Resource allocation Algorithm of Service Function Chain Based on Asynchronous Advantage Actor-Critic Learning

Abstract

Considering the fact that global network information is hard to obtain, and the slice resource allocation optimization problem caused by mobility of User Equipment (UE) and dynamics of packet arrival in the radio access network slice, a Service Function Chain(SFC)resource allocation algorithm based on Asynchronous Advantage Actor-Critic (A3C) learning is proposed. Firstly, a resource management mechanism based on blockchain technology is established, which can credibly share and update the global network information, also supervise and record SFC resource allocation process. Then, a delay minimization model based on joint allocation of radio resources, computing resources and bandwidth resources is built under the circumstance of UE moving and time-varying packet arrival, and further transformed into an Markov Decision Process(MDP) problem. At last, A3C learning method is adopted to obtain the resource allocation optimization strategy in this MDP. Simulation results show that the proposed algorithm could utilize resources more efficiently to optimize the system delay while guarantee the requirement of each UE.