Slicing-based resource optimization in multi-access edge network using ensemble learning aided DDPG algorithm

Recently, the technological development in edge computing and content caching can provide high-quality services for users in the wireless communication networks. As a promising technology, multi-access edge computing (MEC) can offload tasks to the nearby edge servers, which alleviates the pressure of users. However, various services and dynamic wireless channel conditions make effective resource allocation challenging. In addition, network slicing can create a logical virtual network and allocate resources flexibly among multiple tenants. In this paper, we construct an integrated architecture of communication, computing and caching to solve the joint optimization problem of task scheduling and resource allocation. In order to coordinate network functions and dynamically allocate limited resources, this paper adopts an improved deep reinforcement learning (DRL) method, which fully jointly considers the diversity of user request services and the dynamic wireless channel conditions to obtain the mobile virtual network operator (MVNO) maximal profit function. Considering the slow convergence speed of the DRL algorithm, this paper combines DRL and ensemble learning. The simulation result shows that the resource allocation scheme inspired by DRL is significantly better than the other compared strategies. The output of the result of DRL algorithm combined with ensemble learning is faster and more cost-effective.