Network Failure and Anomaly Prediction to Achieve Quality of Service (QoS) on Software-Defined Networks
Yaren Cilek, Ali Furkan Demirbas, Volkan Rodoplu · 2022 Innovations in Intelligent Systems and Applications Conference (ASYU) · 2022
We develop a predictive optimization program to resolve anomalies and failures on Software Defined Networks (SDN) proactively in order to prevent such failures before they render important services like health, security, and production unavailable. The previous studies on preventing network anomalies or failures took a reactive approach by which the anomalies are resolved after they occur. Our program predicts if the incoming 5G flows will cause an anomaly on the nodes by using machine learning and then leverages a linear optimization program to find the best routes for such flows to be admitted safely. Our program is network topology agnostic; hence, it can be run on any topology. Since our approach resolves such anomalies proactively and makes sure the important services are always continuous and available for the communities, it holds the potential to impact the design of SDNs in the near future.