Using NFV and Reinforcement Learning for Anomalies Detection and Mitigation in SDN

Abstract

Computer networks are subject to several anomalies, which leads to the necessity of techniques to coordinate detection and mitigation to keep the network operational. In this paper we propose the use of reinforcement learning to promote resilience in Software Defined Networking (SDN). In particular, it is proposed collecting network metrics and grouping them into profiles, each one having a set of actions that handles problems using reinforcement learning, Network Functions Virtualization (NFV), and an SDN controller. Policies for dealing with anomalies are defined based on rewards for each action. Results show that the system obtains mostly positive rewards, but a small increment in the topology size leads to more than four times the number of entries in the state-action table.