Machine Learning-Based Prediction Models for Control Traffic in SDN Systems

This article presents Elixir, an automated prediction model formulation framework for control traffic using machine learning. Control traffic is vital in software-defined networking (SDN) systems because it determines the reliability and scalability of the entire system. Various studies have sought to design control traffic prediction models for the proper provisioning and planning of SDN systems. However, previously proposed models are based on descriptive modeling, well-suited for only specific SDN system instances. Furthermore, these models exhibit poor accuracy (errors of up to 85%) because of the heterogeneity of SDN systems. Because descriptive modeling requires a significant amount of human contemplation, it is impossible to formulate adequate prediction models for countless SDN system instances. Elixir addresses this problem by applying machine learning. Elixir starts the model formulation through self-generated datasets. Then, Elixir searches prediction models to fit the accuracy for respective SDN systems. Also, Elixir picks robust models that exhibit reasonable accuracy even in a network topology that differs from the topology used for model training. We evaluate the Elixir framework on nine heterogeneous SDN systems. As a key outcome, Elixir significantly reduces prediction errors, achieving up to 10.6x improvement compared to the previous model for control traffic throughput of OpenDayLight controller.