Author:
Zhu Leilei,Wu Zhichen,Zhao Ke,Liu Ruixiang,Liu Dan,Su Wei,Li Li, ,
Abstract
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.
Funder
Education Department of Jilin Province
Education Science of Jilin Province
People's Government of Jilin Province
Publisher
Fuji Technology Press Ltd.
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Human-Computer Interaction
Reference22 articles.
1. J. Pan and J. Mcelhannon, “Future Edge Cloud and Edge Computing for Internet of Things Applications,” IEEE Internet of Things J., Vol.5, No.1, pp. 439-449, 2017.
2. S. K. Sharma and X. Wang, “Live Data Analytics with Collaborative Edge and Cloud Processing in Wireless IoT Networks,” IEEE Access, Vol.5, pp. 4621-4635, 2017.
3. X.-L. Zhang, J.-H. Yang, X.-Q. Sun et al., “Survey of geo-distributed cloud research progress,” J. Softw., Vol.29, No.7, pp. 2116-2132, doi: 10.13328/j.cnki.jos.005555, 2018.
4. C. Li, C. Wang, and Y. Luo, “An efficient scheduling optimization strategy for improving consistency maintenance in edge cloud environment,” The J. of Supercomputing, Vol.76, pp. 6941-6968, 2020.
5. Z. Zhou, F. Li, H. Zhu et al., “An improved genetic algorithm using greedy strategy toward task scheduling optimization in cloud environments,” Neural Comput. & Applic., Vol.32, pp. 1531-1541, 2020.