Experiencing Data Grids

Abstract

Abstract. Many scientific experiments deal with data-intensive applications and the orchestration of computational workflow activities. These can benefit from data parallelism exploited in parallel systems to minimize execution time. Due to its complexity, robustness and efficiency to exploit data parallelism, grid infrastructures are widely used in some e-Science areas like bioinformatics. Workflow techniques are very important to in-silico bioinformatics experiments, allowing the e-scientist to describe and enact experimental process in a structured, repeatable and verifiable way. The main purpose of this paper is to describe our experience with Tavena Workbench and PeDRo, which are part of my Grid project. Taverna is provided with a workflow toolset and enactor, allowing the specification of processing units, data transfer and execution constraints. As a data entry tool, PeDRo provides a model, a controlled vocabulary and field validations for Web Services descriptions, leveraging the knowledge associated to the workflows. The main contribution of this work is a summary of some considerations drawn by our experience with the use of these tools, emphasizing its advantages and negative aspects, together with proposals for some future improvements.