Deep Reinforcement Learning Application for Network Latency Management in Software Defined Networks

The centralization of network intelligence enabled by Software Defined Networking (SDN), and the recent breakthroughs of Machine Learning (ML), paved the way to address a variety of network challenges. Quality-of-Service (QoS)-aware routing is one of the important challenges in SDN-based networks, especially when multiple flows coexist in the same network. Optimizing network performances (i.e., end-to-end delay, throughput) must be achieved in order to enhance the performance of QoS-aware routing. Neural Networks and Reinforcement learning are ML breakthroughs that can tackle this important challenge. To this end, we propose, in this paper, an efficient rules placement algorithm based on Deep Reinforcement Learning (DRL) and traffic prediction. Our proposal aims to dynamically collect the optimal path from the DRL agent and predict future traffic demands using the well-known prediction method LSTM. To do so, we first formulate the flow rules placement as an Integer Linear Program (ILP) that aims to minimize the total network delay. Then, we propose a simple yet efficient heuristic algorithm to solve the formulated ILP problem with low time complexity and high estimation accuracy. The proposed algorithm interacts with the DRL agent to get the optimal path and the traffic prediction module in order to avoid congestion. The obtained results using ONOS controller and OpenvSwitch revealed the efficiency of the proposed approach in decreasing both network latency and packet loss, and rising the network throughput.