An Efficient Task Offloading Approach Based on Multi-Objective Evolutionary Algorithm in Cloud-Edge Collaborative Environment

Abstract

Cloud-edge collaboration can overcome the shortcomings of high latency of the centralized model of cloud computing and insufficient computing power of edge computing, and better meet the needs of various application scenarios, which has become a research hotspot in recent years. The 6 G network is driven by the deep integration of network and computing, which will further accelerate the collaborative and integrated development of the cloud datacenter, edge servers, end devices, and application. This paper considers the combination of edge computing and cloud computing, and studies the problem of task offloading with the optimization objectives of energy consumption, delay, and multi-node load balancing under an “End-Edge-Cloud” collaborative architecture for 6G network. We regard the problem of task offloading as a multi-objective optimization problem. By improving AR-MOEA, a task offloading algorithm based on the “End-Edge-Cloud” architecture (TO-EEC) is proposed. It is verified through experiments that TO-EEC has a fast convergence speed. Compared with other similar offloading algorithms, TO-EEC has significant optimization effects on energy consumption, delay and multi-node load variance under multiple constraints.