research-article

Tango: Simplifying SDN Control with Automatic Switch Property Inference, Abstraction, and Optimization

Authors:

Aggelos Lazaris,

Andreas Voellmy,

Y. Richard Yang,

Minlan YuAuthors Info & Claims

CoNEXT '14: Proceedings of the 10th ACM International on Conference on emerging Networking Experiments and Technologies

Pages 199 - 212

https://doi.org/10.1145/2674005.2675011

Published: 02 December 2014 Publication History

Abstract

A major benefit of software-defined networking (SDN) over traditional networking is simpler and easier control of network devices. The diversity of SDN switch implementation properties, which include both diverse switch hardware capabilities and diverse control-plane software behaviors, however, can make it difficult to understand and/or to control the switches in an SDN network. In this paper, we present Tango, a novel framework to explore the issues of understanding and optimization of SDN control, in the presence of switch diversity. The basic idea of Tango is novel, simple, and yet quite powerful. In particular, different from all previous SDN control systems, which either ignore switch diversity or depend on that switches can and will report diverse switch implementation properties, Tango introduces a novel, proactive probing engine that infers key switch capabilities and behaviors, according to a well-structured set of Tango patterns, where a Tango pattern consists of a sequence of standard OpenFlow commands and a corresponding data traffic pattern. Utilizing the inference results from Tango patterns and additional application API hints, Tango conducts automatic switch control optimization, despite diverse switch capabilities and behaviors. Evaluating Tango on both hardware switches and emulated software switches, we show that Tango can infer flow table sizes, which are key switch implementation properties, within less than 5% of actual values, despite diverse switch caching algorithms, using a probing algorithm that is asymptotically optimal in terms of probing overhead. We demonstrate cases where routing and scheduling optimizations based on Tango improves the rule installation time by up to 70% in our hardware switch testbed.

References

[1]

P. Bosshart, D. Daly, G. Gibb, M. Izzard, N. McKeown, J. Rexford, C. Schlesinger, D. Talayco, A. Vahdat, G. Varghese, and D. Walker. P4: Programming protocol-independent packet processors. SIGCOMM Computer Communications Review, 2013.

Digital Library

[2]

A. R. Curtis, J. C. Mogul, J. Tourrilhes, P. Yalagandula, P. Sharma, and S. Banerjee. DevoFlow: Scaling flow management for high-performance networks. In Proceedings of ACM SIGCOMM, August 2011.

Digital Library

[3]

N. Foster, R. Harrison, M. J. Freedman, C. Monsanto, J. Rexford, A. Story, and D. Walker. Frenetic: A network programming language. In Proceedings of the 16th ACM SIGPLAN International Conference on Functional Programming (ICFP), September 2011.

Digital Library

[4]

D. Y. Huang, K. Yocum, and A. C. Snoeren. High-fidelity switch models for software-defined network emulation. In Proceedings of the Second ACM SIGCOMM Workshop on Hot Topics in Software Defined Networking (HotSDN), August 2013.

Digital Library

[5]

S. Jain, A. Kumar, S. Mandal, J. Ong, L. Poutievski, A. Singh, S. Venkata, J. Wanderer, J. Zhou, M. Zhu, J. Zolla, U. Hölzle, S. Stuart, and A. Vahdat. B4: Experience with a globally-deployed software defined WAN. In Proceedings of ACM SIGCOMM, August 2013.

Digital Library

[6]

X. Jin, H. H. Liu, R. Gandhi, S. Kandula, R. Mahajan, J. Rexford, R. Wattenhofer, and M. Zhang. Dionysus: Dynamic scheduling of network updates. In Proceedings of ACM SIGCOMM, August 2014.

Digital Library

[7]

N. Katta, J. Rexford, and D. Walker. Infinite CacheFlow in software-defined networks. Princeton Computer Science Technical Report TR-966--13, 2013.

[8]

T. Koponen, M. Casado, N. Gude, J. Stribling, L. Poutievski, M. Zhu, R. Ramanathan, Y. Iwata, H. Inoue, T. Hama, and S. Shenker. Onix: A distributed control platform for large-scale production networks. In Proceedings of the 9th USENIX Conference on Operating Systems Design and Implementation (OSDI), October 2010.

Digital Library

[9]

H. H. Liu, X. Wu, M. Zhang, L. Yuan, R. Wattenhofer, and D. Maltz. zUpdate: Updating data center networks with zero loss. In Proceedings of the ACM SIGCOMM, August 2013.

Digital Library

[10]

R. Mahajan and R. Wattenhofer. On consistent updates in software defined networks. In Proceedings of the 12th ACM Workshop on Hot Topics in Networks (HotNets-XII), November 2013.

Digital Library

[11]

N. McKeown, T. Anderson, H. Balakrishnan, G. Parulkar, L. Peterson, J. Rexford, S. Shenker, and J. Turner. OpenFlow: Enabling innovation in campus networks. SIGCOMM Computer Communications Review, 2008.

Digital Library

[12]

C. Monsanto, J. Reich, N. Foster, J. Rexford, and D. Walker. Composing software-defined networks. In Proceedings of the 10th USENIX Conference on Networked Systems Design and Implementation (NSDI), April 2013.

Digital Library

[13]

M. Moshref, M. Yu, A. Sharma, and R. Govindan. Scalable rule management for data centers. In Proceedings of the 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI), April 2013.

Digital Library

[14]

T. Nelson, A. D. Ferguson, M. J. Scheer, and S. Krishnamurthi. Tierless programming and reasoning for software-defined networks. In Proceedings of the 11th USENIX Symposium on Networked Systems Design and Implementation (NSDI), April 2014.

Digital Library

[15]

OF-config: https://github.com/AndreasVoellmy/data-driven-sdn-paper.

[16]

OpenFlow switch specification 1.4.0: https://www.opennetworking.org/images/stories/downloads/sdn-resources/onf-specifications/openflow/openflow-spec-v1.4.0.pdf.

[17]

Openvswitch: http://openvswitch.org/.

[18]

M. Reitblatt, N. Foster, J. Rexford, and D. Walker. Consistentupdates for software-defined networks: Change you can believe in! In Proceedings of the 10th ACM Workshop on Hot Topics in Networks (HotNets-X), November 2011.

Digital Library

[19]

C. Rotsos, N. Sarrar, S. Uhlig, R. Sherwood, and A. W. Moore. Oflops: An open framework for OpenFlow switch evaluation. In Proceedings of the 13th International Conference on Passive and Active Measurement, March 2012.

Digital Library

[20]

Ryu SDN controller: http://osrg.github.io/ryu/.

[21]

D. E. Taylor and J. S. Turner. Classbench: a packet classification benchmark. IEEE/ACM Transactions on Networking, pages 499--511, 2007.

Digital Library

[22]

The eXtensible DataPath Daemon (xdpd): https://www. codebasin.net/redmine/projects/xdpd/wiki.

[23]

A. Voellmy, J. Wang, Y. R. Yang, B. Ford, and P. Hudak. Maple: Simplifying SDN programming using algorithmic policies. In Proceedings of the ACM SIGCOMM, August 2013.

Digital Library

[24]

Mininet: An Instant Virtual Network on your Laptop. http://mininet.org.

Cited By

Chen KLiu SXu YSiddhrau IZhou SGuo ZChao H(2022)SDNShield: NFV-Based Defense Framework Against DDoS Attacks on SDN Control PlaneIEEE/ACM Transactions on Networking10.1109/TNET.2021.310518730:1(1-17)Online publication date: Feb-2022
https://doi.org/10.1109/TNET.2021.3105187
Zhang MLi GXu LBai JXu MGu GWu J(2021)Control Plane Reflection Attacks and Defenses in Software-Defined NetworksIEEE/ACM Transactions on Networking10.1109/TNET.2020.304077329:2(623-636)Online publication date: Apr-2021
https://doi.org/10.1109/TNET.2020.3040773
Eeric NVarasteh AVan Bemten AMas-Machuca CKellerer W(2021)Towards Understanding the Performance of Traffic Policing in Programmable Hardware Switches2021 IEEE 7th International Conference on Network Softwarization (NetSoft)10.1109/NetSoft51509.2021.9492560(70-78)Online publication date: 28-Jun-2021
https://doi.org/10.1109/NetSoft51509.2021.9492560
Show More Cited By

Index Terms

Tango: Simplifying SDN Control with Automatic Switch Property Inference, Abstraction, and Optimization
1. Networks
  1. Network services
    1. Network management
  2. Network types
    1. Packet-switching networks

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Published In

cover image ACM Conferences

CoNEXT '14: Proceedings of the 10th ACM International on Conference on emerging Networking Experiments and Technologies

December 2014

438 pages

ISBN:9781450332798

DOI:10.1145/2674005

General Chairs:
Aruna Seneviratne
NICTA/UNSW, Australia
,
Christophe Diot
Technicolor, France
,
Jim Kurose
Umass, USA
,
Program Chairs:
Augustin Chaintreau
Columbia University, USA
,
Luigi Rizzo
University of Pisa, Italy

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: 02 December 2014

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Division of Computer and Network Systems

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CoNEXT '14

Sponsor:

SIGCOMM

CoNEXT '14: Conference on emerging Networking Experiments and Technologies

December 2 - 5, 2014

Sydney, Australia

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CoNEXT '14 Paper Acceptance Rate 27 of 133 submissions, 20%;

Overall Acceptance Rate 198 of 789 submissions, 25%

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Cited By

Chen KLiu SXu YSiddhrau IZhou SGuo ZChao H(2022)SDNShield: NFV-Based Defense Framework Against DDoS Attacks on SDN Control PlaneIEEE/ACM Transactions on Networking10.1109/TNET.2021.310518730:1(1-17)Online publication date: Feb-2022
https://doi.org/10.1109/TNET.2021.3105187
Zhang MLi GXu LBai JXu MGu GWu J(2021)Control Plane Reflection Attacks and Defenses in Software-Defined NetworksIEEE/ACM Transactions on Networking10.1109/TNET.2020.304077329:2(623-636)Online publication date: Apr-2021
https://doi.org/10.1109/TNET.2020.3040773
Eeric NVarasteh AVan Bemten AMas-Machuca CKellerer W(2021)Towards Understanding the Performance of Traffic Policing in Programmable Hardware Switches2021 IEEE 7th International Conference on Network Softwarization (NetSoft)10.1109/NetSoft51509.2021.9492560(70-78)Online publication date: 28-Jun-2021
https://doi.org/10.1109/NetSoft51509.2021.9492560
Wan YSong HXu YZhang CWang YLiu B(2021)Adaptive Batch Update in TCAM: How Collective Optimization Beats Individual OnesIEEE INFOCOM 2021 - IEEE Conference on Computer Communications10.1109/INFOCOM42981.2021.9488758(1-10)Online publication date: 10-May-2021
https://doi.org/10.1109/INFOCOM42981.2021.9488758
Wang SSun CMeng ZWang MCao JXu MBi JHuang QMoshref MYang THu HZhang G(2020)Martini: Bridging the Gap between Network Measurement and Control Using Switching ASICs2020 IEEE 28th International Conference on Network Protocols (ICNP)10.1109/ICNP49622.2020.9259415(1-12)Online publication date: 13-Oct-2020
https://doi.org/10.1109/ICNP49622.2020.9259415
Kamath SSingh SKumar M(2020)Multiclass Queueing Network Modeling and Traffic Flow Analysis for SDN-Enabled Mobile Core Networks With Network SlicingIEEE Access10.1109/ACCESS.2019.29593518(417-430)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2019.2959351
Van Bemten AĐerić NZerwas JBlenk ASchmid SKellerer WMohaisen AZhang Z(2019)LokoProceedings of the 15th International Conference on Emerging Networking Experiments And Technologies10.1145/3359989.3365424(355-369)Online publication date: 3-Dec-2019
https://dl.acm.org/doi/10.1145/3359989.3365424
Marin EBucciol NConti MCavallaro LKinder JWang XKatz J(2019)An In-depth Look Into SDN Topology Discovery MechanismsProceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security10.1145/3319535.3354194(1101-1114)Online publication date: 6-Nov-2019
https://dl.acm.org/doi/10.1145/3319535.3354194
Liu SBenson TReiter M(2019)Efficient and Safe Network Updates with Suffix Causal ConsistencyProceedings of the Fourteenth EuroSys Conference 201910.1145/3302424.3303965(1-15)Online publication date: 25-Mar-2019
https://dl.acm.org/doi/10.1145/3302424.3303965
Eom THong JAn SPark JKim D(2019)Security and Performance Modeling and Optimization for Software Defined Networking2019 18th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/13th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE)10.1109/TrustCom/BigDataSE.2019.00087(610-617)Online publication date: Aug-2019
https://doi.org/10.1109/TrustCom/BigDataSE.2019.00087
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