|Table of Contents|

Cloud computing resource scheduling based on improved quantum particle swarm optimization algorithm

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2016年02期
Page:
223-
Research Field:
Publishing date:

Info

Title:
Cloud computing resource scheduling based on improved quantum particle swarm optimization algorithm
Author(s):
Zhao Li
College of Information Engineering,Xinyang College of Agriculture and Forestry,Xinyang 464000,China
Keywords:
cloud computing system quantum particle swarm optimization algorithm resources scheduling method resources utilization data processing swarm intelligence optimization algorithm mathematical model
PACS:
TP391
DOI:
10.14177/j.cnki.32-1397n.2016.40.02.015
Abstract:
Resources scheduling is the key technology in the application of the cloud computing system.In view of the defects of slow convergence speed and low efficiency of the standard quantum particle swarm optimization algorithm,a cloud computing resource scheduling method based on the improved quantum particle swarm optimization is proposed.A mathematical model is established based on the analysis of the present situation of cloud computing resources scheduling.The quantum particle swarm optimization algorithm is used to solve the problem in which the average optimal position is randomly disturbed to help particles escape from the local optimal solution.The experiment is used to test and analyze its performance.The results show that the proposed method can effectively improve the utilization of cloud computing resources,ensuring the load balance and good application value.

References:

[1] 林伟伟,齐德昱.云计算资源调度研究综述[J].计算机科学,2012,39(10):1-7.
Lin Weiwei,Qi Deyu.Survey of resource scheduling in cloud computing[J].Computer Science,2012,39(10):1-7.
[2]Garg S K,Yeo C S,Anandsivam A,et al.Environment-conscious scheduling of HPC applications on distributed cloud-oriented data centers[J].Journal of Parallel and Distributed Computing,2011,71(6):732-749.
[3]任崇广.面向海量数据处理领域的云计算及其关键技术研究[D].南京:南京理工大学计算机科学与工程学院,2013.
[4]刘愉,赵志文,李小兰,等.云计算环境中优化遗传算法的资源调度策略[J].北京师范大学学报(自然科学版),2012,48(4):378-384.
Liu Yu,Zhao Zhiwen,Li Xiaolan,et al.Resource scheduling strategy based optimized generic algorithm in cloud computing environment[J].Journal of Beijing Normal University(Natural Science),2012,48(4):378-384.
[5]刘永,王新华,王朕,等.节能及信任驱动的虚拟机资源调度[J].计算机应用研究,2012,29(7):2479-248.
Liu Yong,Wang Xinhua,Wang Zhen,et al.Engery-aware and trust-driven virtual machine scheduling[J].Application Research of Computers,2012,29(7):2479-248.
[6]Lee Y C,Zomaya A Y.Energy efficient utilization of resources in cloud computing systems[J].The Journal of Supercomputing,2012,60(2):268-280.
[7]Iosup A,Ostermann S,Yigitbasi M.Performance analysis of cloud computing services for many-tasks scientific computing[J].IEEE Transactions on Parallel and Distributed System,2011,22(6):931-945.
[8]孙大为,常桂然,李凤云,等.一种基于免疫克隆的偏好多维QoS云资源调度优化算法[J].电子学报,2011,39(8):1824-1831.
Sun Dawei,Chang Guiran,Li Fengyun,et al.Optimizing multi-dimensional QoS cloud resource scheduling by immune clonal with preference[J].Acta Electronica Sinica,2011,39(8):1824-1831.
[9]孟令玺,李洪亮.基于CA-PSO算法的云计算资源调度策略[J].计算机仿真,2013,30(10):406-410.
Meng Lingxi,Li Hongliang.Resource scheduling strategy in cloud computing based on cultural particle swarm optimization algorithm[J].Computer Simulation,2013,30(10):406-410.
[10]刘卫宁,靳洪兵,刘波.基于改进量子遗传算法的云计算资源调度[J].计算机应用,2013,33(8):2151-2153.
Liu Weining,Jin Hongbing,Liu Bo.Cloud computing resource scheduling based on improved quantum genetic algorithm[J].Journal of Computer Applications,2013,33(8):2151-2153.
[11]叶枫.双向收敛蚁群算法在云计算资源调度中的QoS应用[J].光电与控制,2014,21(11):93-96.
Ye Feng.Application of two-way convergence ant colony algorithm in Qos of cloud computing resource scheduling[J].Electronics Optics & Control,2014,21(11):93-96.
[12]Sun J,Fang W,Xu X J,et al.Quantum-behaved particle swarm optimization:analysis of the individual particle's behavior and parameter selection[J].Evolutionary Computation,2012,20(3):349-393.
[13]申京,吴晨光,郝洋,等.面向云计算数据中心的弹性资源调整方法[J].南京理工大学学报,2015,39(1):33-36.
Shen Jing,Wu Chenguang,Hao Yang,et al.Elastic resource adjustment method for cloud computing data center[J].Journal of Nanjing University of Science and Technology,2015,39(1):33-36.
[14]Dean J,Ghemawat S.Map/reduce:simplified data processing on large clusters[J].Communications of the ACM,2008,51(1):107-112.
[15]郑燕平.基于云计算理论的图书馆管理系统研究[D].南京:南京理工大学计算机科学与工程学院,2010.
[16]华夏渝,郑骏,胡文心.基于云计算环境的蚁群优化计算资源分配算法[J].华东师范大学学报(自然科学版),2010,11(1):127-134.
Hua Xiayu,Zheng Jun,Hu Wenxin.Ant colony optimization algorithm for computing resource allocation based on cloud computing environment[J].Journal of East China Normal University(Natural Science),2010,11(1):127-134.
[17]魏士祥.面向过程感知的云作业资源调度[D].南京:南京理工大学计算机科学与工程学院,2014.
[18]陆路.云环境下作业调度算法研究[D].南京:南京理工大学计算机科学与工程学院,2013.

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Last Update: 2016-04-30