The Journal of Supercomputing

, Volume 71, Issue 4, pp 1427–1450 | Cite as

A hyper-heuristic approach for resource provisioning-based scheduling in grid environment

Article

Abstract

Grid computing being immensely based on the concept of resource sharing has always been closely associated with a lot many challenges. Growth of Resource provisioning-based scheduling in large-scale distributed environments like Grid computing brings in new requirement challenges that are not being considered in traditional distributed computing environments. Resources being the backbone of the system, their efficient management plays quite an important role in its execution environment. Many constraints such as heterogeneity and dynamic nature of resources need to be taken care as steps toward managing Grid resources efficiently. The most important challenge in Grids being the job–resource mapping as per the users’ requirement in the most secure way. The mapping of the jobs to appropriate resources for execution of the applications in Grid computing is found to be an NP-complete problem. Novel algorithm is required to schedule the jobs on the resources to provide reduced execution time, increased security and reliability. The main aim of this paper is to present an efficient strategy for secure scheduling of jobs on appropriate resources. A novel particle swarm optimization-based hyper-heuristic resource scheduling algorithm has been designed and used to schedule jobs effectively on available resources without violating any of the security norms. Performance of the proposed algorithm has also been evaluated through the GridSim toolkit. We have compared our resource scheduling algorithm with existing common heuristic-based scheduling algorithms experimentally. The results thus obtained have shown a better performance by our algorithm than the existing algorithms, in terms of giving more reduced cost and makespan of user’s application being submitted to the Grids.

Keywords

Grid computing Resource scheduling Heuristic methods 

Notes

Acknowledgments

We would like to thank all anonymous reviewers for their comments and suggestions for improving the paper. We would like to thank Parteek Gupta for helping in improving the language and expression of a preliminary version of this paper. We are also grateful to the Grid Workloads Archive group for making the Grid workload traces available. We also thank Dr. Dror Feitelson for maintaining the Parallel Workload Archive and all organizations and researchers who made their workload logs available.

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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Computer Science and Engineering DepartmentLNMIITJaipurIndia
  2. 2.Computer Science and Engineering DepartmentThapar UniversityPatialaIndia
  3. 3.Machine Intelligence and Research LabScientific Network for Innovation and Research ExcellenceAuburnUSA

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