Suresha

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

E-mail: sureshabm@yahoo.co.in

Website:

Research Interests: Computer systems and computational processes, Systems Architecture, Image Compression, Image Manipulation, Image Processing, Information Systems, Data Mining, World Wide Web

Biography

Dr. Suresha is currently working as a Professor, in the Department of Studies in Computer Science, University of Mysore, Mysore. He has 26 years of teaching experience in computer science at post-graduate level in various universities. He has obtained MSc. from University of Mysore, M.Phil. from DAVV, M.Tech from IIT-Kharagpur, and Ph.D from IISc-Bangalore. He has published research papers in reputed International and national conferences. His area of research includes Dynamic Web Caching, Database Systems, Image Search Engines, E-governance, Opinion Mining, and Cloud Computing. He has also taught many courses in foreign university as part of teaching assignments.

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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