Research on Scheduling Model in Dynamic Environment Based on Computational Economy

Author:

Duan Li Jun1

Affiliation:

1. Hubei University of Education

Abstract

Efficient resource scheduling in dynamic environment reveals several challenges due to its high heterogeneity, dynamic behavior, and space shared utilization. In order to address this issue, a scheduling model based on computational economy was proposed in this paper. Firstly, the problem of resource scheduling was analyzed, and the essence of resource scheduling was concluded. Secondly, a scheduling strategy based on computational economy was presented. It applied the principles of economics and broker technology in its resource scheduling process. The strategy of the model synthetically considered two factors: execution cost and total complete time. An evaluation function driven by user’s needs was also built based on the two factors. Finally, the scheduling strategy was simulated. The result shows that the model can adjust the relation between price and time based on computational economy efficiently. It indicates that the model is an effective approach for resource scheduling.

Publisher

Trans Tech Publications, Ltd.

Reference8 articles.

1. Abramson D, Buyya R, and Giddy J: A Computational economy for grid computing and its Implementation in the Nimrod-G resource broker, Future Generation Computer Systems, 18(8), p.1061–1074, (2002).

2. Abhishek G., Laxmikant V., Chun H. S., et al: Exploring the performance and mapping of HPC applications to platforms in the cloud. Proc. of HPDC '12, ACM New York, NY, USA, p.121–122, (2012).

3. Gu J.H., Hu J.H., Zhao J.H. et al: A New Resource Scheduling Strategy Based on Genetic Algorithm in Cloud Computing Environment, Journal of Computers, 7(1), p.42–52, (2012).

4. Huang F.X., Jiang X.N., Li Z.J., et al: Task scheduling model based on pruning of permutation tree in economics grid, Computer Engineering, 34(4), p.73–74, (2008).

5. Liu C: Optimal Multi-Resource Scheduling Strategy Simulation Based on Improved Genetic Algorithm, TELKOMNIKA Indonesian Journal of Electrical Engineering, 12(4), p.2898–2904, (2014).

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