research-article

Leveraging Zipf's law for traffic offloading

Authors:

Xin HuangAuthors Info & Claims

ACM SIGCOMM Computer Communication Review, Volume 42, Issue 1

Pages 16 - 22

https://doi.org/10.1145/2096149.2096152

Published: 16 January 2012 Publication History

Abstract

Internet traffic has Zipf-like properties at multiple aggregation levels. These properties suggest the possibility of offloading most of the traffic from a complex controller (e.g., a software router) to a simple forwarder (e.g., a commodity switch), by letting the forwarder handle a very limited set of flows; the heavy hitters. As the volume of traffic from a set of flows is highly dynamic, maintaining a reliable set of heavy hitters over time is challenging. This is especially true when we face a volume limit in the non-offloaded traffic in combination with a constraint in the size of the heavy hitter set or its rate of change. We propose a set selection strategy that takes advantage of the properties of heavy hitters at different time scales. Based on real Internet traffic traces, we show that our strategy is able to offload most of the traffic while limiting the rate of change of the heavy hitter set, suggesting the feasibility of alternative router designs.

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Index Terms

Leveraging Zipf's law for traffic offloading
1. Networks
  1. Network components
    1. Intermediate nodes
      1. Routers

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Published In

cover image ACM SIGCOMM Computer Communication Review

ACM SIGCOMM Computer Communication Review Volume 42, Issue 1

January 2012

88 pages

ISSN:0146-4833

DOI:10.1145/2096149

Issue’s Table of Contents

Copyright © 2012 Authors.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 January 2012

Published in SIGCOMM-CCR Volume 42, Issue 1

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https://doi.org/10.3390/electronics13061022
Liao ZQian SZheng ZZhang JCao JXue GLi M(2024)DBTable: Leveraging Discriminative Bitsets for High-Performance Packet ClassificationIEEE/ACM Transactions on Networking10.1109/TNET.2024.345278032:6(5232-5246)Online publication date: Dec-2024
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Sánchez-Macián AMartínez JReviriego PLiu SLombardi F(2024)On the Privacy of the Count-Min Sketch: Extracting the Top-K ElementsIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2024.338332112:4(1056-1065)Online publication date: Oct-2024
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Dallot JFesharaki APacut MSchmid S(2024)Dependency-Aware Online CachingIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621422(871-880)Online publication date: 20-May-2024
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Xiong BLiu YLiu RZhao JHe SZhao BYang KLi K(2024)ActiveGuardian: An accurate and efficient algorithm for identifying active elephant flows in network trafficJournal of Network and Computer Applications10.1016/j.jnca.2024.103853224(103853)Online publication date: Apr-2024
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Li XSun HHuang Y(2024)Efficient Flow Table Caching Architecture and Replacement Policy for SDN SwitchesJournal of Network and Systems Management10.1007/s10922-024-09824-w32:3Online publication date: 18-Jun-2024
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Huang HYu JDu YLiu JDai HSun Y(2023)Memory-Efficient and Flexible Detection of Heavy Hitters in High-Speed NetworksProceedings of the ACM on Management of Data10.1145/36173341:3(1-24)Online publication date: 13-Nov-2023
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Gozlan IAvin CEinziger GScalosub G(2023)Go-to-Controller is Better: Efficient and Optimal LPM Caching with SplicingProceedings of the ACM on Measurement and Analysis of Computing Systems10.1145/35794417:1(1-33)Online publication date: 2-Mar-2023
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Tang LHuang QLee P(2023)MVPipe: Enabling Lightweight Updates and Fast Convergence in Hierarchical Heavy Hitter DetectionIEEE/ACM Transactions on Networking10.1109/TNET.2023.327330731:6(3207-3221)Online publication date: Dec-2023
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