Dynamic Workload-Based Partitioning Algorithms for Continuously Growing Databases - LIRMM - Laboratoire d’Informatique, de Robotique et de Microélectronique de Montpellier Access content directly
Journal Articles Transactions on Large-Scale Data- and Knowledge-Centered Systems Year : 2013

Dynamic Workload-Based Partitioning Algorithms for Continuously Growing Databases

Abstract

Applications with very large databases, where data items are continuously appended, are becoming more and more common. Thus, the development of efficient data partitioning is one of the main requirements to yield good performance. In the case of applications that have complex access patterns, e.g. scientific applications, workload-based partitioning could be exploited. However, existing workload-based approaches, which work in a static way, cannot be applied to very large databases. In this paper, we propose DynPart and DynPartGroup, two dynamic partitioning algorithms for continuously growing databases. These algorithms efficiently adapt the data partitioning to the arrival of new data elements by taking into account the affinity of new data with queries and fragments. In contrast to existing static approaches, our approach offers constant execution time, no matter the size of the database, while obtaining very good partitioning efficiency. We validated our solution through experimentation over real-world data; the results show its effectiveness.
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Dates and versions

lirmm-00906966 , version 1 (20-11-2013)

Identifiers

Cite

Miguel Liroz-Gistau, Reza Akbarinia, Esther Pacitti, Fabio Porto, Patrick Valduriez. Dynamic Workload-Based Partitioning Algorithms for Continuously Growing Databases. Transactions on Large-Scale Data- and Knowledge-Centered Systems, 2013, LNCS (8320), pp.105-128. ⟨10.1007/978-3-642-45315-1_5⟩. ⟨lirmm-00906966⟩
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