AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
Powered by AMiner. Clicking this button will take you away from SciOpen.
Home Tsinghua Science and Technology Article
PDF (4.6 MB)
Cite
EndNote(RIS) BibTeX
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Virtual Machine-Based Task Scheduling Algorithm in a Cloud Computing Environment

Zhifeng Zhong( ), Kun Chen, Xiaojun Zhai, Shuange Zhou
College of Computer and Information Engineering, Hubei University, Wuhan 430062, China.
Department of Electronics, Computing and Mathematics, University of Derby, Derby DE22 1GB, UK.
Show Author Information

Abstract

Virtualization technology has been widely used to virtualize single server into multiple servers, which not only creates an operating environment for a virtual machine-based cloud computing platform but also potentially improves its efficiency. Currently, most task scheduling-based algorithms used in cloud computing environments are slow to convergence or easily fall into a local optimum. This paper introduces a Greedy Particle Swarm Optimization (G&PSO) based algorithm to solve the task scheduling problem. It uses a greedy algorithm to quickly solve the initial particle value of a particle swarm optimization algorithm derived from a virtual machine-based cloud platform. The archived experimental results show that the algorithm exhibits better performance such as a faster convergence rate, stronger local and global search capabilities, and a more balanced workload on each virtual machine. Therefore, the G&PSO algorithm demonstrates improved virtual machine efficiency and resource utilization compared with the traditional particle swarm optimization algorithm.

Keywords

cloud computingvirtual machineG&PSO algorithm

References

[1]
Armbrust M., Fox A., Griffith R., Joseph A. D., Katz R., Konwinski A., Lee G., Patterson D., Rabkin A., Stoica I., et al., Above the clouds: A Berkeley view of cloud computing, Technical Report No. UCB/EECS-2009-28, University of California at Berkeley, USA, 2009.
[2]
Matsunaga A., Tsugawa M., and Fortes J., CloudBLAST: Combining MapReduce and virtualization on distributed resources for bioinformatics applications, in IEEE Fourth International Conference on Escience, 2008, pp. 222-229.
Crossref
[3]
Uhlig R., Neiger G., Rodgers D., Santoni A. L., Martins F. C. M., Anderson A. V., Bennett S. M., Kagi A., Leung F. H., and Smith L., Intel virtualization technology, Computer, vol. 38, no. 5, pp. 48-56, 2005.
Crossref Google Scholar
[4]
Smith J. E. and Nair R., The architecture of virtual machines, Computer, vol. 38, no. 5, pp. 32-38, 2005.
Crossref Google Scholar
[5]
Shi L. X., Utility maximization model of virtual machine scheduling in cloud environment (in Chinese), Journal of Computers, vol. 36, no. 2, pp. 252-262, 2013.
Crossref Google Scholar
[6]
Daniels J., Server virtualization architecture and implementation, Crossroads, vol. 16, pp. 8-12, 2009.
Crossref Google Scholar
[7]
Liu H., A measurement study of server utilization in public clouds, in IEEE Ninth International Conference on Dependable, Autonomic and Secure Computing, 2011, pp. 435-442.
Crossref
[8]
González-Vélez H. and Kontagora M., Performance evaluation of MapReduce using full virtualisation on a departmental cloud, International Journal of Applied Mathematics & Computer Science, vol. 21, no. 2, pp. 275-284, 2011.
Crossref Google Scholar
[9]
Dong Y. and Zhou Z., X86-based system virtual machine development and application, Computer Engineering, vol. 32, no. 13, pp. 71-73, 2006.
Google Scholar
[10]
Sotomayor B., Montero S. R., and Foster I., Virtual infrastructure management in private and hybrid clouds, IEEE Internet Computing, vol. 13, no. 5, pp. 14-22, 2009.
Crossref Google Scholar
[11]
Kang Q., He H., and Wei J.. An effective iterated greedy algorithm for reliability-oriented task allocation in distributed computing systems, Journal of Parallel & Distributed Computing, vol. 73, no. 8, pp. 1106-1115, 2013.
Crossref Google Scholar
[12]
Kaur S. and Verma A., An efficient approach to genetic algorithm for task scheduling in cloud computing environment, International Journal of Information Technology & Computer Science, vol. 4, no. 10, pp. 74-79, 2012.
Crossref Google Scholar
[13]
Zhang Y., Fang I. L., and Zhou T., Task scheduling algorithm based on genetic ant colony algorithm in cloud computing environment, (in Chinese), ComputerEngineering & Applications, vol. 50, no. 6, pp. 51-55, 2014.
Google Scholar
[14]
Hu D., Hu J., and Yu X.. Virtual machine task scheduling algorithm based on pso in cloud computing environment (in Chinese), Computer Measurement & Control, vol. 22, no. 4, pp. 1189-1192, 2014.
Google Scholar
[15]
Jiang M., Luo Y. P., and Yang S. Y., Stochastic convergence analysis and parameter selection of the standard particle swarm optimization algorithm, Information Processing Letters, vol. 102, no. 1, pp. 8-16, 2007.
Crossref Google Scholar
[16]
Liu D., Tan K. C., Goh C. K., and Ho W. K., A multi-objective memetic algorithm based on particle swarm optimization, IEEE Transactions on Systems Man & Cybernetics—Part B Cybernetics, vol. 37, no. 1, pp. 42-50, 2007.
Crossref Google Scholar
[17]
Liu C. Y., Zou C. M., and Wu P., A task scheduling algorithm based on genetic algorithm and ant colony optimization in cloud computing, in Proc.13th Int.Distributed Computing and Applications to Business, Engineering and Science (DCABES), International Symposium on IEEE, 2014, pp. 68-72.
Crossref
[18]
Li K., Xu G., Zhao G., Dong Y., and Wang D., Cloud task scheduling based on load balancing ant colony optimization, in 2011 6th Annual ChinaGrid Conference, 2011.
Crossref
[19]
Wang P., Research on task scheduling strategy in cloud computing environment, (in Chinese), Computer & Modernization, no. 7, pp. 22-25, 2013.
Google Scholar
[20]
Stillwell M., Vivien F., and Casanova H., Virtual machine resource allocation for service hosting on heterogeneous distributed platforms, IEEE International Parallel & Distributed Processing Symposium, vol. 19, pp. 786-797, 2012.
Crossref Google Scholar
[21]
Ali S., Siegel H. J., Maheswarand M., Hensgen D., and Ali S., Representing task and machine heterogeneities for heterogeneous computing systems, Tamkang Journal of Science & Engineering, vol. 3, no. 3, pp. 19-25, 2003.
Google Scholar
[22]
C.Trelea I., The particle swarm optimization algorithm: Convergence analysis and parameter selection, Information Processing Letters, vol. 85, no. 6, pp. 317-325, 2003.
Crossref Google Scholar
[23]
Yi S., Kondo D., and Andrzejak A., Reducing costs of spot instances via checkpointing in the amazon elastic compute cloud, in IEEE Int. Cloud Computing Conf., 2010, pp. 236-243.
Crossref
[24]
Kansal N. J. and Chana I., Cloud load balancing techniques: A step towards green computing, International Journal of Computer Science Issues, vol. 9, no. 1, pp. 238-246, 2012.
Google Scholar
[25]
Li X., Better spread and convergence: Particle swarm multiobjective optimization using the maximin fitness function, Lecture Notes in Computer Science, vol. 3102, pp. 117-128, 2004.
Crossref Google Scholar
[26]
Hayes B., Cloud computing, Communications of the ACM, vol. 51, no. 1, pp. 47-68, 2008.
Crossref Google Scholar
[27]
Calheiros R. N., Ranjan R., Beloglazov A., De Rose C. A. F., and Buyya R., CloudSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms, Software Practice & Experience, vol. 41, no. 1, pp. 23-50, 2011.
Crossref Google Scholar
Tsinghua Science and Technology
Volume 21 Issue 6,
December 2016
Pages 660-667
DOI: 10.1109/TST.2016.7787008
Cite this article:
Zhong Z, Chen K, Zhai X, et al. Virtual Machine-Based Task Scheduling Algorithm in a Cloud Computing Environment. Tsinghua Science and Technology, 2016, 21(6): 660-667. https://doi.org/10.1109/TST.2016.7787008

809

Views

35

Downloads

50

Crossref

N/A

Web of Science

60

Scopus

2

CSCD

Altmetrics
Received: 14 July 2016
Revised: 18 August 2016
Accepted: 03 October 2016
Published: 19 December 2016
© The author(s) 2016
Return