A. Basit Darem

Work place: PG Department of Computer Science, St. Philomena’s College , University of Mysore, India

E-mail: Basit.darem@yahoo.com


Research Interests: Computational Science and Engineering, Software Engineering, Autonomic Computing, Data Structures and Algorithms, Engineering, Big data and learning analytics


Dr. Abdul Basit Darem is an assistant professor in PG Dept. of Computer Science, St. Philomena’s College, Mysore University. He is Mysore University fellow. He has earned his Ph.D in Computer Science from University of Mysore. He got his BSc. in Computer Science from Basrah University in Iraq in 1999 with first rank, MSc. and MCA from SMU India. He worked as a lecturer, in the Department of Computer Science, Basrah University in Iraq. He has published more than 18 research papers in reputed International Journals and Conferences. He has participated in many conferences in India and abroad. He is a member of E-government team in Yemen; he has worked in the AFMIS project in Yemen. His area of research includes Web Engineering, HCI, E-government, Cloud Computing 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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