Learning-based joint service caching and load balancing for MEC blockchain networks

Abstract

Integrating the blockchain technology into mobile-edge computing (MEC) networks with multiple cooperative MEC servers (MECS) providing a promising solution to improving resource utilization, and helping establish a secure reward mechanism that can facilitate load balancing among MECS. In addition, intelligent management of service caching and load balancing can improve the network utility in MEC blockchain networks with multiple types of workloads. In this paper, we investigate a learning-based joint service caching and load balancing policy for optimizing the communication and computation resources allocation, so as to improve the resource utilization of MEC blockchain networks. We formulate the problem as a challenging long-term network revenue maximization Markov decision process (MDP) problem. To address the highly dynamic and high dimension of system states, we design a joint service caching and load balancing algorithm based on the double-dueling Deep Q network (DQN) approach. The simulation results validate the feasibility and superior performance of our proposed algorithm over several baseline schemes.