Abstract
An essential requirement in cloud computing environment is scheduling the current jobs to be executed with the given constraints. The scheduler should order t he jobs in a way where balance between improving the quality of services and at the same time maintainin g the efficiency and fairness among the jobs. Thus, evaluating the performance of scheduling algorithms is crucial towards realizing large-scale distribut ed systems. In spite of the various scheduling algorit hms 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 practica l 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 f or the tasks and guarantee fairness amongst the job s served. The three metrics for evaluating these job scheduli ng algorithms are throughput, makespan and the tota l execution cost. Several experiments with various ai ms have been accomplished in this comparative study.
Highlights
Nowadays, many companies offering services to the customer based on the concept of “pay as a service”, where each customer pays for the services obtained from the provider
This study aims at analyze and investigate four job scheduling algorithms under cloud environment, namely, Round Robin (RR), Random Resource Selection, Opportunistic Load Balancing and Minimum Completion Time, in terms of their ability to provide quality service for the tasks and guarantee fairness amongst the jobs served
Various performance metrics were taken into consideration in order to measure and evaluate the selected job scheduling algorithms
Summary
Many companies offering services to the customer based on the concept of “pay as a service”, where each customer pays for the services obtained from the provider. The cloud environment provides a different platform by creating a virtual machine that assists users in accomplishing their jobs within a reasonable time and cost-effectively without sacrificing the quality of the services. The huge growth in virtualization and cloud computing technologies reflect the increasing number of jobs that require the services of the virtual machine. Various types of scheduling algorithms have been applied on various data workloads and measured with different performance metrics to evaluate the performance. Most of the scheduling algorithms are developed to accomplish two aims. The first is to improve the quality of services in executing the jobs and provide the expected output on time.
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