poster

Latency in Software Defined Networks: Measurements and Mitigation Techniques

Authors:

Keqiang He,

Junaid Khalid,

Sourav Das,

Aaron Gember-Jacobson,

Chaithan Prakash,

Aditya Akella,

Li Erran Li,

Marina ThottanAuthors Info & Claims

SIGMETRICS '15: Proceedings of the 2015 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems

Pages 435 - 436

https://doi.org/10.1145/2745844.2745880

Published: 15 June 2015 Publication History

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Abstract

We conduct a comprehensive measurement study of switch control plane latencies using four types of production SDN switches. Our measurements show that control actions, such as rule installation, have surprisingly high latency, due to both software implementation inefficiencies and fundamental traits of switch hardware. We also propose three measurement-driven latency mitigation techniques---optimizing route selection, spreading rules across switches, and reordering rule installations---to effectively tame the flow setup latencies in SDN.

References

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

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K. He, J. Khalid, S. Das, A. Akella, E. L. Li, and M. Thottan. Mazu: Taming latency in software defined networks. University of Wisconsin-Madison Technical Report, 2014.

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D. Y. Huang, K. Yocum, and A. C. Snoeren. High-fidelity switch models for software-defined network emulation. In HotSDN, 2013.

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N. McKeown, T. Anderson, H. Balakrishnan, G. Parulkar, L. Peterson, J. Rexford, S. Shenker, and J. Turner. Openflow: enabling innovation in campus networks. SIGCOMM CCR, 2008.

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Cited By

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Kang LChen XChen J(2022)Design and Implementation of Enhanced Programmable Data Plane Supporting ICN MobilityElectronics10.3390/electronics1116252411:16(2524)Online publication date: 12-Aug-2022
https://doi.org/10.3390/electronics11162524
Guan SLi JLi YWang Z(2022)A multi‐controller placement method for software defined network based on improved firefly algorithmTransactions on Emerging Telecommunications Technologies10.1002/ett.448233:7Online publication date: 7-Mar-2022
https://doi.org/10.1002/ett.4482
Harkous HJarschel MHe MPries RKellerer W(2021)P8: P4 With Predictable Packet Processing PerformanceIEEE Transactions on Network and Service Management10.1109/TNSM.2020.303010218:3(2846-2859)Online publication date: Sep-2021
https://doi.org/10.1109/TNSM.2020.3030102
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Index Terms

Latency in Software Defined Networks: Measurements and Mitigation Techniques
1. Hardware
  1. Communication hardware, interfaces and storage
2. Networks

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Latency in Software Defined Networks: Measurements and Mitigation Techniques
Performance evaluation review

We conduct a comprehensive measurement study of switch control plane latencies using four types of production SDN switches. Our measurements show that control actions, such as rule installation, have surprisingly high latency, due to both software ...
Measuring control plane latency in SDN-enabled switches
SOSR '15: Proceedings of the 1st ACM SIGCOMM Symposium on Software Defined Networking Research

Timely interaction between an SDN controller and switches is crucial to many SDN applications---e.g., fast rerouting during link failure and fine-grained traffic engineering in data centers. However, it is not well understood how the control plane in ...
Analytical Modelling of Software and Hardware Switches with Internal Buffer in Software-Defined Networks
Abstract
OpenFlow supports internal buffering of data packets in Software-Defined Networking (SDN) switch whereby a fraction of data packet header is sent to the controller instead of an entire data packet. This internal buffering increases the ...
Graphical abstract

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Highlights
- We propose a unified queues model for hardware and software SDN switch with internal buffer.

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Published In

SIGMETRICS '15: Proceedings of the 2015 ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems

June 2015

488 pages

ISBN:9781450334860

DOI:10.1145/2745844

General Chairs:
Bill Lin
University of California, San Diego
,
Jun (Jim) Xu
Georgia Tech
,
Program Chairs:
Sudipta Sengupta
Microsoft Research
,
Devavrat Shah
Massachusetts Institute of Technology

ACM SIGMETRICS Performance Evaluation Review Volume 43, Issue 1
Performance evaluation review
June 2015
468 pages
ISSN:0163-5999
DOI:10.1145/2796314
Editors:
Derek Eager
University of Saskatchewan
,
Carey Williamson
University of Calgary
Issue’s Table of Contents

Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 June 2015

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SIGMETRICS '15

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SIGMETRICS

SIGMETRICS '15: ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems

June 15 - 19, 2015

Oregon, Portland, USA

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SIGMETRICS '15 Paper Acceptance Rate 32 of 239 submissions, 13%;

Overall Acceptance Rate 459 of 2,691 submissions, 17%

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Cited By

View all

Kang LChen XChen J(2022)Design and Implementation of Enhanced Programmable Data Plane Supporting ICN MobilityElectronics10.3390/electronics1116252411:16(2524)Online publication date: 12-Aug-2022
https://doi.org/10.3390/electronics11162524
Guan SLi JLi YWang Z(2022)A multi‐controller placement method for software defined network based on improved firefly algorithmTransactions on Emerging Telecommunications Technologies10.1002/ett.448233:7Online publication date: 7-Mar-2022
https://doi.org/10.1002/ett.4482
Harkous HJarschel MHe MPries RKellerer W(2021)P8: P4 With Predictable Packet Processing PerformanceIEEE Transactions on Network and Service Management10.1109/TNSM.2020.303010218:3(2846-2859)Online publication date: Sep-2021
https://doi.org/10.1109/TNSM.2020.3030102
Khoobbakht MNoei MParvizimosaed M(2021)Hybrid Flow-Rule Placement Method of Proactive and Reactive in SDNs2021 11th International Conference on Computer Engineering and Knowledge (ICCKE)10.1109/ICCKE54056.2021.9721507(121-127)Online publication date: 28-Oct-2021
https://doi.org/10.1109/ICCKE54056.2021.9721507
Vargaftik SCaba CSchour LBen-Itzhak Y(2020)C-shareACM SIGCOMM Computer Communication Review10.1145/3390251.339025350:1(2-9)Online publication date: 23-Mar-2020
https://dl.acm.org/doi/10.1145/3390251.3390253
Rzepka MBorylo PLason ASzymanski A(2020)PARD: Hybrid Proactive and Reactive Method Eliminating Flow Setup Latency in SDNJournal of Network and Systems Management10.1007/s10922-020-09550-zOnline publication date: 9-Jul-2020
https://doi.org/10.1007/s10922-020-09550-z
Rzym GBoryło PChołda P(2020)A time‐efficient shrinkage algorithm for the Fourier‐based prediction enabling proactive optimisation in software‐defined networksInternational Journal of Communication Systems10.1002/dac.444833:12Online publication date: 29-Apr-2020
https://doi.org/10.1002/dac.4448
Yuan BZou DYu SJin HQiang WShen J(2019)Defending Against Flow Table Overloading Attack in Software-Defined NetworksIEEE Transactions on Services Computing10.1109/TSC.2016.260286112:2(231-246)Online publication date: 1-Mar-2019
https://doi.org/10.1109/TSC.2016.2602861
Bera SMisra SObaidat M(2019)Mobi-Flow: Mobility-Aware Adaptive Flow-Rule Placement in Software-Defined Access NetworkIEEE Transactions on Mobile Computing10.1109/TMC.2018.286893218:8(1831-1842)Online publication date: 1-Aug-2019
https://doi.org/10.1109/TMC.2018.2868932
Zhauniarovich YDodia P(2019)Sorting the Garbage: Filtering Out DRDoS Amplification Traffic in ISP Networks2019 IEEE Conference on Network Softwarization (NetSoft)10.1109/NETSOFT.2019.8806653(142-150)Online publication date: Jun-2019
https://doi.org/10.1109/NETSOFT.2019.8806653
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