Learning-based hybrid routing for scalability in software defined networks

摘要

Software Defined Network is an emerging paradigm in computer networks. The separation of the control plane from the forwarding plane in this arrangement has different aspects. This splitting provides many advantages like easy manageability and configuration. Along with benefits, various issues specific to this paradigm also arise. Routing management in such a paradigm deals with diverse concerns, objectives, and parameters before selecting the best route. Reinforcement Learning has already proven its strength in distinct fields like business, industry automation, gaming, algorithms, etc. Even routing in a network can also be made efficient using concepts defined in reinforcement learning. In this paper, routing within a controller's area is modeled, keeping scalability in mind; and an optimal solution is provided using learning. Both proactive and reactive approaches are used for flow installation, and the link load is utilized optimally. The area under a particular controller is efficiently routed, and it tweaks the network. Q-learning model helps to learn the optimal path and provide the best route in case of a failure. Once the learning completes, the model works on it. Preliminary evaluation depicts that improvement of 78%, 58%, and 47 % is achieved for the number of messages generation when compared with other already exiting solutions for routing in Software Defined Networks.