Game-Theory-Based Task Offloading and Resource Scheduling in Cloud-Edge Collaborative Systems

Task offloading and resource allocation are the major elements of edge computing. A reasonable task offloading strategy and resource allocation scheme can reduce task processing time and save system energy consumption. Most of the current studies on the task migration of edge computing only consider the resource allocation between terminals and edge servers, ignoring the huge computing resources in the cloud center. In order to sufficiently utilize the cloud and edge server resources, we propose a coarse-grained task offloading strategy and intelligent resource matching scheme under Cloud-Edge collaboration. We consider the heterogeneity of mobile devices and inter-channel interference, and we establish the task offloading decision of multiple end-users as a game-theory-based task migration model with the objective of maximizing system utility. In addition, we propose an improved game-theory-based particle swarm optimization algorithm to obtain task offloading strategies. Experimental results show that the proposed scheme outperforms other schemes with respect to latency and energy consumption, and it scales well with increases in the number of mobile devices.

Automated summary

Task offloading and resource allocation are crucial in edge computing, as they can reduce processing time and energy consumption. Current studies mainly focus on resource allocation between terminals and edge servers, disregarding the computing resources in the cloud center. To address this, we propose a coarse-grained task offloading strategy and intelligent resource matching scheme that leverages both cloud and edge server resources. Our approach considers mobile device heterogeneity and inter-channel interference, and maximizes system utility through a game-theory-based task migration model. Experimental results demonstrate the superiority of our scheme in terms of latency, energy consumption, and scalability.