An Approach based Upon Cross Breed Algorithm To Improve Job Scheduling at Cloud

In the cloud computing, many of the users send requests to cloud at the same time to access services. Thus, a big challenge of scheduling of these tasks is in cloud computing. Many algorithms like FCFS, SJF, Priority based, RR, MLQ, LSTR used to schedule the tasks in cloud computing. In cloud computing, most of the data centers consume vast amount of energy and take much more time to schedule the jobs. In this research paper, deploy a hybrid algorithm for job scheduling in cloud computing, using the combination of Multi-Level Feedback Queue Scheduling and Least Slack Time Rate (LSTR) is proposed to improve the issue of maximum energy consumption and time consumption by the data centers. Least Slack Time Rate is used to first select those processes that have the smallest “slack time”. Multi-Level Feedback Queue is used in this scheme the processes can move between the different queues. MLFQ uses the working principle of Round Robin and First come First Serve scheduling algorithms. The performance of the proposed method is measured by calculating the parameters of Energy Consumption, Minimize Processing Time, Executed Jobs, and Unexecuted Jobs.