Improved Grid Task Scheduling Model Algorithm

doi:10.23940/ijpe.19.10.p24.27722782

Int J Performability Eng ›› 2019, Vol. 15 ›› Issue (10): 2772-2782.doi: 10.23940/ijpe.19.10.p24.27722782

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Improved Grid Task Scheduling Model Algorithm

Feng Liu^*

Department of Electrical and Information Engineering, Heilongjiang University of Technology, Jixi, 158100, China

Submitted on ; Revised on ; Accepted on
Contact: Liu Feng
About author:
* Corresponding author. E-mail address: 3140025490@qq.com
Supported by:
This work was supported by the project of the Nature Scientific Foundation of Heilongjiang Province (No F2016038)

Abstract

Abstract:

On the basis of analyzing the current status and the key technology of grid workflow scheduling, in-depth research on the grid workflow scheduling algorithm under the restraint of time QOS and trust QOS is conducted in this paper. A grid workflow task scheduling algorithm (GWTS) based on critical tasks under the constraints of trust is designed. Firstly, backward depth of tasks is calculated in GWTS, and critical tasks are ascertained according to the execution time on candidate resources. Secondly, the trust of grid resources is computed based on direct experience and recommendation experience synthetically. Finally, tasks are scheduled by decreasing backward depth, and resources are closed to meet the integrated function of execution time and trust and are allocated for critical tasks as a priority. Experiments show that the workflow completion time is reduced, the success rate of task execution is increased by 6-15%, and the GWTS algorithm can effectively guarantee grid scheduling resource optimization and improve the scheduling efficiency.

Key words: grid, workflow scheduling, trust QOS

Feng Liu. Improved Grid Task Scheduling Model Algorithm [J]. Int J Performability Eng, 2019, 15(10): 2772-2782.

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References 14.

1.	X. Li, Z. G. Hu,Z. K. Yan, “Research and Development of Grid Workflow Scheduling Algorithm based on Deadline Satisfaction,”Computer Research and Development, Vol. 5, pp. 877-884, 2011
2.	X. N. Yang, F. Liu,T. G. Tian, “A Grid Scheduling Algorithm based on Improved Genetic Algorithm,” Computer and Digital Engineering, Vol. 46, No. 9, pp. 1786-1789, 2018
3.	M. Chang and L. Qiu, “An Improved Grid Task Scheduling Algorithm,” Microcomputer Applications, Vol. 33, No. 33, pp. 30-36, 2017
4.	Y. L.Kang and J. A. Zhang, “Packet Scheduling Algorithm for Grid Tasks based on QoS,” Software, Vol. 37, No. 10, pp. 25-28, 2016
5.	L. Q. Pan, Y. Q. Zhang,Z. J. Mao, “Research on Task Scheduling Algorithm in Grid Environment,” Fujian Computer, Vol. 32, No. 8, pp. 1-12, 2016
6.	J. Cao and S. Q. Wang, “Emergency Resource Grid Scheduling based on Game Strategy,” Logistics Science and Technology, Vol. 39, No. 1, pp. 1-15, 2016
7.	Y. C.Hua and G. K. Zhang, “Research and Analysis of Grid Computing Model based on Big Data,”Information Technology and Informatization, Vol. 10, pp. 230-231, 2015
8.	D. H. Liu, “Brief Analysis of Task Scheduling Algorithm in Grid Environment,” Information Security and Technology, Vol. 5, No. 12, pp. 33-35, 2014
9.	D. C.Wang and Y. Gong, “Task Scheduling Algorithm based on Multi-QoS Constraints in Grid Environment,” Journal of Changchun University of Technology (Natural Science Edition), Vol. 37, No. 5, pp. 134-136, 2014
10.	D. W.Wang and S. Jiang, “An Improved Workflow Scheduling Algorithm in Grid Computing,”Computer Technology and Development, Vol. 2, pp. 71-75, 2014
11.	X. H. Zhu, “A Grid Workflow Scheduling Algorithm for Communication Overhead,” Journal of Jianghan University (Natural Science Edition), Vol. 3, pp. 278-282, 2015
12.	P. Radu and W. Marek, “Bi-Criteria Scheduling of Scientific Grid Workflow,”IEEE Transactions on Automation Science and Engineering, pp. 364-380, 2012
13.	C. Li and Q. M. Zhu, “Review of Research on Grid Workflow Scheduling,”Computer Application and Software, pp. 279-283, 2010
14.	H. J.Cao and X. H. Shi, “Efficient and Dependable Scheduling of DAG Workflow Job in Grid,”High Performance Computing and Communication, pp. 201-223, 2012

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Improved Grid Task Scheduling Model Algorithm

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