Review: Analysis of Job Scheduling Algorithms and Physical Servers

In cloud computing, with full control of the underlying infrastructures, cloud providers can flexibly place user jobs on suitable physical servers and dynamically allocate computing resources to user jobs in the form of virtual machines. As a cloud provider, scheduling user jobs in a way that minimizes their completion time is important, as this can increase the utilization, productivity, or profit of a cloud. In this paper, we focus on the problem of scheduling embarrassingly parallel jobs composed of a set of independent tasks and consider energy consumption during scheduling. Thus, scheduling becomes tough in cloud computing because of large number of jobs submitted randomly. The ultimate objective of the study analysis is to reduce the make span of the job, to improve the processor utilization irrespective with the cloud environment. Adaptive Deadline Based Dependent Job Scheduling (A2DJS) algorithm in cloud computing that comprises of three major components as job manager, data center and VM creation. Here, the job manager embeds with dependency resolver and task-prioritizer. The dependency resolver will determine the dependency among the tasks and task-prioritizer will prioritize the tasks to avoid starvation. Moreover, the data center embeds with job scheduler and host creation with VM allocation. The job scheduler schedules the job with the VM existing. The host creation with VM allocation allocates the jobs to the VM in a two-tier VM