摘要
针对云数据中心不同于传统的数据中心,其管理和维护需要解决更加复杂的问题的情况,为实现云计算平台中大数据系统的平稳升级和更新,提出了一种基于群体智能算法的大数据迁移策略,解决了负载平衡和带宽瓶颈问题。首先对云计算体系架构上的大数据迁移技术进行研究和分析,然后采用人工鱼群优化算法解决m个服务器之间n个数据迁移的最优解问题。最后,将量子比特引入到人工鱼群算法中实现其三大基本行为。Cloudsim仿真平台上的测试结果表明:相比其他迁移策略,所提出算法能更有效地提高云数据中心的运行效率,具有更好的全局寻优能力。
Unlike traditional data centers,the management and maintenance of cloud data centers requires solving more complex problems.In order to realize the smooth upgrade and update of big data system in cloud computing platform,a big data migration strategy based on swarm intelligence algorithm is proposed,which effectively solves the problem of load balancing and bandwidth bottleneck.Firstly,the research and analysis of big data migration technology on cloud computing architecture was carried out.Then the artificial fish swarm optimization algorithm was used to solve the optimal solution problem of n data migration between m servers.Finally,the quantum bits were introduced into the artificial fish swarm algorithm to achieve their three basic behaviors.The test results on the Cloudsim simulation platform show that compared with other migration strategies,the proposed algorithm can improve the efficiency of the cloud data center more effectively and has better global optimization ability.
作者
曾毅
马琳娟
鱼明
ZENG Yi;MA Linjuan;YU Ming(Guangxi University Xingjian College of Science and Liberal Arts Computer andInformation Engineering Department,Nanning 530005,China;School of Computer Science & Technology,Beijing 100081,China;School of Economics and Management,Shihezi University,Shihezi 832000,China)
出处
《重庆理工大学学报（自然科学）》
CAS
北大核心
2019年第6期122-127,共6页
Journal of Chongqing University of Technology：Natural Science
基金
福建省科技厅引导性项目(2018H0028)
广西壮族自治区教育厅2019年度广西高校中青年教师科研基础能力提升项目(2019KY0960)
关键词
群体智能
量子人工鱼群
云计算
数据迁移
全局寻优
负载平衡
group intelligence
quantum artificial fish school
cloud computing
data migration
global optimization
load balancing
