Kahejvand, V., Pedram, H., Zandieh, M. (2014). Multi-objective and Scalable Heuristic Algorithm for Workflow Task Scheduling in Utility Grids. Journal of Optimization in Industrial Engineering, 7(14), 27-36.
Vahid Kahejvand; Hossein Pedram; Mostafa Zandieh. "Multi-objective and Scalable Heuristic Algorithm for Workflow Task Scheduling in Utility Grids". Journal of Optimization in Industrial Engineering, 7, 14, 2014, 27-36.
Kahejvand, V., Pedram, H., Zandieh, M. (2014). 'Multi-objective and Scalable Heuristic Algorithm for Workflow Task Scheduling in Utility Grids', Journal of Optimization in Industrial Engineering, 7(14), pp. 27-36.
Kahejvand, V., Pedram, H., Zandieh, M. Multi-objective and Scalable Heuristic Algorithm for Workflow Task Scheduling in Utility Grids. Journal of Optimization in Industrial Engineering, 2014; 7(14): 27-36.
Multi-objective and Scalable Heuristic Algorithm for Workflow Task Scheduling in Utility Grids
1Assistant Professor, Department of Computer Engineering and Information Technology, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2Associate Professor, Department of Computer Engineering and Information Technology, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
3Associate Professor, Department of Industrial Management, Shahid Beheshti University, G.C., Tehran, Iran
Abstract
To use services transparently in a distributed environment, the Utility Grids develop a cyber-infrastructure. The parameters of the Quality of Service such as the allocation-cost and makespan have to be dealt with in order to schedule workflow application tasks in the Utility Grids. Optimization of both target parameters above is a challenge in a distributed environment and may conflict one another. We, therefore, present a novel heuristic algorithm for scheduling a workflow application on Utility Grids. Our proposed algorithm optimizes the allocation-cost and makespan in a scalable and very low runtime. The results of the wide-spread simulation indicate that the proposed algorithm is scalable against an increase in the application size and task parallelism of the application. The proposed algorithm effectively outperforms the current algorithms in terms of the allocation-cost, makespan and runtime scalability.
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