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Project Scheduling Heuristics-Based Standard PSO for Task-Resource Assignment in Heterogeneous Grid

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  • Ruey-Maw Chen
  • Chuin-Mu Wang

Abstract

The task scheduling problem has been widely studied for assigning resources to tasks in heterogeneous grid environment. Effective task scheduling is an important issue for the performance of grid computing. Meanwhile, the task scheduling problem is an NP-complete problem. Hence, this investigation introduces a named “standard“ particle swarm optimization (PSO) metaheuristic approach to efficiently solve the task scheduling problems in grid. Meanwhile, two promising heuristics based on multimode project scheduling are proposed to help in solving interesting scheduling problems. They are the best performance resource heuristic and the latest finish time heuristic. These two heuristics applied to the PSO scheme are for speeding up the search of the particle and improving the capability of finding a sound schedule. Moreover, both global communication topology and local ring communication topology are also investigated for efficient study of proposed scheme. Simulation results demonstrate that the proposed approach in this investigation can successfully solve the task-resource assignment problems in grid computing and similar scheduling problems.

Suggested Citation

  • Ruey-Maw Chen & Chuin-Mu Wang, 2011. "Project Scheduling Heuristics-Based Standard PSO for Task-Resource Assignment in Heterogeneous Grid," Abstract and Applied Analysis, Hindawi, vol. 2011, pages 1-20, February.
  • Handle: RePEc:hin:jnlaaa:589862
    DOI: 10.1155/2011/589862
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    Cited by:

    1. Hu, Hongping & Wang, Haiyan & Bai, Yanping & Liu, Maoxing, 2019. "Determination of endometrial carcinoma with gene expression based on optimized Elman neural network," Applied Mathematics and Computation, Elsevier, vol. 341(C), pages 204-214.

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