A High-Efficient Joint ’Cloud-Edge’ Aware Strategy for Task Deployment and Load Balancing

Abstract

Task deployment has become a research hotspot for load balancing in joint “cloud-edge” datacenter. In view of the problem that most of the hosts are overloaded in the current joint “cloud-edge” datacenter, which may cause unbalanced load in the center, existing research mainly pay attention to the problem of unilateral load balancing of cloud computing center or edge computing center. In order to realize efficient deployment of “cloud-edge” tasks and overall load balancing, on the basis of the deployment mode of joint “cloud-edge”, this paper proposes a resource management and task deployment strategy JCETD (Joint Cloud-Edge Task Deployment) based on pruning algorithm and deep reinforcement learning. The main idea consists of two parts: firstly, the set of “cloud-edge” hosts is pruned according to the attribute value of the physical host. Then, there will be a non-dominated set of joint hosts which reduces the computational complexity of the whole algorithm and improve the computational efficiency of the system. Secondly, the problem of task deployment is simulated as a deep reinforcement learning process under the “cloud-edge” model. Through the continuous exploration and utilization of the system environment, the tasks are reasonably and efficiently deployed in the cloud computing center and edge computing center. Finally, the “cloud-edge” system can achieve an efficient computing performance and overall load balancing. The experimental results show that the proposed algorithm significantly reduces the total completion time and average response time compared with the existing research, which effectively optimizes the service ability and realizes the load balancing of the joint “cloud-edge” system.

Keywords

• Task deployment
• joint “cloud-edge”
• pruning
• deep reinforcement learning
• load balancing