Skip to content

Virtual topology partitioning towards an efficient failure recovery of software defined networks

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Software Defined Networking is a new networking paradigm that has emerged recently as a promising solution for tackling the inflexibility of the classical IP networks. The centralized approach of SDN yields a broad area for intelligence to optimise the network at various levels. Fault tolerance is considered one of the most current research challenges that facing the SDN, hence, in this paper we introduce a new method that computes an alternative paths re-actively for centrally controlled networks like SDN. The proposed method aims to reduce the update operation cost that the SDN network controller would spend in order to recover from a single link failure. Through utilising the principle of community detection, we define a new network model for the sake of improving the network's fault tolerance capability. An experimental study is reported showing the performance of the proposed method. Based on the results, some further directions are suggested in the context of machine learning towards achieving further advances in this research area.
Original languageEnglish
Title of host publication2017 International Conference on Machine Learning and Cybernetics (ICMLC)
Number of pages6
ISBN (Electronic)978-1538604083
ISBN (Print)978-1538604069
Publication statusPublished - 16 Nov 2017
EventThe 16th International Conference on Machine Learning and Cybernetics (ICMLC) - Ningbo, China
Duration: 9 Jul 201712 Jul 2017

Publication series

NameIEEE ICMLC Proceedings Series
ISSN (Electronic)2160-1348


ConferenceThe 16th International Conference on Machine Learning and Cybernetics (ICMLC)


  • ICMLC_short_paper-3

    Rights statement: © 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.

    Accepted author manuscript (Post-print), 559 KB, PDF document

Related information

Relations Get citation (various referencing formats)

ID: 8677005