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Identifying performance bottlenecks in CDNs through TCP-level monitoring

Authors:

Michael J. Freedman,

Jennifer RexfordAuthors Info & Claims

W-MUST '11: Proceedings of the first ACM SIGCOMM workshop on Measurements up the stack

Pages 49 - 54

https://doi.org/10.1145/2018602.2018615

Published: 19 August 2011 Publication History

Abstract

Content distribution networks (CDNs) need to make decisions, such as server selection and routing, to improve performance for their clients. The performance may be limited by various factors such as packet loss in the network, a small receive buffer at the client, or constrained server CPU and disk resources. Conventional measurement techniques are not effective for distinguishing these performance problems: application-layer logs are too coarse-grained, while network-level traces are too expensive to collect all the time. We argue that passively monitoring the transport-level statistics in the server's network stack is a better approach.

This paper presents a tool for monitoring and analyzing TCP statistics, and an analysis of a CoralCDN node in PlanetLab for six weeks. Our analysis shows that more than 10% of connections are server-limited at least 40% of the time, and many connections are limited by the congestion window despite no packet loss. Still, we see that clients in 377 Autonomous Systems (ASes) experience persistent packet loss. By separating network congestion from other performance problems, our analysis provides a much more accurate view of the performance of the network paths than what is possible with server logs alone.

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Cited By

Geng RFang CGuo SKang DLyu BZhu SCheng P(2023)FlowPinpoint: Localizing Anomalies in Cloud-client Services for Cloud ProvidersIEEE Transactions on Cloud Computing10.1109/TCC.2023.3257162(1-15)Online publication date: 2023
https://doi.org/10.1109/TCC.2023.3257162
Hentschel RBley KLange F(2023)A Performance-Based Assessment Approach for Cloud Service Provider SelectionNew Sustainable Horizons in Artificial Intelligence and Digital Solutions10.1007/978-3-031-50040-4_19(250-264)Online publication date: 9-Nov-2023
https://dl.acm.org/doi/10.1007/978-3-031-50040-4_19
Meng QQian KCheng WRen F(2021)RBA: Adaptive TCP Receive Buffer Sizing2021 IEEE Symposium on Computers and Communications (ISCC)10.1109/ISCC53001.2021.9631487(1-7)Online publication date: 5-Sep-2021
https://doi.org/10.1109/ISCC53001.2021.9631487
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Index Terms

Identifying performance bottlenecks in CDNs through TCP-level monitoring
1. General and reference
  1. Cross-computing tools and techniques
    1. Measurement
    2. Metrics
2. Networks
  1. Network services
    1. Network monitoring

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Published In

cover image ACM Conferences

W-MUST '11: Proceedings of the first ACM SIGCOMM workshop on Measurements up the stack

August 2011

74 pages

ISBN:9781450308007

DOI:10.1145/2018602

Program Chairs:
Nina Taft
Technicolor, USA
,
David Wetherall
University of Washington, USA

Copyright © 2011 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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SIGCOMM: ACM Special Interest Group on Data Communication

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Association for Computing Machinery

New York, NY, United States

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Published: 19 August 2011

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SIGCOMM '11: ACM SIGCOMM 2011 Conference

August 19, 2011

Ontario, Toronto, Canada

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Cited By

Geng RFang CGuo SKang DLyu BZhu SCheng P(2023)FlowPinpoint: Localizing Anomalies in Cloud-client Services for Cloud ProvidersIEEE Transactions on Cloud Computing10.1109/TCC.2023.3257162(1-15)Online publication date: 2023
https://doi.org/10.1109/TCC.2023.3257162
Hentschel RBley KLange F(2023)A Performance-Based Assessment Approach for Cloud Service Provider SelectionNew Sustainable Horizons in Artificial Intelligence and Digital Solutions10.1007/978-3-031-50040-4_19(250-264)Online publication date: 9-Nov-2023
https://dl.acm.org/doi/10.1007/978-3-031-50040-4_19
Meng QQian KCheng WRen F(2021)RBA: Adaptive TCP Receive Buffer Sizing2021 IEEE Symposium on Computers and Communications (ISCC)10.1109/ISCC53001.2021.9631487(1-7)Online publication date: 5-Sep-2021
https://doi.org/10.1109/ISCC53001.2021.9631487
Bauer SHolzinger KJaeger BEmmerich PCarle G(2020)Online Monitoring of TCP Throughput LimitationsNOMS 2020 - 2020 IEEE/IFIP Network Operations and Management Symposium10.1109/NOMS47738.2020.9110324(1-9)Online publication date: Apr-2020
https://doi.org/10.1109/NOMS47738.2020.9110324
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Im YRahimzadeh PShouse BPark SJoe-Wong CLee KHa S(2019)I Sent ItProceedings of the Fourteenth EuroSys Conference 201910.1145/3302424.3303961(1-15)Online publication date: 25-Mar-2019
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Zhou JLi ZWu QSteenkiste PUhlig SLi JXie G(2019)TCP Stalls at the Server SideIEEE/ACM Transactions on Networking10.1109/TNET.2018.288628227:1(272-287)Online publication date: 1-Feb-2019
https://dl.acm.org/doi/10.1109/TNET.2018.2886282
Mohamed Mansour DMoustafa IKhalil AMahdi H(2019)An assessment model for identifying maintenance priorities strategy for bridgesAin Shams Engineering Journal10.1016/j.asej.2019.06.003Online publication date: Aug-2019
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Ahmed FShafiq MKhakpour ALiu A(2018)Optimizing Internet Transit Routing for Content Delivery NetworksIEEE/ACM Transactions on Networking10.1109/TNET.2017.276175226:1(76-89)Online publication date: 1-Feb-2018
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Xie XZhang XZhu SChoudhury TKo SCampbell AGanesan D(2017)Accelerating Mobile Web Loading Using Cellular Link InformationProceedings of the 15th Annual International Conference on Mobile Systems, Applications, and Services10.1145/3081333.3081367(427-439)Online publication date: 16-Jun-2017
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