A Particle Swarm Optimization Approach With Migration for Resource Allocation in Cloud
Aleena Xavier T1, Rejimoan R.2
1Aleena Xavier T, Department of Computer Science, Sree Chitra Thirunal College of Engineering, Thiruvananthapuram (Kerala), India.
2Rejimoan R, Department of Computer Science, Sree Chitra Thirunal College of Engineering, Thiruvananthapuram (Kerala), India.
Manuscript received on 13 August 2016 | Revised Manuscript received on 20 August 2016 | Manuscript Published on 30 August 2016 | PP: 133-137 | Volume-5 Issue-6, August 2016 | Retrieval Number: F4698085616/16©BEIESP
Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© 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: Cloud computing is an emerging technology. The main motivation behind the proposed work is to design a Cloud Broker for efficiently managing cloud resources and to complete the jobs within a deadline. The proposed approach intends to achieve the objectives of reducing execution time, cost and workload based on the defined fitness function. The work is simulated in CloudSim and the results prove the effectiveness of the proposed work. A better allocation was achieved when all of the three factors were considered. The analysis of work was done by comparing one of the previous works where only time and cost were the objectives. By plotting a graph against Response time and deadline and another graph depicting the relation between the idle time and deadline this result has been proved.
Keywords: Resource Allocation, Job Scheduling, Cloud Computing, IaaS, Particle Swarm Optimization
Scope of the Article: Cloud Computing