A 'Joint-Me' Task Deployment Strategy for Load Balancing in Edge Computing

Task deployment has become a hot topic for load balancing in edge computing. In view of the problem that most of the hosts are overloaded in the edge computing, the central load is unbalanced. Much work focuses on the load balancing of the cloud data center or the short-term load balancing of edge data centers. In order to solve the host selection problem of task deployment in joint cloud data centers with edge computing while achieving the overall long-term load balancing, this paper utilizes that the deployment mode of joint cloud model, on this basis, proposes a deployment strategy HEELS based on the analysis of heuristic task clustering method and glowworm swarm optimization algorithm. Its main idea consists of two parts. First, the task with large resources in the current task set is filtered out by the clustering analysis, and the task offloading technology is exploited to upload the result to the cloud computing center for deployment and calculation. Then, the optimized GSO algorithm is exploited in the edge computing center, and the idea of SCA is combined into the optimization of step size so that the optimized GSO algorithm has an adaptive step size, achieving better global search ability in the early stage and better local convergence ability in the later stage. The experimental results show that compared with the existing research, HEELS realizes better load balancing effect and makes the joint datacenter more green and efficient.