Deployment Algorithm of Service Function Chain Based on Multi-Agent Soft Actor-Critic Learning

Considering the problem of Service Function Chain (SFC) deployment optimization caused by the dynamic change of service requests under the Network Function Virtualization (NFV) architecture, an SFC deployment optimization algorithm based on Multi-Agent Soft Actor-Critic (MASAC) learning is proposed. Firstly, the model of minimizing resource load penalty, SFC deployment cost and delay cost is established, which is constrained by SFC end-to-end delay and reservation threshold of network resource. Secondly, the stochastic optimization is transformed into a Markov Decision Process (MDP) to realize the dynamic deployment of SFC and the balanced scheduling of resources. The arrangement scheme according to services division for multiple decision makers is further proposed. At last, the Soft Actor-Critic (SAC) algorithm is adopted in distributed multi-agent system to enhance exploration, then the central attention mechanism and advantage function are further introduced, which can dynamically and selectively focus on the information to obtain greater deployment return. Simulation results show that the proposed algorithm can optimize the load penalty, delay and deployment cost, and scale better with the increase of service requests.