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On the predictability of large transfer TCP throughput

Authors:

Constantine Dovrolis,

Mostafa AmmarAuthors Info & Claims

SIGCOMM '05: Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications

Pages 145 - 156

https://doi.org/10.1145/1080091.1080110

Published: 22 August 2005 Publication History

Abstract

Predicting the throughput of large TCP transfers is important for a broad class of applications. This paper focuses on the design, empirical evaluation, and analysis of TCP throughput predictors. We first classify TCP throughput prediction techniques into two categories: Formula-Based (FB) and History-Based (HB). Within each class, we develop representative prediction algorithms, which we then evaluate empirically over the RON testbed. FB prediction relies on mathematical models that express the TCP throughput as a function of the characteristics of the underlying network path. It does not rely on previous TCP transfers in the given path, and it can be performed with non-intrusive network measurements. We show, however, that the FB method is accurate only if the TCP transfer is window-limited to the point that it does not saturate the underlying path, and explain the main causes of the prediction errors. HB techniques predict the throughput of TCP flows from a time series of previous TCP throughput measurements on the same path, when such a history is available. We show that even simple HB predictors, such as Moving Average and Holt-Winters, using a history of few and sporadic samples, can be quite accurate. On the negative side, HB predictors are highly path-dependent. We explain the cause of such path dependencies based on two key factors: the load on the path and the degree of statistical multiplexing.

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https://doi.org/10.1016/j.comnet.2024.110189
Aung HOhsaki H(2023)FL-PERF: Predicting TCP Throughput with Federated LearningGLOBECOM 2023 - 2023 IEEE Global Communications Conference10.1109/GLOBECOM54140.2023.10437294(4332-4337)Online publication date: 4-Dec-2023
https://doi.org/10.1109/GLOBECOM54140.2023.10437294
Wang BXu MRen FWu J(2022)Improving Robustness of DASH Against Unpredictable Network VariationsIEEE Transactions on Multimedia10.1109/TMM.2021.305008624(323-337)Online publication date: 2022
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Index Terms

On the predictability of large transfer TCP throughput
1. Networks
  1. Network protocols
    1. Transport protocols
  2. Network types
    1. Public Internet

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Published In

cover image ACM Conferences

SIGCOMM '05: Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications

August 2005

350 pages

ISBN:1595930094

DOI:10.1145/1080091

General Chair:
Roch Guerin
University of Pennsylvania
,
Program Chairs:
Ramesh Govindan
University of Southern California
,
Greg Minshall
Unaffiliated

ACM SIGCOMM Computer Communication Review Volume 35, Issue 4
Proceedings of the 2005 conference on Applications, technologies, architectures, and protocols for computer communications
October 2005
324 pages
ISSN:0146-4833
DOI:10.1145/1090191
Issue’s Table of Contents

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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Published: 22 August 2005

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SIGCOMM05

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SIGCOMM05: ACM SIGCOMM 2005 Conference

August 22 - 26, 2005

Pennsylvania, Philadelphia, USA

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Overall Acceptance Rate 462 of 3,389 submissions, 14%

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Pi YJamin SWei Y(2024)Measuring congestion-induced performance imbalance in Internet load balancing at scaleComputer Networks10.1016/j.comnet.2024.110189240(110189)Online publication date: Feb-2024
https://doi.org/10.1016/j.comnet.2024.110189
Aung HOhsaki H(2023)FL-PERF: Predicting TCP Throughput with Federated LearningGLOBECOM 2023 - 2023 IEEE Global Communications Conference10.1109/GLOBECOM54140.2023.10437294(4332-4337)Online publication date: 4-Dec-2023
https://doi.org/10.1109/GLOBECOM54140.2023.10437294
Wang BXu MRen FWu J(2022)Improving Robustness of DASH Against Unpredictable Network VariationsIEEE Transactions on Multimedia10.1109/TMM.2021.305008624(323-337)Online publication date: 2022
https://doi.org/10.1109/TMM.2021.3050086
Nam YGao JBothra CGhabashneh ERao SRibeiro BZhan JZhang H(2021)Xatu: Richer Neural Network Based Prediction for Video StreamingProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/34910565:3(1-26)Online publication date: 15-Dec-2021
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Schmid JHöss ASchuller B(2021)A Survey on Client Throughput Prediction Algorithms in Wired and Wireless NetworksACM Computing Surveys10.1145/347720454:9(1-33)Online publication date: 8-Oct-2021
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Phanekham DNair SRao NTruty M(2021)Predicting Throughput of Cloud Network Infrastructure Using Neural NetworksIEEE INFOCOM 2021 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)10.1109/INFOCOMWKSHPS51825.2021.9484520(1-6)Online publication date: 10-May-2021
https://doi.org/10.1109/INFOCOMWKSHPS51825.2021.9484520
Tegze DKovács FHosszú G(2021)Special Purpose Network Simulator for Transport Protocol AnalysisLISS 202010.1007/978-981-33-4359-7_32(447-460)Online publication date: 11-Apr-2021
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Kang YLim Y(2020)Bit Rate Adaptation Using Linear Quadratic Optimization for Mobile Video StreamingApplied Sciences10.3390/app1101009911:1(99)Online publication date: 24-Dec-2020
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Raca DZahran ASreenan CSinha RHalepovic EJana RGopalakrishnan V(2020)On Leveraging Machine and Deep Learning for Throughput Prediction in Cellular Networks: Design, Performance, and ChallengesIEEE Communications Magazine10.1109/MCOM.001.190039458:3(11-17)Online publication date: Mar-2020
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Wang XLi HWu Q(2019)Optimizing Adaptive Coding and Modulation for Satellite Network with ML-based CSI Prediction2019 IEEE Wireless Communications and Networking Conference (WCNC)10.1109/WCNC.2019.8885616(1-6)Online publication date: Apr-2019
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