The Intelligent Routing Control Strategy Based on Deep Learning

Abstract

The rapid development of computer hardware and software provides a suitable platform for machine learning, in which deep learning has become a breakthrough in machine learning technology in various fields in many disciplines. Some recent research efforts have focused on routing control based on deep learning. Therefore, this paper studies the problem of intelligent routing, and aims to propose an intelligent control strategy based on deep learning with the help of Software-Defined Network (SDN) and other new network technologies. The characteristics of SDN network that can easily obtain the network topology have laid the foundation for selecting different routing paths according to the different QoS levels of the flow. Nowadays, the routing modules in commonly used SDN controllers use the shortest path algorithm which is simple to implement and works effectively. However, the best path calculated by controllers may suffer from huge traffic load and result in congestion, and the controllers cannot learn from the previous experiences to intelligently switch to other paths. This paper present intelligent routing control strategy based on Deep Q-Learning (DQN) in SDN, which uses the Openflow to collect information from the network, and aggregates them to the SDN controller, and then uses DQN to generate the specific routing for forwarders.KeywordsSDNDeep learningRouting strategyReinforcement learning