A Dynamic Cloud Dimensioning Approach for Parallel Scientific Workflows: a Case Study in the Comparative Genomics Domain

Abstract

Usually, scientists need to execute experiments that demand high performance computing environments and parallel techniques. This is the scenario found in many bioinformatics experiments modeled as scientific workflows, such as phylogenetic and phylogenomic analyses. To execute these experiments, scientists have adopted virtual machines (VMs) instantiated in clouds. Estimating the number of VMs to instantiate is a crucial task to avoid negative impacts on the execution performance and on the financial costs with under or overestimations. Previously, the necessary number of VMs to execute bioinformatics workflows have been estimated by a GRASP heuristic and have been coupled to a Cloud-based Parallel Scientific Workflow Management System. Although this work was a step forward, this approach only provided a static dimensioning. If the characteristics of the environment change (processing capacity, network speed), this static dimensioning may not be suitable. In this way, it is of interest that the dimensioning is adjusted at runtime. To achieve this, we developed a novel framework for monitoring and dynamically dimensioning resources during the execution of parallel scientific workflows in clouds, called Dynamic Dimensioning of Cloud Computing Framework (DDC-F). We have evaluated DDC-F in real executions of bioinformatics workflows. Experiments showed that DDC-F is able to efficiently calculate the number of VMs necessary to execute bioinformatics workflows of Comparative Genomics (CG), also reducing the financial costs, when compared with other works of the related literature.