TY  - JOUR
T1  - A Fine Tuned Expedite Evolutionary Approach for Scheduling Deadline Constrained Scientific Application in Cloud
AU - Sindhu, S. AU - Mukherjee, Saswati 
JO  - Asian Journal of Information Technology
VL  - 15
IS  - 17
SP  - 3268
EP  - 3279
PY  - 2016
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2016.3268.3279
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2016.3268.3279
KW  - Workflow
KW  -scheduling
KW  -cloud computing
KW  -deadline
KW  -genetic algorithm
KW  -fine tuning
AB  - Recently cloud computing has been widely used by researchers for executing scientific applications. Cloud offers resources in the form of virtual machines which are dynamically provisioned and released and comes with a usage cost. This gives an illusion of abundance of resources available for execution. Hence, efficient scheduling mechanisms which satisfy the various quality of service requirements while minimizing the cost are the need of the hour. Although, scheduling algorithms which map a deadline constrained workflow to a cloud while minimizing cost have been studied, most of the time the makespan of the optimal schedule returned will be less than the deadline. Hence, a fine tuning of the optimal schedule thus returned will be useful and has the advantage of minimizing the under utilization of resources thereby achieving savings in cost. This study proposes an expedite genetic algorithm with fine tuning for scheduling deadline constrained workflow applications onto a cloud.
ER  -