RFM-PSO -RFM Score based PSO Task Scheduling in Cloud
R. Valarmathi1, T. Sheela2

1R. Valarmathi, Research Scholar, Sathyabama Institute of Science and Technology, Sri Sairam Engineering College, Chennai (TamilNadu), India.

2T. Sheela, Professor, Department of Information Technology, Sri Sairam Engineering College, Chennai (TamilNadu), India.

Manuscript received on 04 April 2019 | Revised Manuscript received on 11 April 2019 | Manuscript Published on 26 April 2019 | PP: 25-29 | Volume-8 Issue-6S April 2019 | Retrieval Number: F60150486S19/19©BEIESP

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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: Resource Management problem is considered as the major issue in recent decades This paper presents a novel particle swarm optimisation algorithm, with RFM score. Our proposed algorithm is used to solve the task scheduling problem. In our proposed algorithm there are two phases. RFM analysis of customers is done to improve the user experience as well as to increase the profit of cloud provider. The tasks are ranked according to RFM score and given priority according to the best rank. Ranked tasks forms the initial population of Particle swarm optimisation (PSO). In the Second phase the tasks are classified as CPU-intensive and I/O intensive. The two-phase algorithm helps to improve the performance of scheduling. Our proposed algorithm uses Cloudsim and compared with the existing metaheuristic algorithms like ACO and GA. Experimental results show that the RFM-PSO algorithm outperforms the other algorithms.

Keywords: The Two-Phase Algorithm Helps to Improve the Performance of Scheduling.
Scope of the Article: Security Technology and Information Assurance