Modeling of multi objective job scheduling system for cloud environment

Abstract

Cloud computing is one of the most recent innovative technologies which deliver on-demand computing services over the internet on a pay-as-you-use basis. It increases the working efficiency, as Cloud offers a variety of services to organizations, so that the organizations can avoid the upfront cost and complexity of occurring and maintaining their infrastructure. The most essential characteristic of the Cloud computing is the ability to dynamically schedule the task on the better Virtual Machine (VM) according to the resources. Although, Cloud computing manages a variety of virtualized resources, task scheduling is a major challenge in Cloud environment. In the Cloud, a client may utilize several thousand virtualized assets for every client task. Therefore, manual job scheduling is not a feasible solution. The basic idea behind job scheduling is to schedule jobs to minimize scheduling time and maximize profit. In recent years, several research works have been carried out related to job scheduling. In fact, there is a scope to improve the job scheduling. In this study, an efficient multi-objective job scheduling in a Cloud environment is developed and proposed to improve the performance of the job scheduling process. A hybridization of Firefly and BAT (FFBAT) algorithm deals with efficient job scheduling which is proposed to schedule the jobs in a Cloud computing environment. The FFBAT algorithm is used to yield a better solution of job scheduling to minimize the total execution time by selecting a best fitness that is obtained through the maximum number of iterations. newline