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.
|IEEE ICMLC Proceedings Series
|The 16th International Conference on Machine Learning and Cybernetics (ICMLC)
|9/07/17 → 12/07/17
- failure recovery
- network partitioning