A distributed placement service for graph-structured and tree-structured data

Effective data placement strategies can enhance the performance of data-intensive applications implemented on high end computing clusters. Such strategies can have a significant impact in localizing the computation, in minimizing synchronization (communication) costs, in enhancing reliability (via strategic replication policies), and in ensuring a balanced workload or enhancing the available bandwidth from massive storage devices (e.g. disk arrays). Existing work has largely targeted the placement of relatively simple data types or entities (e.g. elements, vectors, sets, and arrays). Here we investigate several hash-based distributed data placement methods targeting tree- and graph- structured data, and develop a locality enhancing placement service for large cluster systems. Target applications include the placement of a single large graph (e.g. Web graph), a single large tree (e.g. large XML file), a forest of graphs or trees (e.g. XML database) and other specialized graph data type...

• Type: Conference
• Year: 2010
• Where: PPOPP
• Authors: Gregory Buehrer, Srinivasan Parthasarathy, Shirish Tatikonda
• Keywords: Data Placement Methods, Data Placement Strategies, Graph Data Types, Parallel Computing, PPOPP 2010