@inproceedings{29d065d82b924e4ab6d6dfaa8d27c081,
title = "Virtual topology partitioning towards an efficient failure recovery of software defined networks",
abstract = "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.",
keywords = "SDN, failure recovery, network partitioning",
author = "Ali Malik and Benjamin Aziz and Han Liu and Mo Adda and Chih-Heng Ke",
year = "2017",
month = nov,
day = "16",
doi = "10.1109/ICMLC.2017.8108982",
language = "English",
isbn = "978-1538604069 ",
series = "IEEE ICMLC Proceedings Series",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "646--651",
booktitle = "2017 International Conference on Machine Learning and Cybernetics (ICMLC)",
address = "United States",
note = "The 16th International Conference on Machine Learning and Cybernetics (ICMLC) ; Conference date: 09-07-2017 Through 12-07-2017",
}