Abstract
Query processing in a distributed database system requires the transmission of data between sites using communication networks. Distributed query processing is an important factor in the overall performance of a distributed database system. In distributed query optimization, complexity and cost increases with increasing number of relations in the query. Cost is the sum of local cost (I/O cost and CPU cost at each site) and the cost of transferring data between sites. Extensive research has been done for query processing and optimization in distributed databases. Numerous search strategies like static, dynamic and randomized strategies are available for determining an optimal plan. However these search strategies are not suitable for the autonomous distributed database systems. These search strategies make certain assumptions (like all sites have same processing capability), which do not hold for autonomous systems. Mariposa, Query Trading (QT) and Query Trading with Processing Task Trading (QTPT) are the query processing algorithms developed for autonomous distributed database systems. However, they incur high optimization cost due to involvement of all nodes in generating optimal plan. We present our solution k-QTPT, to reduce the high optimization cost incurred by QTPT. In k-QTPT, only k nodes participate in generating optimal plans. We discuss implementation details of QT, QTPT algorithm and our solution k-QTPT. We evaluate k-QTPT through emulation. We show that the cost of optimization reduces substantially in k-QTPT as compared to QT and QTPT.
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Doshi, P., Raisinghani, V. (2013). k-QTPT: A Dynamic Query Optimization Approach for Autonomous Distributed Database Systems. In: Unnikrishnan, S., Surve, S., Bhoir, D. (eds) Advances in Computing, Communication, and Control. ICAC3 2013. Communications in Computer and Information Science, vol 361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36321-4_1
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DOI: https://doi.org/10.1007/978-3-642-36321-4_1
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