Fast workflow scheduling for grid computing based on a multi-objective Genetic Algorithm

Task scheduling and resource allocation are two of the most important issues in grid computing. In a grid computing system, the workflow management system receives inter-dependent tasks from users and allocates each task to an appropriate resource. The assignment is based on user constraints such as...

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Bibliographic Details
Main Authors: Khajemohammadi, Hassan, Fanian, Ali, Gulliver, T. Aaron
Format: Conference Proceeding
Language:English
Subjects:
Genetic Algorithm (GA)
Genetic algorithms
Grid computing
Optimization
Processor scheduling
Scheduling
Sociology
Statistics
Utility Grid
Workflow Scheduling
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Description
Summary:Task scheduling and resource allocation are two of the most important issues in grid computing. In a grid computing system, the workflow management system receives inter-dependent tasks from users and allocates each task to an appropriate resource. The assignment is based on user constraints such as budget and deadline. Thus, the workflow management system has a significant effect on system performance and efficient resource use. In general, optimal task scheduling is an NP-complete problem. Hence, heuristic and meta-heuristic methods are employed to obtain a solution which is close to optimal. In this paper, workflow management based on a multi-objective Genetic Algorithm (GA) is proposed to improve grid computing performance. In grid computing, task runtime is an important parameter. Thus the proposed method considers a workflow as a collection of levels to eliminate the need to check workflow dependencies after a solution is obtained for the next population. As a result, both scheduling time and solution quality are improved. Results are presented which show that the proposed method has better performance compared to similar techniques.
ISSN:1555-5798
2154-5952
DOI:10.1109/PACRIM.2013.6625456