Research of Cloud Computing Task Scheduling Algorithm Based on Improved Genetic Algorithm

Jun Wei Ge; Yong Sheng Yuan

doi:10.4028/www.scientific.net/AMM.347-350.2426

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 347-350Research of Cloud Computing Task Scheduling...

Research of Cloud Computing Task Scheduling Algorithm Based on Improved Genetic Algorithm

Abstract:

Use genetic algorithm for task allocation and scheduling has get more and more scholars' attention. How to reasonable use of computing resources make the total and average time of complete the task shorter and cost smaller is an important issue. The paper presents a genetic algorithm consider total task completion time, average task completion time and cost constraint. Compared with algorithm that only consider cost constraint (CGA) and adaptive algorithm that only consider total task completion time by the simulation experiment. Experimental results show that this algorithm is a more effective task scheduling algorithm in the cloud computing environment.

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Periodical:

Applied Mechanics and Materials (Volumes 347-350)

Pages:

2426-2429

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.347-350.2426

Citation:

Cite this paper

Online since:

August 2013

Authors:

Jun Wei Ge, Yong Sheng Yuan

Keywords:

Cloud Computing, Genetic Algorithm (GA), Task Scheduling

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References

[1] CHEN Quan, DENG Qianni, Cloud computing and its key techniques, Journal of Computer Applications, vol. 29, no. 9, pp.2563-2566, Sep9. (2009).

Google Scholar

[2] WU Yuqi, ZENG Guosun, ZENG Yuan, Trend Analysis for Scheduling Algorithm in Cloud Computing, Journal of Microelectronics & Computer, vol. 29, no. 9, pp.103-107, Sep9. (2012).

Google Scholar

[3] R Buyya, D Abramson, J Giddy, An economy driven resource management architecture for global computational power grids. Int, l Conf on Parallel and Distributed processing Techniques and Applications, Las Vegas, (2000).

Google Scholar

[4] XU Xiaoyong, PAN Yu, Power-aware resource scheduling under cloud computing environment, Journal of Computer Applications vol. 32, no. 7, pp.1913-1915, July1. (2012).

DOI: 10.3724/sp.j.1087.2012.01913

Google Scholar

[5] WANG Xiaoping; CAO Liming, Genetic Algorithms, Xi'anJiaotongUniversityPress, China, (2002).

Google Scholar

[6] Vincenzo Di Martino. Scheduling in a grid computing environmentusing genetic algorithms. Marco Mililotti the 16th Int'l parallel and Distributed processing Symp(I PDPS2002), Florida , USA, (2002).

DOI: 10.1109/ipdps.2002.1016654

Google Scholar

[7] Rodrigo N. Calheiros, Rajiv Ranjan, César A. F. De Rose, Rajkumar Buyya, CloudSim: A Novel Framework for Modeling and Simulation of Cloud ComputingInfrastructures and Services, (2009).

Google Scholar