Mokhtar A. Alworafi

Work place: DoS in Computer Science, University of Mysore, Mysore, India

E-mail: mokhtar119@gmail.com

Website:

Research Interests: Computer systems and computational processes, Autonomic Computing, Computer Architecture and Organization, Computer Networks, Distributed Computing, Parallel Computing, Big data and learning analytics

Biography

Mokhtar A. Alworafi is a doctoral student at the Department of Study in Computer Science, University of Mysore in India. He has earned BSc in Computer Science from Al-Technologia University in Iraq and MSc in CS from Kuvempu University. He works as a teaching assistant in The Center of Computer and Information Technology, Ibb University, Yemen. He has published 4 research papers in reputed international Journals and Conferences. His area of research interest includes Cloud Computing, Parallel Computing, Computer Networks, Internet of Things, and Big Data.

Author Articles
Cost-Aware Task Scheduling in Cloud Computing Environment

By Mokhtar A. Alworafi Atyaf Dhari Asma A. Al-Hashmi Suresha A. Basit Darem

DOI: https://doi.org/10.5815/ijcnis.2017.05.07, Pub. Date: 8 May 2017

Cloud computing is a new generation of computing environment which delivers the applications as a service to users over the internet. The users can select any service from a list provided by service providers depending on their demands or needs. The nature of this new computing environment leads to tasks scheduling and load balancing problems which become a booming research area. In this paper, we have proposed Scheduling Cost Approach (SCA) that calculates the cost of CPU, RAM, bandwidth, storage available. In this approach, the tasks will be distributed among the VMs based on the priority given by user. The priority depends on the user budget satisfaction. The proposed SCA will try to improve the load balance by selecting suitable VM for each task. The results of SCA are compared with the results of FCFS and SJF algorithms which proves that, the proposed SCA approach significantly reduces the cost of CPU, RAM, bandwidth, storage compared to FCFS and SJF algorithms.

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