Research on Edge Cloud Load Balancing Strategy Based on Chaotic Hierarchical Gene Replication
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- Zhu Leilei
- College of Computer Science and Technology, Changchun University of Science and Technology
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- Wu Zhichen
- College of Computer Science and Technology, Changchun University of Science and Technology
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- Zhao Ke
- College of Computer Science and Technology, Changchun University of Science and Technology
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- Liu Ruixiang
- College of Computer Science and Technology, Changchun University of Science and Technology
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- Liu Dan
- College of Computer Science and Technology, Changchun University of Science and Technology
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- Su Wei
- College of Medical Information, Changchun University of Chinese Medicine
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- Li Li
- College of Computer Science and Technology, Changchun University of Science and Technology
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抄録
<p>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.</p>
収録刊行物
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- Journal of Advanced Computational Intelligence and Intelligent Informatics
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Journal of Advanced Computational Intelligence and Intelligent Informatics 26 (5), 758-767, 2022-09-20
富士技術出版株式会社
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詳細情報 詳細情報について
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- CRID
- 1390293478734368128
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- NII書誌ID
- AA12042502
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- ISSN
- 18838014
- 13430130
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- NDL書誌ID
- 032391333
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- 本文言語コード
- en
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- データソース種別
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- JaLC
- NDL
- Crossref
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- 抄録ライセンスフラグ
- 使用不可