Int. J. Electric and Hybrid Vehicles, Vol. x, No. x, 1–19 1 Efficient Job Scheduling in Cloud Computing Based on Genetic Algorithm Shirin Hosseinzadeh Sahraei Islamic Azad University, North Tehran Branch, Tehran, Iran. E-mail: hosseinzadeh.shirin@yahoo.com Mohammad Mansour Riahi Kashani * Islamic Azad University, North Tehran Branch, Tehran, Iran. E-mail: M_Riahi_Kashani@iau-tnb.ac.ir * corresponding author Javad Rezazadeh Islamic Azad University, North Tehran Branch, Tehran, Iran & University of Technology Sydney, Australia E-mail: rezazadeh@ieee.org Reza Farahbakhsh Institut Mines-Télécom, Télécom SudParis, CNRS Lab UMR5157 E-mail: reza.farahbakhsh@it-sudparis.eu Abstract: Scheduling in cloud is one of the challenging issues in resource management topic where the main question is how to manage time and cost in an optimized way. This study tackles the mentioned problem by managing time and cost through a genetic based algorithm. The primary goal of this study is to manage jobs in a shorter time with lower cost and higher utilization. Toward that end, we leverage the genetic algorithm solutions and a new model is proposed where jobs are created in genetic format. In the evaluation part of the model, different scenarios based on taking different fitness functions and format of the population are considered. We have analyzed makespan, cost and utilization in comparison to other two existing scheduling models (MAX-MIN and MIN-MIN). The results show considerable improvement in the cost, makespan, and utilization. Keywords: Cloud Computing, Job Scheduling, Genetic Algorithm, Cost, Makespan, Utilization. 1 Introduction Cloud Computing is a vast area which consists of three main services as follow, Infrastructure as a Service (IaaS),Platform as a Service (PaaS) and Software as a Service (SaaS). These Copyright © 201X Inderscience Enterprises Ltd.