article

End-to-end estimation of the available bandwidth variation range

Authors:

Constantinos DovrolisAuthors Info & Claims

ACM SIGMETRICS Performance Evaluation Review, Volume 33, Issue 1

Pages 265 - 276

https://doi.org/10.1145/1071690.1064242

Published: 06 June 2005 Publication History

Abstract

The available bandwidth (avail-bw) of a network path is an important performance metric and its end-to-end estimation has recently received significant attention. Previous work focused on the estimation of the average avail-bw, ignoring the significant variability of this metric in different time scales. In this paper, we show how to estimate a given percentile of the avail-bw distribution at a user-specified time scale. If two estimated percentiles cover the bulk of the distribution (say 10% to 90%), the user can obtain a practical estimate for the avail-bw variation range. We present two estimation techniques. The first is iterative and non-parametric, meaning that it is more appropriate for very short time scales (typically less than 100ms), or in bottlenecks with limited flow multiplexing (where the avail-bw distribution may be non-Gaussian). The second technique is parametric, because it assumes that the avail-bw follows the Gaussian distribution, and it can produce an estimate faster because it is not iterative. The two techniques have been implemented in a measurement tool called Pathvar. Pathvar can track the avail-bw variation range within 10-20%, even under non-stationary conditions. Finally, we identify four factors that play a crucial role in the variation range of the avail-bw: traffic load, number of competing flows, rate of competing flows, and of course the measurement time scale.

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Sun YYin XJiang JSekar VLin FWang NLiu TSinopoli BBarcellos MCrowcroft JVahdat AKatti S(2016)CS2PProceedings of the 2016 ACM SIGCOMM Conference10.1145/2934872.2934898(272-285)Online publication date: 22-Aug-2016
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Index Terms

End-to-end estimation of the available bandwidth variation range
1. Networks
  1. Network services
    1. Network monitoring

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Published In

cover image ACM SIGMETRICS Performance Evaluation Review

ACM SIGMETRICS Performance Evaluation Review Volume 33, Issue 1

Performance evaluation review

June 2005

417 pages

ISSN:0163-5999

DOI:10.1145/1071690

Issue’s Table of Contents

SIGMETRICS '05: Proceedings of the 2005 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
June 2005
428 pages
ISBN:1595930221
DOI:10.1145/1064212
General Chairs:
Derek Eager
University of Saskatchewan, Canada
,
Carey Williamson
University of Calgary, Canada
,
Program Chairs:
Sem Borst
Bell Labs, USA & CWI, The Netherlands
,
John C. S. Lui
Chinese University of Hong Kong, China

Copyright © 2005 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 06 June 2005

Published in SIGMETRICS Volume 33, Issue 1

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Kim SKim C(2019)XMASIEEE Transactions on Multimedia10.1109/TMM.2018.285662621:2(442-456)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1109/TMM.2018.2856626
Rahman MUddin MHasan TRahman MKaykobad M(2018)Using Adaptive Heartbeat Rate on Long-Lived TCP ConnectionsIEEE/ACM Transactions on Networking10.1109/TNET.2017.277427526:1(203-216)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1109/TNET.2017.2774275
Sun YYin XJiang JSekar VLin FWang NLiu TSinopoli BBarcellos MCrowcroft JVahdat AKatti S(2016)CS2PProceedings of the 2016 ACM SIGCOMM Conference10.1145/2934872.2934898(272-285)Online publication date: 22-Aug-2016
https://dl.acm.org/doi/10.1145/2934872.2934898
Rahman MUddin YRahman MKaykobad M(2016)Using adaptive heartbeat rate on long-lived TCP connections2016 International Conference on Networking Systems and Security (NSysS)10.1109/NSysS.2016.7400700(1-9)Online publication date: Jan-2016
https://doi.org/10.1109/NSysS.2016.7400700
Rahman SPrima ARazzaque M(2016)Optimal allocation of 3G budget for smartphones running heterogeneous applications2016 International Conference on Networking Systems and Security (NSysS)10.1109/NSysS.2016.7400698(1-6)Online publication date: Jan-2016
https://doi.org/10.1109/NSysS.2016.7400698
Zheng ZDi LWang SXia MHu KZhang R(2016)Bandwidth Forecasting for Power Communication Using Adaptive Extreme Learning MachineCloud Computing and Security10.1007/978-3-319-48674-1_8(83-91)Online publication date: 1-Nov-2016
https://doi.org/10.1007/978-3-319-48674-1_8
LaCurts KDeng SGoyal ABalakrishnan HPapagiannaki KGummadi KPartridge C(2013)ChoreoProceedings of the 2013 conference on Internet measurement conference10.1145/2504730.2504744(191-204)Online publication date: 23-Oct-2013
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Smilkov DZhao HDettori PNogima JSchaffa FWesterink PWu C(2010)Non-intrusive Adaptive Multi-media Routing in Peer-to-Peer Multi-party Video ConferencingProceedings of the 2010 IEEE International Symposium on Multimedia10.1109/ISM.2010.24(105-112)Online publication date: 13-Dec-2010
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Kim SKim C(2019)XMAS: An Efficient Mobile Adaptive Streaming Scheme Based on Traffic ShapingIEEE Transactions on Multimedia10.1109/TMM.2018.285662621:2(442-456)Online publication date: Feb-2019
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