A new paradigm: Data-aware scheduling in grid computing

Efficient and reliable access to large-scale data sources and archiving destinations in a widely distributed computing environment brings new challenges. The insufficiency of the traditional systems and existing CPU-oriented batch schedulers in addressing these challenges has yielded a new emerging era: data-aware schedulers. In this article, we discuss the limitations of the traditional CPU-oriented batch schedulers in handling the challenging data management problem of large-scale distributed applications; give our vision for the new paradigm in data-intensive scheduling; and elaborate on our case study: the Stork data placement scheduler.