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Software-Defined Networking (SDN) introduces a centralized network controland management by separating the data plane from the control plane whichfacilitates traffic flow monitoring, security analysis and policy formulation.However, it is challenging to choose a proper degree of traffic flow handlinggranularity while proactively protecting forwarding devices from gettingoverloaded. In this paper, we propose a novel traffic flow matching controlframework called Q-DATA that applies reinforcement learning in order to enhancethe traffic flow monitoring performance in SDN based networks and preventtraffic forwarding performance degradation. We first describe and analyse anSDN-based traffic flow matching control system that applies a reinforcementlearning approach based on Q-learning algorithm in order to maximize thetraffic flow granularity. It also considers the forwarding performance statusof the SDN switches derived from a Support Vector Machine based algorithm.Next, we outline the Q-DATA framework that incorporates the optimal trafficflow matching policy derived from the traffic flow matching control system toefficiently provide the most detailed traffic flow information that othermechanisms require. Our novel approach is realized as a REST SDN applicationand evaluated in an SDN environment. Through comprehensive experiments, theresults show that---compared to the default behavior of common SDN controllersand to our previous DATA mechanism---the new Q-DATA framework yields aremarkable improvement in terms of traffic forwarding performance degradationprotection of SDN switches while still providing the most detailed traffic flowinformation on demand.

Q-DATA: Enhanced Traffic Flow Monitoring in Software-Defined Networks applying Q-learning