Dynamic PSO for Task Scheduling Optimization in Cloud Computing
M. S. Sudheer1, M. Vamsi Krishna2

1M. S. Sudheer, Research Scholar, Department of CSE, Centurion University of Technology and Management, Parlakhemudi (Odisha), India.
2Dr. M. Vamsi Krishna, Professor, Department of CSE, Chaitanya Engineering College, Kakinada (Andhra Pradesh), India.
Manuscript received on 10 October 2019 | Revised Manuscript received on 19 October 2019 | Manuscript Published on 02 November 2019 | PP: 332-338 | Volume-8 Issue-2S11 September 2019 | Retrieval Number: B10520982S1119/2019©BEIESP | DOI: 10.35940/ijrte.B1052.0982S1119
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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: Task scheduling is still a challenge in cloud computing as no existing scheduling algorithms are not effectively provisioning and scheduling the resources in the cloud. Existing authors considered only metrics like make span, execution time and turnaround time etc. and the previous authors concentrated only to optimize the above mentioned metrics. But no existing authors were considered about the effective provisioning of the resources in the cloud i.e, compute, storage and network capacities and still many resources in the cloud were underutilized. In this paper, we want to propose an algorithm which can effectively utilize the resources in the cloud by extending Particle Swarm Optimization by addressing the metrics Bandwidth utilization and Memory utilization particularly. We have simulated this algorithm by using cloud sim and compared the modified Dynamic PSO with the PSO algorithm and it outperforms in terms of Bandwidth and Memory utilization and the make span is also optimized.
Keywords: Task Scheduling, Meta heuristic, Cloud Computing, Make span, Resource Utilization, Bandwidth Utilization, Memory Utilization.
Scope of the Article: Cloud Computing