +82 31 8041 0489
   EMail: pskim@kpu.ac.kr








































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