JACIII Vol.26 No.5 pp. 758-767
doi: 10.20965/jaciii.2022.p0758


Research on Edge Cloud Load Balancing Strategy Based on Chaotic Hierarchical Gene Replication

Leilei Zhu*, Zhichen Wu*, Ke Zhao*, Ruixiang Liu*, Dan Liu*, Wei Su**, and Li Li*

*College of Computer Science and Technology, Changchun University of Science and Technology
7186 Weixing Road, Changchun, Jilin 130022, China

**College of Medical Information, Changchun University of Chinese Medicine
1035 Boshuo Road, Jingyue National High-tech Industrial Development Zone, Nanguan, Changchun, Jilin 130117, China

February 14, 2022
May 21, 2022
September 20, 2022
edge cloud, resource allocation, chaotic theory, replication ratio, Kubernetes
Research on Edge Cloud Load Balancing Strategy Based on Chaotic Hierarchical Gene Replication

Experimental results of the CRPSO model

Edge cloud is used to handle latency-sensitive services. However, due to the large number of concurrent requests for edge intensive tasks, the resource allocation strategy affects the stability of nodes. In addition to an adaptive resource allocation model based on chaotic hierarchical gene replication (CRPSO model), the concept of chaotic replication ratio is proposed. This study is divided into two parts. The first is to verify the algorithm verification of the simulation platform. By comparison, it is found that CRPSO reduces the CPU and bandwidth utilization by 43.7% and 62.7% on average, respectively, and the memory usage is also lower than other algorithms. Thereafter, we compared the CRPSO algorithm with the Kubernetes clustering algorithm. Experiments showed that the fitness of the CRPSO model is 33.7% higher than that of the comparison algorithm on average. The algorithm is superior to the cluster scheduling algorithm in terms of CPU utilization and memory utilization. Furthermore, the total variance of the two resources involved in this model improved significantly, reaching 69.8% on average. In addition, CRPSO also has great advantages in other aspects of CPU and memory. It is indicated that the model in this study is suitable for the scenario of edge large-scale requests.

Cite this article as:
L. Zhu, Z. Wu, K. Zhao, R. Liu, D. Liu, W. Su, and L. Li, “Research on Edge Cloud Load Balancing Strategy Based on Chaotic Hierarchical Gene Replication,” J. Adv. Comput. Intell. Intell. Inform., Vol.26, No.5, pp. 758-767, 2022.
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Last updated on Sep. 22, 2022