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Title: Track: Tracerouting in SDN networks with arbitrary network functions
Authors: Zhang, Yuxiang
Cui, Lin
Tso, Fung Po
Zhang, Yuan
Keywords: Network diagnostics
Network function
Software-defined networking
Issue Date: 2017
Publisher: © IEEE
Citation: ZHANG, Y. ... et al, 2017. Track: Tracerouting in SDN networks with arbitrary network functions. IN: 2017 IEEE 6th International Conference on Cloud Networking (CloudNet), Prague, Czech Republic, 25-27 September 2017.
Abstract: The centralization of control plane in Software defined networking (SDN) creates a paramount challenge on troubleshooting the network as packets are ultimately forwarded by distributed data planes. Existing path tracing tools largely utilize packet tags to probe network paths among SDN-enabled switches. However, network functions (NFs) or middleboxes, whose presence is ubiquitous in today's networks, can drop packets or alter their tags - an action that can collapse the probing mechanism. In addition, sending probing packets through network functions could corrupt their internal states, risking of the correctness of servicing logic (e.g., incorrect load balancing decisions). In this paper, we present a novel troubleshooting tool, Track, for SDN-enabled network with arbitrary NFs. Track can discover the forwarding path including NFs taken by any packets, without changing the forwarding rules in switches and internal states of NFs. We have implemented Track on RYU controller. Our extensive experiment results show that Track can achieve 95.08% and 100% accuracy for discovering forwarding paths with and without NFs respectively, and can efficiently generate traces within 3 milliseconds per hop.
Description: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Sponsor: This work is partially supported by Chinese National Research Fund (NSFC) Project No. 61402200; the UK Engineering and Physical Sciences Research Council (EPSRC) grants EP/P004407/1 and EP/P004024/1; the Fundamental Research Funds for the Central Universities (21617409); the Opening Project of Guangdong Province Key Laboratory of Big Data Analysis and Processing (2017009).
Version: Accepted for publication
DOI: 10.1109/CloudNet.2017.8071526
URI: https://dspace.lboro.ac.uk/2134/33374
Publisher Link: https://doi.org/10.1109/CloudNet.2017.8071526
ISBN: 9781509040261
Appears in Collections:Conference Papers and Presentations (Computer Science)

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