Comparative Study of Scheduling Al-gorithms in Cloud Computing Environment

An essential requirement in cloud computing environment is scheduling the current jobs to be executed with the given constraints. The scheduler should order the jobs in a way where balance between improving the quality of services and at the same time maintaining the efficiency and fairness among the jobs. Thus, evaluating the performance of scheduling algorithms is crucial towards realizing large-scale distributed systems. In spite of the various scheduling algorithms proposed for cloud environment, there is no comprehensive performance study undertaken which provides a unified platform for comparing such algorithms. Comparing these scheduling algorithms from different perspectives is an aspect that needs to be addressed. This pa-per aims at achieving a practical comparison study among four common job scheduling algorithms in cloud computing. These algorithms are Round Rubin (RR), Random Resource Selection, Opportunistic Load Balancing and Minimum Completion Time. These algorithms have been evaluated in terms of their ability to provide quality service for the tasks and guarantee fairness amongst the jobs served. The three metrics for evaluating these job scheduling algorithms are throughput, makespan and the total execution cost. Several experiments with various aims have been accomplished in this comparative study.