Overview

The Second International Workshop on Data-Aware Distributed Computing (DADC'09) was held in conjunction with the 18th International Symposium on High Performance Distributed Computing (HPDC-18), in Munich, Germany.

** Workshop program and slides are available here **
The data needs of scientific as well as commercial applications from a diverse range of fields have been increasing exponentially over the recent years. This increase in the demand for large-scale data processing has necessitated collaboration and sharing of data collections among the world's leading education, research, and industrial institutions and use of distributed resources owned by collaborating parties. In a widely distributed environment, data is often not locally accessible and has thus to be remotely retrieved and stored. While traditional distributed systems work well for computation that requires limited data handling, they may fail in unexpected ways when the computation accesses, creates, and moves large amounts of data especially over wide-area networks. Further, data accessed and created is often poorly described, lacking both metadata and provenance. Scientists, researchers, and application developers are often forced to solve basic data-handling issues, such as physically locating data, how to access it, and/or how to move it to visualization and/or compute resources for further analysis.

This workshop focused on the challenges imposed by data-intensive applications on distributed systems, and on the different state-of-the-art solutions proposed to overcome these challenges. Characterized by a new paradigm called "data-aware distributed computing", this workshop explores problems in data aware scheduling, resource allocation, metadata collection, workflow management, and visualization. Armed with tools and theory of suitable data management techniques, the workshop series will enable domain scientists to focus on their primary goal. This workshop brought together the collaborative and distributed computing community and the data management community in an effort to generate productive conversations on the planning, management, and scheduling of data handling tasks and data storage resources.