Journal of Guangxi Normal University(Natural Science Edition) ›› 2022, Vol. 40 ›› Issue (3): 202-209.doi: 10.16088/j.issn.1001-6600.2021071502

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Balanced Placement Strategy of Cloud Data Based on Particle Swarm Optimization Algorithm

ZHENG Lining1, JIN Xuesong2, YUN Lijun3*   

  1. 1. School of Information, Yunnan Normal University, Kunming Yunnan 650500, China;
    2. Information Network Center, Second People’s Hospital of Yuxi, Yuxi Yunnan 653100, China;
    3. Key Laboratory of Optoelectronic Information Technology, Yunnan Normal University, Kunming Yunnan 650500, China
  • Received:2021-07-15 Revised:2021-09-09 Online:2022-05-25 Published:2022-05-27

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Abstract: In the cloud data storage system with multiple storage nodes, how to keep the load balance level of the cloud storage system to a reasonable value and minimize the time of data retrieval is a problem worth studying. To solve this problem, this paper proposes a balanced placement strategy of cloud data based on particle swarm optimization algorithm (BPCD). Firstly, a cloud storage system model is presented. Secondly, the Gini coefficient is introduced as the index to measure the load balancing level of the system, and a multi-objective constrained optimization model is constructed by combining the objective function of data retrieval time. Thirdly, the particle swarm optimization algorithm is used to solve the problem, which mainly includes four processes: data node coding and parameter setting, population initialization, particle swarm spatial search and algorithm iteration. Finally, the proposed algorithm is compared with the traditional cloud data placement algorithm. Simulation experiments show that the proposed cloud data balanced placement strategy has good effects in optimizing the load level and data retrieval time of the cloud storage system.

Key words: cloud storage system, load balancing, retrieval time, multi-objective optimization, particle swarm

CLC Number: 

  • TP301
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