To Study Optimization Method in Scheduling for Cloud Computing

Abstract

In order to secure guaranteed network storage space and processing resources, subscription-based services are essential to cloud computing. The fundamental goal of cloud computing is to deliver dependable services while keeping energy use and cost as two crucial aspects of service quality. Rule-based scheduling algorithms are widely employed in cloud computing systems. It improves the performance of the algorithms and is straightforward and simple to implement. The Energy Efficient Scheduling Scheme (EESS) prioritises placing the greatest amount of load on the fewest possible virtual machines. By allocating the right resources to the desired activities, cost-based scheduling with Genetic Algorithms (GA) aims to shorten the execution time, which in turn lowers the user cost. The CloudSim simulator is used to create the cloud environment. In comparison to other schemes, the EES scheme is more energy-efficient, while the GA scheme executes more quickly, lowering the cost of cloud computing. Currently used scheduling methods take a long time since they run many checks. A novel heuristic algorithm is proposed to choose the effective scheduling method based on the results of the existing heuristic algorithms. A novel hyper-heuristics scheduling method is suggested as the best option for cloud computing systems. The optimal output policy is obtained by choosing the heuristic algorithm and using the best policy selector.