Deployment Algorithm of Service Function Chain Based on Transfer Actor-Critic Learning

To solve the problem of high system delay caused by unreasonable resource allocation because of randomness and unpredictability of service requests in 5G network slicing, this paper proposes a deployment scheme of Service Function Chain (SFC) based on Transfer Actor-Critic (A-C) Algorithm (TACA). Firstly, an end-to-end delay minimization model is built based on Virtual Network Function (VNF) placement, and joint allocation of computing resources, link resources and fronthaul bandwidth resources, then the model is transformed into a discrete-time Markov Decision Process (MDP). Next, A-C learning algorithm is adopted in the MDP to adjust dynamically SFC deployment scheme by interacting with environment, so as to optimize the end-to-end delay. Furthermore, in order to realize and accelerate the convergence of the A-C algorithm in similar target tasks (such as the arrival rate of service requests is generally higher), the transfer A-C algorithm is adopted to utilize the SFC deployment knowledge learned from source tasks to find quickly the deployment strategy in target tasks. Simulation results show that the proposed algorithm can reduce and stabilize the queuing length of SFC packets, optimize the system end-to-end delay, and improve resource utilization.