DeepMatch: Fine-Grained Traffic Flow Measurement in SDN With Deep Dueling Neural Networks

摘要

In this paper, we propose a novel flow rule matching framework, DeepMatch, in Software-Defined Networking (SDN) to provide a fine-grained traffic flow measurement capability. Specifically, the flow rule matching control at a particular SDN switch is examined to maximize the traffic flow granularity degree while proactively protecting the flow-table in the switch from being overflowed. This control process is supervised by a control module referred to as DeepMatch instance. Regarding this instance, an optimization problem is formulated based on a Markov decision process (MDP) and a Partially Observable Markov decision process (POMDP), respectively. We develop a deep dueling neural network based flow rule matching control algorithm to solve the optimization problem, thereby quickly attaining a significant traffic flow granularity level and eliminating the switch flow-table overflow problem. Furthermore, we propose an experience data sharing (EDS) mechanism that enables a new instance to learn faster about the flow rule matching control. The results of our performance evaluation show that, by applying the DeepMatch framework in a highly dynamic traffic scenario, the traffic flow granularity degree at the access and the core switches increases by 24.0 and 31.63, respectively, compared to the FlowStat method. DeepMatch is also highly outperforming the ReWiFlow, SDN-Mon, and Exact-Match approaches. In addition, by employing the EDS mechanism, a new instance can reduce its learning time up to 46.42 for supervising an access switch and up to 37.50 for supervising a core switch.