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

Paper:

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

Received:
February 14, 2022
Accepted:
May 21, 2022
Published:
September 20, 2022
Keywords:
edge cloud, resource allocation, chaotic theory, replication ratio, Kubernetes
Abstract
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.
Data files:
References
  1. [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. [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. [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. [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. [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.
  6. [6] M. Abdullahi, M. A. Ngadi, S. I. Dishing et al., “An efficient symbiotic organisms search algorithm with chaotic optimization strategy for multi-objective task scheduling problems in cloud computing environment,” J. of Network & Computer Applications, Vol.133, pp. 60-74, 2019.
  7. [7] T. Zhao, S. Zhou, X. Guo et al., “A cooperativescheduling scheme of local cloud and Internet cloud for delay-aware mobile cloud computing,” Proc. IEEE Globecom, doi: 10.1109/GLOCOMW.2015.7414063, 2015.
  8. [8] Y. Ma, Y. Han, J. Wang et al., “A Constrained Static Scheduling Strategy in Edge Computing for Industrial Cloud Systems,” Int. J. of Information Technologies and Systems Approach, Vol.14, No.1, pp. 33-61, 2021.
  9. [9] B. Lin, C. Lin, X. Chen et al., “A Fuzzy Scheduling Strategy for Workflow Decision Making in Uncertain Edge-Cloud Environments,” arXiv:2107.01405, 2021.
  10. [10] L. Bo, X. Huang, L. Niu et al., “Task Offloading Decision in Vehicle Edge Computing Environment,” Microelectronics & Computer, Vol.36, No.2, pp. 78-82, 2019.
  11. [11] C. You, Y. Zeng, R. Zhang et al., “Asynchronous Mobile-Edge Computation Offloading: Energy-Efficient Resource Management,” IEEE Trans. on Wireless Communications, Vol.17, Issue 11, pp. 7590-7605, 2018.
  12. [12] Y. Mao, J. Zhang, and K. B. Letaief, “Dynamic Computation Offloading for Mobile-Edge Computing with Energy Harvesting Devices,” IEEE J. on Selected Areas in Communications, Vol.34, Issue 12, pp. 3590-3605, 2016.
  13. [13] X. Wang, H. Gu, and Y.-X. Yue, “The optimization of virtual resource allocation in cloud computing based on RBPSO,” Concurrency and Computation Practice and Experience, Vol.32, Issue 16, Article No.e5113, 2018.
  14. [14] F. Qian, Y. Fan, H. Liu et al., “Study on Reactive Power Optimization of Adaptive Particle Swarm Based on Logistic Chaotic Mapping,” Power Capacitor & Reactive Power Compensation, Vol.38, No.4, pp. 146-151, 2017.
  15. [15] Y. Zhang and F. Wang, “Lighting control optimization method based on improved sparrow search algorithm,” J. of Computer Application, doi: 10.11772/j.issn.1001-9081.2022010031, 2022.
  16. [16] M. Anantathanavit and M. Munlin, “Radius Particle Swarm Optimization,” Int. Computer Science and Engineering Conf. (ICSEC), pp. 126-130, doi: 10.1109/ICSEC.2013.6694765, 2013.
  17. [17] Y. Xie, Y. Zhu, Y. Wang et al., “A novel directional and non-local-convergent particle swarm optimization based workflow scheduling in cloud-edge environment,” Future Generation Computer Systems, Vol.97, pp. 361-378, 2019.
  18. [18] C. Jian, M. Li, and X. Kuang, “Edge cloud computing service composition based on modified bird swarm optimization in the internet of things,” Cluster Computing, Vol.22, pp. 8079-8087, 2018.
  19. [19] G. I. Sayed, G. Khoriba, and M. Haggag, “A novel chaotic salp swarm algorithm for global optimization and feature selection,” Applied Intelligence, Vol.48, pp. 3462-3481, 2018.
  20. [20] A. Dalvandi, M. Gurusamy, and K. C. Chua, “Application Scheduling, Placement, and Routing for Power Efficiency in Cloud Data Centers,” IEEE Trans. on Parallel & Distributed Systems, Vol.28, Issue 4, pp. 947-960, 2017.
  21. [21] F. Ahamed, S. Shahrestani, and B. Javadi, “Security aware and energy-efficient virtual machine consolidation in cloud computing systems,” IEEE Trustcom/BigDataSE/ISPA, pp. 1516-1523, 2016.
  22. [22] T. Wen, G.-J. Sheng, G. Quan et al., “Web Service Composition Based on Modified Particle Swarm Optimization,” Chinese J. of Computers, Vol.36, No.5, pp. 1031-1046, 2013 (in Chinese).

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Last updated on Sep. 22, 2022