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LGSA: Hybrid Task Scheduling in Multi Objective Functionality in Cloud Computing Environment

LGSA: Hybrid Task Scheduling in Multi Objective Functionality in Cloud Computing Environment Cloud computing turns to be a big shift from the conventional perception of the IT resources. It is a transpiring computing technology that is increasingly stabling itself as the promising future of distributed on-demand computing. The processes comprised in it are the ones that act as a vital backbone and which strengthen the entire stream of cloud computing as a whole. In specific, Task scheduling is the one such phenomena that enhances the cloud computing in terms of performance. Hence task scheduling that is considered as a predominant one amidst others is what this paper comprises all about. Maximizing the profit via assigning the whole task to the virtual machine is what the problem of scheduling deals with. Although there prevails many more ways to resolve this problem, this paper explores one such solution that consumes lesser number of resources, having lower cost and much importantly consuming lesser energy. By making a profound research regarding this approach of scheduling so as to represent the multi-objective function, both lion optimization algorithm and gravitational search algorithm are hybridized. In spite of having certain drawbacks which could be avoided although, the brighter side relies the merits of making use of both lion search and gravitational search algorithm. There could be many means of measurement for computing the performance of the algorithm. The different algorithms that aid to depict the comparable study encompasses gravitational search algorithm, genetic algorithm and lion, particle swarm optimization. The experimental results serve as the evident for depicting the bitterness of our proposed algorithm compared to the prevailing approaches. As an unexplored path may seem trivial but is effective so does the betterment of our lion approach. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png 3D Research Springer Journals

LGSA: Hybrid Task Scheduling in Multi Objective Functionality in Cloud Computing Environment

3D Research , Volume 10 (2) – Mar 15, 2019

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References (28)

Publisher
Springer Journals
Copyright
Copyright © 2019 by 3D Display Research Center, Kwangwoon University and Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Engineering; Signal,Image and Speech Processing; Computer Imaging, Vision, Pattern Recognition and Graphics; Optics, Lasers, Photonics, Optical Devices
eISSN
2092-6731
DOI
10.1007/s13319-019-0222-2
Publisher site
See Article on Publisher Site

Abstract

Cloud computing turns to be a big shift from the conventional perception of the IT resources. It is a transpiring computing technology that is increasingly stabling itself as the promising future of distributed on-demand computing. The processes comprised in it are the ones that act as a vital backbone and which strengthen the entire stream of cloud computing as a whole. In specific, Task scheduling is the one such phenomena that enhances the cloud computing in terms of performance. Hence task scheduling that is considered as a predominant one amidst others is what this paper comprises all about. Maximizing the profit via assigning the whole task to the virtual machine is what the problem of scheduling deals with. Although there prevails many more ways to resolve this problem, this paper explores one such solution that consumes lesser number of resources, having lower cost and much importantly consuming lesser energy. By making a profound research regarding this approach of scheduling so as to represent the multi-objective function, both lion optimization algorithm and gravitational search algorithm are hybridized. In spite of having certain drawbacks which could be avoided although, the brighter side relies the merits of making use of both lion search and gravitational search algorithm. There could be many means of measurement for computing the performance of the algorithm. The different algorithms that aid to depict the comparable study encompasses gravitational search algorithm, genetic algorithm and lion, particle swarm optimization. The experimental results serve as the evident for depicting the bitterness of our proposed algorithm compared to the prevailing approaches. As an unexplored path may seem trivial but is effective so does the betterment of our lion approach.

Journal

3D ResearchSpringer Journals

Published: Mar 15, 2019

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