Failure mitigation in software defined networking employing load type prediction
Alghadhban, Amer Mohammad JarAlla
Alalmaei, Shiyam Mohammed Abdullah
KAUST DepartmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computer Science Program
Electrical Engineering Program
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AbstractThe controller is a critical piece of the SDN architecture, where it is considered as the mastermind of SDN networks. Thus, its failure will cause a significant portion of the network to fail. Overload is one of the common causes of failure since the controller is frequently invoked by new flows. Even through SDN controllers are often replicated, the significant recovery time can be an overkill for the availability of the entire network. In order to overcome the problem of the overloaded controller failure in SDN, this paper proposes a novel controller offload solution for failure mitigation based on a prediction module that anticipates the presence of a harmful long-term load. In fact, the long-standing load would eventually overwhelm the controller leading to a possible failure. To predict whether the load in the controller is short-term or long-term load, we used three different classification algorithms: Support Vector Machine, k-Nearest Neighbors, and Naive Bayes. Our evaluation results demonstrate that Support Vector Machine algorithm is applicable for detecting the type of load with an accuracy of 97.93% in a real-time scenario. Besides, our scheme succeeded to offload the controller by switching between the reactive and proactive mode in response to the prediction module output.
CitationBouacida N, Alghadhban A, Alalmaei S, Mohammed H, Shihada B (2017) Failure mitigation in software defined networking employing load type prediction. 2017 IEEE International Conference on Communications (ICC). Available: http://dx.doi.org/10.1109/ICC.2017.7997295.
Conference/Event name2017 IEEE International Conference on Communications, ICC 2017