Utility Optimization for Resource Allocation in Edge Network Slicing Using DRL

Abstract

Network slicing and Multi-access Edge Computing (MEC) have been envisioned as promising technique in the fifth generation mobile communication (5G). In this work, we study joint optimization of radio and computation resource in network slicing with MEC to maximize utility of Mobile Virtual Network Operator (MVNO), while meeting slice Quality of Service (QoS) requirements. On account of the dynamic change of slice demands and environment information, it is hard to solve resource allocation problems with conventional methods. Inspired by the superiority of deep reinforcement learning (DRL) in decision-making problems with the high state space and continuous action space. We formulate the utility maximization problem as a markov decision process (MDP). With an MVNO controller, the problem can be solved utilizing deep deterministic policy gradient (DDPG) algorithm to execute the dynamic resource allocation scheme. Simulation results show that utility performance of the proposed algorithm outperforms than the benchmark algorithms and enables dynamic resource allocation scheme.