Abstract

The objective of the study is to device an Adaptive Machine Scoring Technique with Cluster (AMSTWC) to schedule the jobs/tasks in a grid environment which reduces the overall completion time (make span) and increases the resource Utilization. It also minimizes the execution time of the algorithm and with QoS satisfaction. The scheduling is done for computational as well as data grids. There are many heterogeneous Gridlets/machines which are geographically distributed. So, the searching time of the appropriate Gridlets, most suitable for the given job is more. This algorithm clusters the Gridlets depending on their configurations which reduces the search time of the Gridlets/machines which satisfies QoS. Task requirements are matched against the Machine capabilities available in Grid and AMSTWC selects the machine which has the highest resource score. AMSTWC result is compared with the existing algorithms in terms of make span, Resource Utilization, Flow Time and Execution time. AMSTWC performs better than the existing algorithms in most of the cases.

Keywords:

Execution time, flow time, gridlets, machine scoring, make span, resource utilization,

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Competing interests

The authors have no competing interests.

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