Efficient Scheduling of Workflow in Cloud Enviornment Using Billing Model Aware Task Clustering

Cloud computing is a cost effective alternative for the scientific community to deploy large scale workflow applications.For executing large scale scientific workflow applications in a distributed hetereogenous enviornment ,scheduling of workflow tasks with the dynamic resources is a challenging issue.Moreover in a utility based computing like cloud which supports pay per use model of the resources ,scheduling algorithm must efficiently utilize the available time of the resource.Most of the existing scheduling heuristics does not consider the dynamic nature of the cloud and hence produce the static schedule. Public cloud enviornment like Amazon EC2 offers catalog of resources and the price is generally metered per hour.Here any fractional usage is rounded off to the next hour.To meet the budget and deadline of the customers proposed work focuses to incorporate a billing model aware task clustering mechanism in the workflow scheduling process . This work also presents a resource selection algorithm which can be used for choosing proper resource at each stage in the workflow. Preliminary results obtained by running two scientific applications Montage and Cybershake with different resources and task clustering mechanisms are discussed.