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.
Highlights
Joint ‘‘cloud-edge’’ computing is a research direction with particular prospects after distributed computing, cloud computing and edge computing [1], It is a hot topic in current research
In order to achieve efficient computing ability and load balancing in the joint ‘‘cloud-edge’’ system environment, this paper proposes a resource management and task deployment strategy based on pruning algorithm [2] and deep reinforcement learning
WORK Based on the joint ‘‘cloud-edge’’ computing, this paper proposes a resource management and task deployment strategy JCETD based on pruning algorithm and deep reinforcement learning, and gives its main ideas, process implementation and evaluation
Summary
Joint ‘‘cloud-edge’’ computing is a research direction with particular prospects after distributed computing, cloud computing and edge computing [1], It is a hot topic in current research. It will make subsequent tasks unable to be deployed effectively, and make the load of the ‘‘cloudedge’’ datacenter unbalanced It fails to provide users with real-time calculation results and fails to show the advantages of joint ‘‘cloud-edge’’ computing. In order to achieve efficient computing ability and load balancing in the joint ‘‘cloud-edge’’ system environment, this paper proposes a resource management and task deployment strategy based on pruning algorithm [2] and deep reinforcement learning. On the basis of the joint ‘‘cloud-edge’’ architecture, the deep reinforcement learning algorithm is used to realize the task efficient deployment and long-term load balancing of the ‘‘cloud-edge’’ system.
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