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ICDCS '04 Proceedings of the 24th International Conference on Distributed Computing Systems (ICDCS'04)
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Scientific Programming
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PARA'10 Proceedings of the 10th international conference on Applied Parallel and Scientific Computing - Volume 2
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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.