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Data Placement in Distributed Systems
Failure Awareness and Dynamic Adaptation in Data Scheduling
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Over the years, scientific applications have become§more complex and more data intensive. Especially§large scale simulations and scientific experiments in§areas such as physics, biology, astronomy and earth§sciences demand highly distributed resources to§satisfy excessive computational requirements.§Increasing data requirements and the distributed§nature of the resources made I/O the major bottleneck§for end-to-end application performance. Existing§systems fail to address issues such as reliability,§scalability, and efficiency in dealing with wide area§data access, retrieval and processing. We explore§data-intensive distributed computing and study§challenges in data placement in distributed§environments. After analyzing different application§scenarios, we develop new data scheduling§methodologies and the key attributes§for reliability, adaptability and performance§optimization of distributed data placement tasks.
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