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Failure mitigation in software defined networking employing load type prediction

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Publication date28/07/2017
Host publication2017 IEEE International Conference on Communications, ICC 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages7
ISBN (Electronic)9781467389990
ISBN (Print)9781467390002
<mark>Original language</mark>English
Event2017 IEEE International Conference on Communications, ICC 2017 - Paris, France
Duration: 21/05/201725/05/2017

Conference

Conference2017 IEEE International Conference on Communications, ICC 2017
Country/TerritoryFrance
CityParis
Period21/05/1725/05/17

Conference

Conference2017 IEEE International Conference on Communications, ICC 2017
Country/TerritoryFrance
CityParis
Period21/05/1725/05/17

Abstract

The 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.