Abstract
Computational grid is made up of virtual resources and differs from high-performance computing (HPC), as it is used in scientific and technological computation-intensive problem-solving. In computational grid, resources are classified based on their load factors. Utilization of these resources through co-ordination of various loads is always considered an optimization problem. In order to achieve this, approach of adaptive resource ranking is applied in this research. This paper proposes to improve the efficiency of earlier proposed NDFS algorithm by introducing historical or average load of each resource along with their current load for a defined period or interval. This adaptive methodology of resource co-ordination is necessary for balancing load in the computational grid. Jobs are scheduled for adaptively ranked resources, thus meeting the service quality agreement (SQA). The grid test bed experimental set-up is made by Globus Toolkit 5.2, and benchmark codes of matrix multiplication and fast Fourier transform are executed to demonstrate the results in this paper.
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Goswami, S., Das, A., Mukherjee, K. (2019). A Predictive Resource Management Technique in Grid. In: Abraham, A., Dutta, P., Mandal, J., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 814. Springer, Singapore. https://doi.org/10.1007/978-981-13-1501-5_9
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DOI: https://doi.org/10.1007/978-981-13-1501-5_9
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