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Today, data-intensive applications rely on geographically distributed systems to leverage data collection, storing and processing. Data locality has been seen as a prominent technique to improve application performance and reduce the impact of network latency by scheduling jobs directly in the nodes hosting the data to be processed. MapReduce and Dryad are examples of frameworks which exploit locality...
For better efficiency of parallel and distributed computing, Apache Hadoop distributes the imported data randomly on data nodes. This mechanism provides some advantages for general data analysis. With the same concept Apache Sqoop separates each table into four parts and randomly distributes them on data nodes. However, there is still a database performance concern with this data placement mechanism...
Grid architecture integrates geographically distributed nodes to manage and provide resources to execute scientific applications. For data locality, applications with different computational phases require data redistribution for realignment. The tradeoff between high efficiency computation and communication cost of data redistribution accompanies. This paper introduces a research model and two methods...
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