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A High-Efficient Joint 'Cloud-Edge' Aware Strategy for Task Deployment and Load Balancing
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DOI：10.1109/ACCESS.2021.3051672.png)
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Abstract
En 中文
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 analysis
Cloud computing
Load management
Reinforcement learning
Scheduling
Edge computing
Computational modeling
Task deployment
joint cloud-edge
pruning
deep reinforcement learning
load balancing
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