XCHANGE: High Performance Data Morphing in Distributed Applications

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Lofstead, Jay
Schwan, Karsten
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Distributed applications in which large volumes of data are exchanged between components that generate, process, and store or display data are common in both the high performance and enterprise domains. A key issue in these domains is mismatches of the data being generated with the data required by end users or by intermediate components. Mismatches are due to the need to customize or personalize data for certain end users, or they arise from natural differences in the data representations used by different components. In either case, mismatch correction – data morphing -- requires either servers or clients to perform extensive data processing. This paper describes automated methods and associated tools for morphing data in overlay networks that connect data producers with consumers. These methods automatically generate data transformation codes from declarative specifications, ‘just in time’, i.e., when and as needed. By describing data transformations declaratively, code generation can take into account the current nature of the data being generated, the current needs of data sinks, and the current resources available in the overlay connecting sources to sinks. In addition, code generation can consider the shared requirements of multiple consumers, to reduce redundant data transmissions and transformations. Data morphing is realized with the XCHANGE toolset, and in this paper, it is applied to both high performance and enterprise applications. Runtime generation and deployment of data morphing codes for filtering and transforming the large data volumes exchanged in a high performance remote data visualization is shown to result in improved network usage, able to generate code that matches the data volumes exchanged to available network resources. Morphing codes dynamically generated and deployed for an enterprise application in the healthcare domain demonstrates the importance of generating code so as to improve server scalability by reducing server loads.
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