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A Genetic Algorithm Based Scheduling Algorithm for Grid Computing Environments

Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 436)

Abstract

A grid computing environment is a parallel and distributed environment in which various computing capabilities are brought together to solve large size computational problems. Task scheduling is a crucial issue for grid computing environments; so it needs to be addressed efficiently to minimize the overall execution time. Directed acyclic graphs (DAGs) can be used as task graphs to be scheduled on grid computing systems. The proposed study presents a genetic algorithm for efficient scheduling of task graphs represented by DAG on grid systems. The proposed algorithm is implemented and evaluated using five real datasets taken from the literature. The result shows that the proposed algorithm outperforms other popular algorithms in a number of scenarios.

Keywords

  • Directed acyclic graph (DAG)
  • Scheduling
  • Genetic algorithm (GA)
  • Makespan

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Correspondence to Shivani Sachdeva .

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Poonam Panwar, Shivani Sachdeva, Satish Rana (2016). A Genetic Algorithm Based Scheduling Algorithm for Grid Computing Environments. In: Pant, M., Deep, K., Bansal, J., Nagar, A., Das, K. (eds) Proceedings of Fifth International Conference on Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 436. Springer, Singapore. https://doi.org/10.1007/978-981-10-0448-3_13

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  • DOI: https://doi.org/10.1007/978-981-10-0448-3_13

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