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Enhanced Genetic Algorithm based Task Scheduling in Cloud Computing
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 101 - Number 14 |
| Year of Publication: 2014 |
| Authors: Rajveer Kaur, Supriya Kinger |
10.5120/17752-8653
|
Rajveer Kaur, Supriya Kinger . Enhanced Genetic Algorithm based Task Scheduling in Cloud Computing. International Journal of Computer Applications. 101, 14 ( September 2014), 1-6. DOI=10.5120/17752-8653
Abstract
Cloud computing is basically internet based computing while software, information and shared resources are provided to devices and computers on demand, like electricity grid. With the fusion of network technology and traditional computing technology such as distributed computing parallel computing, grid computing a cloud computing product is formed. Task scheduling is the major concern in the field of cloud computing. As the use of cloud computing increases, the burden on the cloud network also increases. So, it's the duty of the scheduler to make cloud efficient to solve client's tasks. This work focuses on the same to achieve the objective of optimized task scheduling where improved genetic algorithm is proposed. Genetic algorithm is artificial intelligent based soft computing technique to optimize the process. Here in this work, genetic algorithm is enhanced using new fitness function based on mean and grand mean values. This optimization can be implemented on both ends, for job scheduling and resource scheduling. This will schedule the whole process and optimize as much as possible. The results analysis also proves the cloud system's increased efficiency for task scheduling.
References
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