基于群体智能算法的大数据迁移策略研究
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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.
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.
引文
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[16]CHEN F,WANG H,YONG X,et al.An ACO-based online routing method for multiple order pickers with congestion consideration in warehouse[J].Journal of Intelligent Manufacturing,2016,27(2):389-408.
[17]WU S,HONG J,BO M.Proactive Data Migration for Improved Storage Availability in Large-Scale Data Centers[J].IEEE Transactions on Computers,2015,64(9):2637-2651.
[2]YOU C,HUANG K,CHAE H.Energy Efficient Mobile Cloud Computing Powered by Wireless Energy Transfer[J].IEEE Journal on Selected Areas in Communications,2016:1-1.
[3]CHANG V,RAMACHANDRAN M.Towards achieving Data Security with the Cloud Computing Adoption Framework[J].IEEE Transactions on Services Computing,2016,9(1):138-151.
[4]CHEN X,JIAO L,LI W,et al.Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing[J].IEEE/ACM Transactions on Networking,2015,24(5):2795-2808.
[5]ZHANG H,JIANG H,LI B,et al.A Framework for Truthful Online Auctions in Cloud Computing with Heterogeneous User Demands[J].IEEE Transactions on Computers,2016,65(3):805-818.
[6]ZHANG W,LIN Y,XIAO S,et al.Privacy Preserving Ranked Multi-Keyword Search for Multiple Data Owners in Cloud Computing[J].IEEE Transactions on Computers,2015,65(5):1-1.
[7]黄冬梅,杜艳玲,贺琪.混合云存储中海洋大数据迁移算法的研究[J].计算机研究与发展,2014,51(1):199-205.
[8]张晋芳,王清心,丁家满,等.一种云计算环境下大数据动态迁移策略[J].计算机工程,2016,42(5):13-17.
[9]SANCHEZ V M,CHAVEZ-RAMIREZ A U,DURON-TORRES S M,et al.Techno-economical optimization based on swarm intelligence algorithm for a stand-alone wind-photovoltaic-hydrogen power system at south-east region of Mexico[J].International Journal of Hydrogen Energy,2014,39(29):16646-16655.
[10]LAZZU'S J A,RIVERA M,LPEZ-CARABALLO C H.Parameter estimation of Lorenz chaotic system using a hybrid swarm intelligence algorithm[J].Physics Letters A,2016,380(11/12):1164-1171.
[11]CHAVES-GONZLEZ J M,PREZ-TOLEDANO M A,NAVASA A.Software requirement optimization using a multiobjective swarm intelligence evolutionary algorithm[J].Knowledge-Based Systems,2015,83(1):105-115.
[12]NESHAT M,SEPIDNAM G,SARGOLZAEI M,et al.Artificial fish swarm algorithm:a survey of the state-of-theart,hybridization,combinatorial and indicative applications[J].Artificial Intelligence Review,2014,42(4):965-997.
[13]AZAD M A K,ROCHA A M A C,FERNANDES E M GP.A simplified binary artificial fish swarm algorithm for0-1 quadratic knapsack problems[J].Journal of Computational&Applied Mathematics,2014,259(4):897-904.
[14]BOOTHBY T,KING A D,ROY A.Fast clique minor generation in Chimera qubit connectivity graphs[J].Quantum Information Processing,2016,15(1):495-508.
[15]SENGOTTUVELAN P,PRASATH N.BAFSA:Breeding Artificial Fish Swarm Algorithm for Optimal Cluster Head Selection in Wireless Sensor Networks[J].Wireless Personal Communications,2016,94(4):1-13.
[16]CHEN F,WANG H,YONG X,et al.An ACO-based online routing method for multiple order pickers with congestion consideration in warehouse[J].Journal of Intelligent Manufacturing,2016,27(2):389-408.
[17]WU S,HONG J,BO M.Proactive Data Migration for Improved Storage Availability in Large-Scale Data Centers[J].IEEE Transactions on Computers,2015,64(9):2637-2651.
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