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• W3109939632 abstract "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." @default.
• W3109939632 created "2020-12-07" @default.
• W3109939632 creator A5029131634 @default.
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• W3109939632 date "2021-07-01" @default.
• W3109939632 modified "2023-10-16" @default.
• W3109939632 title "DeepMatch: Fine-Grained Traffic Flow Measurement in SDN With Deep Dueling Neural Networks" @default.
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• W3109939632 doi "https://doi.org/10.1109/jsac.2020.3041406" @default.
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