Cloud computing implies to the delivery of information technology (IT) services which functions by retrieving the resources from the Internet; implementing various web-based tools and applications, in opposition to directly connecting to a server. In a nutshell, cloud computing works on the purpose of taking all the efforts involved in processing large quantities of data from the device carried around and moving that work to huge computer clusters far away in a virtual space. The internet becomes the virtual information space “THE CLOUD”, and all the data, work and applications are available from any device which when connected to the internet, anywhere in the world accesses it. Cloud computing is the provisioning of business computing model and providing multifarious facilities over the internet. Data that are looked over by third parties or other person at various remote locations can be assessed by individuals and various other business organizations through Cloud Computing applications. A cloud environment is categorized into computing clouds and data clouds.Task scheduling is considered to be the core feature and plays an important role in maintaining the quality of service in the cloud computing environment. The application of genetic algorithm in cloud computing task scheduler environment is a topic gaining popularity in the recent years. But, achieving an efficient task scheduling methodology is a major attribute for harnessing the potential of cloud computing applications in an effective manner. The objective of this paper is to discuss the application of heuristic algorithms; the use of GAs to minimize the total scheduling time and execution cost of tasks improves task completion time and maximize resource utilization in cloud computing framework by a task scheduler genetic algorithm.

Cloud Computing; Genetic Algorithm; Selection Operation; Crossover Operation; Task Scheduling; Mutation Operation; Fitness Function

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