Open AccessHTSCC A Hybrid Task Scheduling Algorithm in Cloud Computing Environment
Author(s) -
Rasha Ali Al-Arasi,
Anwar Saif
Publication year - 2018
Publication title -
international journal of computer and technology
Language(s) - English
Resource type - Journals
ISSN - 2277-3061
DOI - 10.24297/ijct.v17i2.7584
Subject(s) - cloudsim , computer science , cloud computing , job shop scheduling , distributed computing , particle swarm optimization , scheduling (production processes) , fair share scheduling , dynamic priority scheduling , algorithm , mathematical optimization , operating system , computer network , quality of service , schedule , mathematics
Nowadays, cloud computing makes it possible for users to use the computing resources like application, software, and hardware, etc., on pay as use model via the internet. One of the core and challenging issue in cloud computing is the task scheduling. Task scheduling problem is an NP-hard problem and is responsible for mapping the tasks to resources in a way to spread the load evenly. The appropriate mapping between resources and tasks reduces makespan and maximizes resource utilization. In this paper, we present and implement an independent task scheduling algorithm that assigns the users' tasks to multiple computing resources. The proposed algorithm is a hybrid algorithm for task scheduling in cloud computing based on a genetic algorithm (GA) and particle swarm optimization (PSO). The algorithm is implemented and simulated using CloudSim simulator. The simulation results show that our proposed algorithm outperforms the GA and PSO algorithms by decreasing the makespan and increasing the resource utilization.