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Distributed and collaborative traffic monitoring in software defined networks

Authors:

Xin LiAuthors Info & Claims

HotSDN '14: Proceedings of the third workshop on Hot topics in software defined networking

Pages 85 - 90

https://doi.org/10.1145/2620728.2620739

Published: 22 August 2014 Publication History

Abstract

Network traffic monitoring supports fundamental network management tasks. However, monitoring tasks introduce non-trivial overhead to network devices such as switches. We propose a Distributed and Collaborative Monitoring system, named DCM, with the following properties. First, DCM allows switches to collaboratively achieve flow monitoring tasks and balance measurement load. Second, DCM is able to perform per-flow monitoring, by which different groups of flows are monitored using different actions. Third, DCM is a memory-efficient solution for switch data plane and guarantees system scalability. DCM uses novel two-stage Bloom filters to represent monitoring rules using small memory space. It utilizes the centralized SDN control to install, update, and reconstruct the two-stage Bloom filters in the switch data plane. We study how DCM performs two representative monitoring tasks, namely flow size counting and packet sampling, and evaluate its performance. Experiments using real data center and ISP traffic data on real network topologies show that DCM achieves highest measurement accuracy among existing solutions given the same memory budget of switches.

References

[1]

The caida ucsd anonymized internet traces 2013 - 2014. mar. http://www.caida.org/data/passive/passive_2013_dataset.xml.

[2]

M. Al-Fares, A. Loukissas, and A. Vahdat. A scalable, commodity data center network architecture. In Proc. of ACM SIGCOMM, 2008.

Digital Library

[3]

T. Benson, A. Akella, and D. A. Maltz. Network traffic characteristics of data centers in the wild. In Proceedings of ACM IMC, 2010.

Digital Library

[4]

T. Benson, A. Anand, A. Akella, and M. Zhang. Microte: fine grained traffic engineering for data centers. In Proc. of ACM CoNEXT, 2011.

Digital Library

[5]

B. H. Bloom. Space/time trade-offs in hash coding with allowable errors. Communications of the ACM, 13(7):422--426, 1970.

Digital Library

[6]

J. Byers, J. Considine, M. Mitzenmacher, and S. Rost. Informed content delivery across adaptive overlay networks. In Proc. of ACM SIGCOMM, 2002.

Digital Library

[7]

S. R. Chowdhury, M. F. Bari, R. Ahmed, and R. Boutaba. Payless: A low cost netowrk monitoring framework for software defined networks. In Proc. of IEEE/IFIP NOMS, 2014.

[8]

B. Claise. Cisco systems netflow services export version 9, 2004.

[9]

G. Cormode and S. Muthukrishnan. An improved data stream summary: the count-min sketch and its applications. Journal of Algorithms, 55(1):58--75, 2005.

Digital Library

[10]

L. Fan, P. Cao, J. Almeida, and A. Z. Broder. Summary cache: a scalable wide-area web cache sharing protocol. IEEE/ACM Transactions on Networking, 8(3):281--293, 2000.

Digital Library

[11]

W. Feng, K. G. Shin, D. D. Kandlur, and D. Saha. The blue active queue management algorithms. IEEE/ACM Transactions on Networking, 10(4):513--528, 2002.

Digital Library

[12]

A. Goel and P. Gupta. Small subset queries and bloom filters using ternary associative memories, with applications. In Proc. of ACM SIGMETRICS, 2010.

Digital Library

[13]

Y. Kanizo, D. Hay, and I. Keslassy. Palette: Distributing tables in software-defined networks. In Proc. of IEEE INFOCOM, 2013.

[14]

D. Li, H. Cui, Y. Hu, Y. Xia, and X. Wang. Scalable data center multicast using multi-class bloom filter. In Proc. of IEEE ICNP, 2011.

Digital Library

[15]

T. Mishra and S. Sahni. Duo-dual tcam architecture for routing tables with incremental update. In Proc. of IEEE ISCC, 2010.

Digital Library

[16]

M. Moshref, M. Yu, and R. Govindan. Resource/accuracy tradeoffs in software-defined measurement. In Proc. of ACM HotSDN, 2013.

Digital Library

[17]

T. Pan, X. Guo, C. Zhang, J. Jiang, H. Wu, and B. Liu. Tracking millions of flows in high speed networks for application identification. In Proc. of IEEE INFOCOM, 2012.

[18]

P. Phaal and M. Lavine. sflow version 5, 2004.

[19]

A. Ramachandran, S. Seetharaman, N. Feamster, and V. Vazirani. Fast monitoring of traffic subpopulations. In Proc. of ACM IMC, 2008.

Digital Library

[20]

V. Sekar, A. Gupta, M. K. Reiter, and H. Zhang. Coordinated sampling sans origin-destination identifiers: algorithms and analysis. In Proc. of IEEE COMSNETS, 2010.

Digital Library

[21]

V. Sekar, M. K. Reiter, W. Willinger, H. Zhang, R. R. Kompella, and D. G. Andersen. csamp: A system for network-wide flow monitoring. In Proc. of USENIX NSDI, 2008.

Digital Library

[22]

V. Sekar, M. K. Reiter, and H. Zhang. Revisiting the case for a minimalist approach for network flow monitoring. In Proc. of ACM IMC, 2010.

Digital Library

[23]

S. Shen and A. Akella. Decor: a distributed coordinated resource monitoring system. In Proc. of IEEE IWQoS, 2012.

Digital Library

[24]

A. C. Snoeren, C. Partridge, L. A. Sanchez, C. E. Jones, F. Tchakountio, B. Schwartz, S. T. Kent, and W. T. Strayer. Single-packet ip traceback. IEEE/ACM Transactions on Networking, 10(6):721--734, 2002.

Digital Library

[25]

N. Spring, R. Mahajan, and D. Wetherall. Measuring isp topologies with rocketfuel. 2002.

[26]

Y. Xie, V. Sekar, D. A. Maltz, M. K. Reiter, and H. Zhang. Worm origin identification using random moonwalks. In Proc. of IEEE S&P, 2005.

Digital Library

[27]

M. Yu, A. Fabrikant, and J. Rexford. Buffalo: Bloom filter forwarding architecture for large organizations. In Proceedings of ACM CoNEXT, 2009.

Digital Library

[28]

M. Yu, L. Jose, and R. Miao. Software defined traffic measurement with opensketch. In Proc. of USENIX NSDI, 2013.

Digital Library

[29]

M. Yu, J. Rexford, M. J. Freedman, and J. Wang. Scalable flow based networking with difane. In Proc. of ACM SIGCOMM, 2010.

Digital Library

[30]

Y. Zhang. An adaptive flow counting method for anomaly detection in sdn. In Proc. of ACM CoNEXT, 2013.

Digital Library

Cited By

Zhao HPan SCai ZChen XJin LGao HWan SMa RGuan H(2024) faaShark : an End-to-End Network Traffic Analysis System atop Serverless Computing IEEE Transactions on Network Science and Engineering10.1109/TNSE.2023.3294406(1-12)Online publication date: 2024
https://doi.org/10.1109/TNSE.2023.3294406
Yao DMa QWang HChen MXu H(2024)Distributed Strategy for Collaborative Traffic Measurement in a Multi-Controller SDNIEEE Transactions on Network Science and Engineering10.1109/TNSE.2023.327112311:3(2450-2461)Online publication date: May-2024
https://doi.org/10.1109/TNSE.2023.3271123
Cai MLiu YKong LChen GLiu LQiu MMumtaz S(2024)Resource Critical Flow Monitoring in Software-Defined NetworksIEEE/ACM Transactions on Networking10.1109/TNET.2023.328669132:1(396-410)Online publication date: Feb-2024
https://doi.org/10.1109/TNET.2023.3286691
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Index Terms

Distributed and collaborative traffic monitoring in software defined networks
1. Networks
  1. Network services
    1. Network monitoring

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Published In

cover image ACM Conferences

HotSDN '14: Proceedings of the third workshop on Hot topics in software defined networking

August 2014

252 pages

ISBN:9781450329897

DOI:10.1145/2620728

Program Chairs:
Aditya Akella
University of Wisconsin-Madison, USA
,
Albert Greenberg
Microsoft, USA

Copyright © 2014 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

Publication History

Published: 22 August 2014

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SIGCOMM'14

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SIGCOMM

SIGCOMM'14: ACM SIGCOMM 2014 Conference

August 22, 2014

Illinois, Chicago, USA

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HotSDN '14 Paper Acceptance Rate 50 of 114 submissions, 44%;

Overall Acceptance Rate 88 of 198 submissions, 44%

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Cited By

Zhao HPan SCai ZChen XJin LGao HWan SMa RGuan H(2024) faaShark : an End-to-End Network Traffic Analysis System atop Serverless Computing IEEE Transactions on Network Science and Engineering10.1109/TNSE.2023.3294406(1-12)Online publication date: 2024
https://doi.org/10.1109/TNSE.2023.3294406
Yao DMa QWang HChen MXu H(2024)Distributed Strategy for Collaborative Traffic Measurement in a Multi-Controller SDNIEEE Transactions on Network Science and Engineering10.1109/TNSE.2023.327112311:3(2450-2461)Online publication date: May-2024
https://doi.org/10.1109/TNSE.2023.3271123
Cai MLiu YKong LChen GLiu LQiu MMumtaz S(2024)Resource Critical Flow Monitoring in Software-Defined NetworksIEEE/ACM Transactions on Networking10.1109/TNET.2023.328669132:1(396-410)Online publication date: Feb-2024
https://doi.org/10.1109/TNET.2023.3286691
Li FGuo KShen JWang X(2024)Effective Network-Wide Traffic Measurement: A Lightweight Distributed Sketch DeploymentIEEE INFOCOM 2024 - IEEE Conference on Computer Communications10.1109/INFOCOM52122.2024.10621384(181-190)Online publication date: 20-May-2024
https://doi.org/10.1109/INFOCOM52122.2024.10621384
Agarwal ISingh AAgarwal AMishra SSatapathy SCho SPrusty MMohanty S(2024)Enhancing Road Safety and Cybersecurity in Traffic Management Systems: Leveraging the Potential of Reinforcement LearningIEEE Access10.1109/ACCESS.2024.335027112(9963-9975)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3350271
Gu JSong CDai HShi LWu JLu L(2022)ACM: Accuracy-Aware Collaborative Monitoring for Software-Defined Network-Wide MeasurementSensors10.3390/s2220793222:20(7932)Online publication date: 18-Oct-2022
https://doi.org/10.3390/s22207932
Wang YWang XXu SHe CZhang YRen JYu S(2022)FlexMonJournal of Network and Computer Applications10.1016/j.jnca.2022.103344201:COnline publication date: 1-May-2022
https://dl.acm.org/doi/10.1016/j.jnca.2022.103344
Mohammadi RAkleylek SGhaffari AShirmarz A(2022)Taxonomy of traffic engineering mechanisms in software-defined networks: a surveyTelecommunication Systems10.1007/s11235-022-00947-681:3(475-502)Online publication date: 5-Sep-2022
https://doi.org/10.1007/s11235-022-00947-6
Sanya DOnésime D(2022)New roles for Yarrowia lipolytica in molecules synthesis and biocontrolApplied Microbiology and Biotechnology10.1007/s00253-022-12227-z106:22(7397-7416)Online publication date: 15-Oct-2022
https://doi.org/10.1007/s00253-022-12227-z
Lazaris APrasanna V(2021)An LSTM Framework for Software-Defined MeasurementIEEE Transactions on Network and Service Management10.1109/TNSM.2020.304015718:1(855-869)Online publication date: Mar-2021
https://doi.org/10.1109/TNSM.2020.3040157
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