Modified Particle Swarm Optimization based upon Task Categorization in Cloud Environment
Neha Miglani1, Gaurav Sharma2
1Neha Miglani, Department of Computer Engineering, National Institute of Technology, Kurukshetra (Haryana,) India.
2Dr. Gaurav Sharma, Department of Computer Science and Engineering, Seth Jai Parkash Mukand Lal Institute of Engineering and Technology, Radaur, Kurukshetra University, Kurukshetra (Haryana,) India.
Manuscript received on 25 March 2019 | Revised Manuscript received on 06 April 2019 | Manuscript Published on 11 April 2019 | PP: 67-72 | Volume-8 Issue-4C, April 2019 | Retrieval Number: D24230484C19/19©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: Cloud Computing has become a spearhead in the field of industries and academia. As far as the IT Industry is concerned, it is pioneering the peculiar domains of clustering, virtualization and grid computing. Traditionally, the complex computation nowadays, demands abundance of resources and computing facilities to perform operational tasks. Cloud computing provides user a new wave in procuring available resources. To scale up the capacity, task scheduling has been emerged as one of the key features of Cloud Computing. Though it is considered as NP-Hard problem, yet numerous researchers and authors have tried to reap out the effective and implementable results for scheduling of tasks to different virtual machines. Meta-heuristic techniques have been embedded to obtain nearly optimal results in the previous studies, still loopholes are lying in the consideration of multiple QoS parameters. In this paper, PSO approach has been modified by manipulating parameters based on the QoS factors from the very initial stage. Instead of considering the population randomly, MIPS and Bandwidth factors have been inculcated to refine and adjust the parametric structure as well as for balancing the load more efficiently. The experimental setup shows that the proposed algorithm works fairly well in assigning the upcoming tasks, henceforth, resulting in reduction of execution time as well.
Keywords: Cloud Computing, Load Balancing, Particle Swarm Optimization, Quality of Service, Task Scheduling.
Scope of the Article: Cloud Computing