Article in Proceedings INPROC-2010-116

BibliographyMemon, Faraz; Tiebler, Daniel; Dürr, Frank; Rothermel, Kurt: Optimized Information Discovery using Self-adapting Indices over Distributed Hash Tables.
In: Proceedings of 29th International Performance Computing and Communications Conference (IPCCC'10).
University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology.
pp. 1-9, english.
Albuquerque, New Mexico, USA: IEEE, December 9, 2010.
Article in Proceedings (Conference Paper).
CR-SchemaC.2.1 (Network Architecture and Design)
E.1 (Data Structures)
E.2 (Data Storage Representations)
H.3.1 (Content Analysis and Indexing)

Distributed Hash Table (DHT)-based peer-to-peer information discovery systems have emerged as highly scalable systems for information storage and discovery in massively distributed networks. Originally DHTs supported only point queries. However, recently they have been extended to support more complex queries, such as multi-attribute range (MAR) queries. Generally, the support for MAR queries over DHTs has been provided either by creating an individual index for each data attribute or by creating a single index using the combination of all data attributes. In contrast to these approaches, we propose to create and modify indices using the attribute combinations that dynamically appear in MAR queries in the system.

In this paper, we present an adaptive information discovery system that adapts the set of indices according to the dynamic set of MAR queries in the system. The main contribution of this paper is a four-phase scalable index adaptation process. Our evaluations show that the adaptive information discovery system continuously optimizes the overall system performance for MAR queries. Moreover, compared to a non-adaptive system, the adaptive information discovery system shows several orders of magnitude improved performance.

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Department(s)University of Stuttgart, Institute of Parallel and Distributed Systems, Distributed Systems
Project(s)Optimized Information Discovery
Entry dateJanuary 3, 2011
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