关键词: 服务功能链部署, 强化学习, 马尔科夫决策过程, 异步优势Actor-Critic, 图注意力网络, 门控循环单元

Abstract: ToIn order to efficiently allocate limited network resources in cloud computing whileto ensuringe service quality, while and improving resource utilization and management efficiency, an encoder-decoder-based deep reinforcement learning algorithm (ED-DRL) was proposed for service function chain (SFC) deployment. this paper proposes a deep reinforcement learning algorithm based on an encoder-decoder structure (ED-DRL) for the deployment of Service Function Chains (SFCs). The SFC placement was formulated as a Markov decision process, and a reinforcement learning approach with a graph attention network (GAT) encoder and a gated recurrent unit (GRU) decoder was employed to effectively extract network topology features and inter-node dependencies.The algorithm first treats SFC placement as a Markov decision process, then employs a reinforcement learning method with a Graph Attention Network (GAT) encoder and a Gated Recurrent Unit (GRU) decoder structure to efficiently extract network topology features and inter-node dependencies. By integratingCombined with the Asynchronous Advantage Actor-Critic (A3C) method, the algorithm was capable of generates secure and reliable SFC placement strategies in dynamic environments.SFC placement strategies that are reliable and secure in dynamic environments. Simulation results demonstrateshow that the encoder-decoder structure reinforcement learning approachmethod, which considerings security and reliability, achieved an acceptance rate of 70.1% and an average reward of 0.0635, outperforming existing algorithmsoutperforms existing algorithms in terms of acceptance rate, average reward, and average running time.

Key words: Service Function Chain Deployment, Reinforcement Learning, Markov Decision Process, Asynchronous Advantage Actor-Critic, Graph Attention Network, Gated Recurrent Unit