A Persuasive Resource-Aware Allocation Scheduler for Enhancing Task Scheduling
1R. Rengasamy, Research Scholar A.V.V.M Sri Pushpam College, Poondi, Thanjavur, India.
2M. Chidambaram Department of Computer Science, Rajah Serfoji Government College, Thanjavur, India.
Manuscript received on July 20, 2019. | Revised Manuscript received on August 10, 2019. | Manuscript published on August 30, 2019. | PP: 2808-2811 | Volume-8 Issue-6, August 2019. | Retrieval Number: F8908088619/2019©BEIESP | DOI: 10.35940/ijeat.F8908.088619
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The deployment of Map Reduce has been built to grant enhancements to total system objectives such as job throughput. Hence, the support for user-specific objectives and resource allocation management has been least regarded and addressed. Schedulers enable users to assign jobs to queues that fulfil shared of specific resource. Existing work mainly focus on scheduling glitch occurring on the master’s side where the scheduler on the master node tries to allocate same work across all the worker nodes. The proposed scheduler focus on enhancing resource allocation when various kinds of workloads execute on the clusters. In order to evaluate the performance on the proposed scheduler which enhances resource utilization, an accomplishing time goal with each job is created.
Keywords: Map Reduce, Big data, Resource allocation, Hadoop.