基于粒子群优化算法的云数据均衡放置策略

doi:10.16088/j.issn.1001-6600.2021071502

广西师范大学学报（自然科学版） ›› 2022, Vol. 40 ›› Issue (3): 202-209.doi: 10.16088/j.issn.1001-6600.2021071502

• 研究论文 • 上一篇

基于粒子群优化算法的云数据均衡放置策略

郑力宁¹, 金雪松², 云利军^3*

1.云南师范大学信息学院, 云南昆明 650500;
2.玉溪市第二人民医院信息网络中心, 云南玉溪 653100;
3.云南师范大学云南省光电信息技术重点实验室, 云南昆明 650500

收稿日期:2021-07-15 修回日期:2021-09-09 出版日期:2022-05-25 发布日期:2022-05-27
通讯作者: 云利军(1973—), 男(蒙古族), 内蒙古呼和浩特人, 云南师范大学教授, 博士。E-mail: yunlijun@ynnu.edu.cn
基金资助:
云南省应用基础研究计划重点项目(2018FA033)

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

ZHENG Lining¹, JIN Xuesong², YUN Lijun^3*

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

摘要/Abstract

摘要： 在拥有多个存储节点的云数据存储系统中,保持云存储系统的负载均衡水平为一个合理的值和最小化数据检索的时间是一个值得研究的问题。本文提出一种基于粒子群优化算法的云数据均衡放置策略(balanced placement strategy of cloud data based on particle swarm optimization algorithm, BPCD),首先,给出一种云存储系统模型;其次,引入基尼系数作为衡量该系统负载均衡水平的指标,结合数据检索时间目标函数构建多目标约束优化模型;再次,采用粒子群优化算法对问题进行求解,主要包括数据节点编码与参数设置、种群初始化、粒子群空间搜索、算法迭代4个过程;最后,将本文算法与传统云数据放置算法进行对比分析。仿真实验表明,本文提出的云数据均衡放置策略在优化云存储系统的负载水平和数据检索时间方面具有良好的效果。

关键词: 云存储系统, 负载均衡, 检索时间, 多目标优化, 粒子群

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

中图分类号:

TP301

郑力宁, 金雪松, 云利军. 基于粒子群优化算法的云数据均衡放置策略[J]. 广西师范大学学报（自然科学版）, 2022, 40(3): 202-209.

ZHENG Lining, JIN Xuesong, YUN Lijun. Balanced Placement Strategy of Cloud Data Based on Particle Swarm Optimization Algorithm[J]. Journal of Guangxi Normal University(Natural Science Edition), 2022, 40(3): 202-209.

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基于粒子群优化算法的云数据均衡放置策略

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

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