Data Provenance Management of Bioinformatics Workflows in Federated Clouds In Bioinformatics, the reproducibility of experiments is a fundamental principle to which the data provenance significantly contributes through the acquisition and management of information about the trajectory of the data in a workflow. In addition to the data provenance, aspects such as program configurations and the entire computational environment must be considered to achieve this goal. Cloud computing can provide computational resources, hiding technical details and providing an accessible and configurable ondemand environment for researchers. Cloud federation enables the broad distribution of services and a flexible combination of computing power. Considering this particular scenario, we propose a management platform that collects the provenance data of Bioinformatics workflows allowing portability between different clouds in federated clouds using the Infrastructure as a Service model. This platform is composed of tools that support running these workflows with data provenance captured in NoSQL databases. The retrospective data provenance is captured according to the PROVDM norms. The findings indicate that the proposed platform can be used for some of the most commoninly available clouds.

OnTimeURB: MultiCloud Resource Brokering for Bioinformatics Workflows Scientific workflows due to their data and memory intensive requirements are among the prime applications which benefit by leveraging cloud computing. However, Cloud service providers (CSPs) have distinct policies and service dynamics that present a problem of excess choice for users. Performance and cost of the cloud services are among the principal factors in CSP selection for scientific bioinformatics workflows. The workflows typically are based on private data, and require diverse cloud resources, thus often requiring synergistic services from multiple CSPs. In this paper, we address this challenge of multicloud resource selection using cloud template solutions based on user specifications. We propose an optimizer that incorporates a combinatorial optimization model built on performance, cost and CSPs interoperability factors. The optimizer is integrated within a novel resource broker (i.e., OnTimeURB) for prescriptive recommendations of template solutions with intuitive choices for users. We implement and evaluate the OnTimeURB recommendations framework with a catalog of bioinformatics workflow applications integrated within a KBCommons science gateway. The evaluation considered four CSP resources featuring more than 300 different machine configuration instances. Our evaluation results show that our OnTimeURB creates consistently more economical, performance optimized and practical cloud solutions compared to a knearest neighbors (kNN) approach.