Genetic Algorithm Inspired Task Scheduling Optimization in Cloud Environment
Keywords:Cloud Computing, Genetic Algorithm, Task Scheduling, Grid Computing
With the advancements in various fields of technology, complex and data-oriented problems require supercomputers for proper computation. Massive data that is gathered from various fields of sciences and engineering and it is increasing exponentially with every passing day. There is a dire need of economical solution for efficient processing of data. This is where cloud computing comes into picuture. Cloud computing plays a major role in providing services to individual people and companies with added benefits such as elastic computation, pay on the go, scalable and high-performance computation solutions. The performance factor in cloud environments is highly reliant on task scheduling. Load balancing is simply the distribution of incoming load of users’ request onto the available computing machines. This paper aims to cover a brief overview of Cloud computing and Genetic algorithm and implementation of genetic algorithm for task scheduling pupose. The aim of this paper is to perform a comparative analysis of various task scheduling algorithms that are already in practice and are used in cloud computing. The paper also covers improved scheduling techniques that are inspired from Genetic Algorithm. A systematic comparative analysis of different task scheduling algorithms is also included. Lastly some concluding remarks and future work inspiration is described.
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