ISBN-13: 9783639124330 / Angielski / Miękka / 2009 / 100 str.
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.
Over the years, scientific applications have becomemore complex and more data intensive. Especiallylarge scale simulations and scientific experiments inareas such as physics, biology, astronomy and earthsciences demand highly distributed resources tosatisfy excessive computational requirements.Increasing data requirements and the distributednature of the resources made I/O the major bottleneckfor end-to-end application performance. Existingsystems fail to address issues such as reliability,scalability, and efficiency in dealing with wide areadata access, retrieval and processing. We exploredata-intensive distributed computing and studychallenges in data placement in distributedenvironments. After analyzing different applicationscenarios, we develop new data schedulingmethodologies and the key attributesfor reliability, adaptability and performanceoptimization of distributed data placement tasks.