Performance Analysis of Resource Selection Algorithms in Grid Computing Environment
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Copyright: © 2020 Malarvizhi Nandagopal and Rhymend Uthariaraj. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Problem statement: Meta- scheduling has become important due to increased number of jobs and resources in the area of grid computing to achieve the objectives of grid users and grid resources. Approach: A variety of factors need to be considered for effective scheduling of resources in such environments such as total time job spend in the grid system, global and local allocation policies and resource utilization. A general and extensible scheduling architecture addressing these issues is proposed. Within this architecture a Multi Criteria Resource Selection (MCRS) algorithm is developed and performance of this algorithm is evaluated using simulations for a wide range of parameters. To select a resource for job execution the proposed algorithm considers multiple criteria like processing power, workload and network bandwidth of the resource. Results: The proposed algorithm is compared with the conventional single criteria resource selection algorithms. Conclusion: Simulation results show that in order to improve the performance of both user and resource it is important to consider multiple criteria together to select the optimal resource, rather than considering them separately.
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- Performance analysis
- LARS algorithm
- computing environment
- Meta- scheduling
- Multi Criteria Resource Selection (MCRS)
- Grid Information Server (GIS)
- Power Aware Resource Selection (PARS)