Article

A task scheduling algorithm based on load balancing in cloud computing

Authors:

Yiqiu Fang,

Fei Wang,

Junwei GeAuthors Info & Claims

WISM'10: Proceedings of the 2010 international conference on Web information systems and mining

Pages 271 - 277

Published: 23 October 2010 Publication History

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Abstract

Efficient task scheduling mechanism can meet users' requirements, and improve the resource utilization, thereby enhancing the overall performance of the cloud computing environment. But the task scheduling in grid computing is often about the static task requirements, and the resources utilization rate is also low. According to the new features of cloud computing, such as flexibility, virtualization and etc, this paper discusses a two levels task scheduling mechanism based on load balancing in cloud computing. This task scheduling mechanism can not only meet user's requirements, but also get high resource utilization, which was proved by the simulation results in the CloudSim toolkit.

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Cited By

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Sahraei SKashani MRezazadeh JFarahbakhsh R(2019)Efficient job scheduling in cloud computing based on genetic algorithmInternational Journal of Communication Networks and Distributed Systems10.1504/ijcnds.2019.09996822:4(447-467)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1504/ijcnds.2019.099968
Phani Praveen S. Rao K(2018)Client-Awareness Resource Allotment and Job Scheduling in Heterogeneous Cloud by Using Social Group OptimizationInternational Journal of Natural Computing Research10.4018/IJNCR.20180101027:1(15-31)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.4018/IJNCR.2018010102
Kang BChoo H(2018)An SDN-enhanced load-balancing technique in the cloud systemThe Journal of Supercomputing10.1007/s11227-016-1936-z74:11(5706-5729)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.1007/s11227-016-1936-z
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Information

Published In

WISM'10: Proceedings of the 2010 international conference on Web information systems and mining

October 2010

441 pages

ISBN:3642165141

Editors:
Fu Lee Wang
Caritas Francis Hsu College, Department of Business Administration, Hong Kong, China
,
Zhiguo Gong
University of Macau, Department of Computer and Information Science, Taipa, Macau, SAR China
,
Xiangfeng Luo
Shanghai University, School of Computer, Shanghai, China
,
Jingsheng Lei
Nanjing University of Posts and Telecommunications, School of Computer, Nanjing, China

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 23 October 2010

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Sahraei SKashani MRezazadeh JFarahbakhsh R(2019)Efficient job scheduling in cloud computing based on genetic algorithmInternational Journal of Communication Networks and Distributed Systems10.1504/ijcnds.2019.09996822:4(447-467)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1504/ijcnds.2019.099968
Phani Praveen S. Rao K(2018)Client-Awareness Resource Allotment and Job Scheduling in Heterogeneous Cloud by Using Social Group OptimizationInternational Journal of Natural Computing Research10.4018/IJNCR.20180101027:1(15-31)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.4018/IJNCR.2018010102
Kang BChoo H(2018)An SDN-enhanced load-balancing technique in the cloud systemThe Journal of Supercomputing10.1007/s11227-016-1936-z74:11(5706-5729)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.1007/s11227-016-1936-z
Elmougy SSarhan SJoundy M(2017)A novel hybrid of Shortest job first and round Robin with dynamic variable quantum time task scheduling techniqueJournal of Cloud Computing: Advances, Systems and Applications10.1186/s13677-017-0085-06:1(1-12)Online publication date: 1-Dec-2017
https://dl.acm.org/doi/10.1186/s13677-017-0085-0
Zhu WJiang HZhou SAddison M(2017)An optimal resources scheduling strategy on multimedia cloud computing under multi- device constraintMultimedia Tools and Applications10.1007/s11042-015-3140-176:19(19429-19444)Online publication date: 1-Oct-2017
https://dl.acm.org/doi/10.1007/s11042-015-3140-1
Mansouri YBuyya R(2016)To move or not to moveJournal of Network and Computer Applications10.1016/j.jnca.2016.08.02975:C(223-235)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1016/j.jnca.2016.08.029
Naha ROthman M(2016)Cost-aware service brokering and performance sentient load balancing algorithms in the cloudJournal of Network and Computer Applications10.1016/j.jnca.2016.08.01875:C(47-57)Online publication date: 1-Nov-2016
https://dl.acm.org/doi/10.1016/j.jnca.2016.08.018
Milani ANavimipour N(2016)Load balancing mechanisms and techniques in the cloud environmentsJournal of Network and Computer Applications10.1016/j.jnca.2016.06.00371:C(86-98)Online publication date: 1-Aug-2016
https://dl.acm.org/doi/10.1016/j.jnca.2016.06.003
Chen ZZhu YDi YFeng S(2015)A dynamic resource scheduling method based on fuzzy control theory in cloud environmentJournal of Control Science and Engineering10.1155/2015/3832092015(34-34)Online publication date: 1-Jan-2015
https://dl.acm.org/doi/10.1155/2015/383209
Mertzios GShalom MVoloshin AWong PZaks S(2015)Optimizing busy time on parallel machinesTheoretical Computer Science10.1016/j.tcs.2014.10.033562:C(524-541)Online publication date: 11-Jan-2015
https://dl.acm.org/doi/10.1016/j.tcs.2014.10.033
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Load balancing in cloud computing: A big picture

User Task Priority Based Resource Allocation with Multi Class Task Scheduling Strategy and Load Balancing in Cloud Environment

Load balancing in cloud computing using worst-fit bin-stretching

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