Clustering strategy for scientific workflow applications in iaas cloud environment

Abstract

Scientific workflows benefit from the cloud computing, which offers access to virtual resources provisioned on pay-as-you-go and on-demand basis. Minimizing resources costs to meets users budget is very important in the cloud environment. Several optimization approaches have been proposed to improve the performance and the cost of data-intensive scientific Workflow Scheduling (DiSWS) in cloud computing. However, in the literature, the majority of the DiSWS approaches focused on the use of heuristic and meta-heuristic as an optimization method. Furthermore, the tasks hierarchy in data-intensive scientific workflows has not been extensively explored in the current literature. Specifically, in this paper, a data-intensive scientific workflow is represented as a hierarchy, which specifies hierarchical relations between workflow tasks, and an approach for data-intensive workflow scheduling applications is proposed. We propose a scheduling algorithm for workflow applications; the design is done by deploying a set of clusters across the cloud. We are looking to execute the workflow under budget constraints. To do this, we regroup workflow tasks in clusters to reduce the inter-task communication rates. The performance and cost are analyzed using an extension of Cloudsim simulation tool and compared with existing multi-objective approaches. The results demonstrate that our approach reduces resources cost with respect to the user budgets.