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 JPEE  Vol.3 No.4 , April 2015
The Application of IHA in Grid Cloud Computing Task Decomposition and Scheduling Based on Bionics
Abstract: Based on the large amount and variations of the power grid task as well as its requirement of real- time performance and economic benefit, we make a further improvement and expansion of IHA (Improved Heuristic Algorithm) on the combination of bionics in genetic engineering and evolution to solve the decomposing and scheduling problems. Firstly, we transform those complex decomposing problems into the operational optimal solution problem by IHA to decrease the rate of running into the local optimal solution [1]. In task scheduling, we classify the sub-tasks by the emergency degree for resource allocation, which not only largely reduces the calculation and resource cost but also improves working efficiency and the speed of execution [2]. Finally, we select optimal scheduling scheme by the Fitness function defined about time and cost.
Cite this paper: Shi, K. (2015) The Application of IHA in Grid Cloud Computing Task Decomposition and Scheduling Based on Bionics. Journal of Power and Energy Engineering, 3, 467-469. doi: 10.4236/jpee.2015.34064.
References

[1]   Su, X.H. and Zhang, H.L. (2012) Improvement of Cloud Computing Task Decomposition Algorithm. Electronic Design Engineering, 23, 47-49.

[2]   Li, J., Li, P.-W., Li, Y.-K., Yu, J.-G. and Shao, Z.X. (2012) Cloud Computing in the Application of the Smart Grid Research. Mathematics in Practice and Theory, 13, 123-129.

[3]   Zhu, Y.H. (2013) Task Scheduling Research of Differential Evolution Algorithm and Its Application in Cloud Computing. Lanzhou Jiaotong University.

[4]   Yu, H.F. (2011) Scheduling Algorithm Research of Cloud Computing Based on Effect Function. Science and Technology Information, 3, 35+47.

[5]   Kang, Y.M. and Hu, J. (1997) Task Decomposition, Task Scheduling and Parallel Algorithm Design. Computer Development & Applications, 3, 42-44.

 
 
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