Multi-Resource Computing Offload Strategy for Energy Consumption Optimization in Mobile Edge Computing

Abstract

The energy consumption optimization of edge devices in the mobile edge computing environment is mainly based on computational offload strategy. Most of the current common computing offload strategies only consider a single computing resource and do not comprehensively consider different kinds of computing resources in mobile edge computing environments, which cannot fully reduce the energy consumption of edge devices under the condition of ensuring response time constraints. To solve this problem, a multi-resource computing unloading energy consumption model is proposed in the mobile edge computing environment, and a new fitness calculation method for evaluating the energy consumption of edge devices is designed. Combined with the workflow management system, a multi-resource computing offloading particle swarm optimization task scheduling algorithm for energy consumption optimization in mobile edge computing is proposed. The algorithm can fully reduce the energy consumption of mobile terminals under the condition of considering the response time constraint. Experiments show that, compared with the existing four algorithms, the task scheduling algorithm corresponding to the new strategy has stable convergence and optimal fitness. Under the constraint of user response time, the energy consumption of edge devices in the task scheduling scheme is better than the other four unloading strategies.