short-paper

Towards accurate online traffic matrix estimation in software-defined networks

Authors:

Mehdi Malboubi,

Chen-Nee ChuahAuthors Info & Claims

SOSR '15: Proceedings of the 1st ACM SIGCOMM Symposium on Software Defined Networking Research

Article No.: 26, Pages 1 - 7

https://doi.org/10.1145/2774993.2775068

Published: 17 June 2015 Publication History

Abstract

Traffic matrix measurement provides essential information for network design, operation and management. In today's networks, it is challenging to get accurate and timely traffic matrix due to the hard resource constraints of network devices. Recently, Software-Defined Networking (SDN) technique enables customizable traffic measurement, which can provide flexible and fine-grain visibility into network traffic. However, the existing software-defined traffic measurement solutions often suffer from feasibility and scalability issues. In this paper, we seek accurate, feasible and scalable traffic matrix estimation approaches. We propose two strategies, called Maximum Load Rule First (MLRF) and Large Flow First (LFF), to design feasible traffic measurement rules that can be installed in TCAM entries of SDN switches. The statistics of the measurement rules are collected by the controller to estimate fine-grained traffic matrix. Both MLRF and LFF satisfy the flow aggregation constraints (determined by associated routing policies) and have low-complexity. Extensive simulation results on real network and traffic traces reveal that MLRF and LFF can achieve high accuracy of traffic matrix estimation and high probability of heavy hitter detection.

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Cited By

Ren JWang YLiu YWang XWang S(2024)Server-Assisted Traffic Measurement for Programmable Data Center NetworksIEEE Transactions on Network Science and Engineering10.1109/TNSE.2024.339729111:5(4729-4743)Online publication date: Sep-2024
https://doi.org/10.1109/TNSE.2024.3397291
Lakineni PKumar SModi SJoshi KMareeskannan VLande J(2023)Deepflow: A Software-Defined Measurement System for Deep Learning2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)10.1109/ICACITE57410.2023.10182469(1217-1221)Online publication date: 12-May-2023
https://doi.org/10.1109/ICACITE57410.2023.10182469
Miletic SPokrajac IPena-Pena KArce GMladenovic V(2022)A Multigraph-Defined Distribution Function in a Simulation Model of a Communication NetworkEntropy10.3390/e2409129424:9(1294)Online publication date: 14-Sep-2022
https://doi.org/10.3390/e24091294
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Towards accurate online traffic matrix estimation in software-defined networks

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cover image ACM Conferences

SOSR '15: Proceedings of the 1st ACM SIGCOMM Symposium on Software Defined Networking Research

June 2015

226 pages

ISBN:9781450334518

DOI:10.1145/2774993

Program Chairs:
Jennifer Rexford
Princeton
,
Amin Vahdat
Google

Copyright © 2015 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
ONS: Open Networking Summit

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 June 2015

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Short-paper

Funding Sources

973 Program
NSFC Fund
NSF
Program for Changjiang Scholars and Innovative Research Team (PCSIRT) in University

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SOSR 2015

Sponsor:

SIGCOMM
ONS

SOSR 2015: ACM SIGCOMM Symposium on SDN Research

June 17 - 18, 2015

California, Santa Clara

Acceptance Rates

SOSR '15 Paper Acceptance Rate 7 of 43 submissions, 16%;

Overall Acceptance Rate 7 of 43 submissions, 16%

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Cited By

Ren JWang YLiu YWang XWang S(2024)Server-Assisted Traffic Measurement for Programmable Data Center NetworksIEEE Transactions on Network Science and Engineering10.1109/TNSE.2024.339729111:5(4729-4743)Online publication date: Sep-2024
https://doi.org/10.1109/TNSE.2024.3397291
Lakineni PKumar SModi SJoshi KMareeskannan VLande J(2023)Deepflow: A Software-Defined Measurement System for Deep Learning2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE)10.1109/ICACITE57410.2023.10182469(1217-1221)Online publication date: 12-May-2023
https://doi.org/10.1109/ICACITE57410.2023.10182469
Miletic SPokrajac IPena-Pena KArce GMladenovic V(2022)A Multigraph-Defined Distribution Function in a Simulation Model of a Communication NetworkEntropy10.3390/e2409129424:9(1294)Online publication date: 14-Sep-2022
https://doi.org/10.3390/e24091294
Liu GGuo SXiao BYang Y(2022)SDN-Based Traffic Matrix Estimation in Data Center Networks through Large Size Flow IdentificationIEEE Transactions on Cloud Computing10.1109/TCC.2019.294482310:1(675-690)Online publication date: 1-Jan-2022
https://doi.org/10.1109/TCC.2019.2944823
Chen YWang P(2022)Concise Retrieval of Flow Statistics for Software-Defined NetworksIEEE Systems Journal10.1109/JSYST.2021.306530616:1(554-565)Online publication date: Mar-2022
https://doi.org/10.1109/JSYST.2021.3065306
Michel OBifulco RRétvári GSchmid S(2021)The Programmable Data PlaneACM Computing Surveys10.1145/344786854:4(1-36)Online publication date: 3-May-2021
https://dl.acm.org/doi/10.1145/3447868
Srivastava VPandey R(2021)Load balancing for software-defined network: a reviewInternational Journal of Computers and Applications10.1080/1206212X.2021.191983544:8(746-759)Online publication date: 27-Apr-2021
https://doi.org/10.1080/1206212X.2021.1919835
Alparslan OArakawa SMurata M(2020)SDN-Based Control of IoT Network by Brain-Inspired Bayesian Attractor Model and Network SlicingApplied Sciences10.3390/app1017577310:17(5773)Online publication date: 20-Aug-2020
https://doi.org/10.3390/app10175773
Jiang DWang WShi LSong H(2020)A Compressive Sensing-Based Approach to End-to-End Network Traffic ReconstructionIEEE Transactions on Network Science and Engineering10.1109/TNSE.2018.28775977:1(507-519)Online publication date: 1-Jan-2020
https://doi.org/10.1109/TNSE.2018.2877597
Queiroz WCapretz MDantas M(2020)A MapReduce Approach for Traffic Matrix Estimation in SDNIEEE Access10.1109/ACCESS.2020.30162498(149065-149076)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.3016249
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