Service Chain Mapping Algorithm Based on Reinforcement Learning
Wei Li, Haiyang Wu, Chunxia Jiang, Ping Jia, Naling Li, Peng Lin · 2020
Network function virtualization integrates different types of dedicated network equipment into standard industry IT server, storage and switch equipment, enabling network functions traditionally implemented using specific equipment to use software running on IT industry standard server hardware, thereby enhancing system flexibility. Using the organic combination of NFV and software-defined network technologies, an software defined Smart grid communication network can be constructed, so that the network functions of service function chaining can be implemented on general-purpose equipment, and end-to-end services are transformed into a set of sequentially connected VNFs, which can effectively deploy and manage service function chains. Service provision and server resource utilization will be affected by SFC mapping. In order to ensure the reasonable use of network resources and the QoS of SFC, the research on SFC mapping algorithms is particularly important. In this paper, we propose a service chain mapping algorithm based on reinforcement learning, which aims to learn by the system status and the feedback value given by the mapped environment and then finally determine the actual deployment location of each virtual function node in the SFC. The comparison and analysis with other algorithms show that the SFC mapping algorithm proposed in this paper can adjust the feedback value function to optimize the load balance of the system and reduce the SFC average link delay in different network topologies.