By Topic

Optimized information discovery using self-adapting indices over Distributed Hash Tables

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Memon, F. ; IPVS - Distrib. Syst. Dept., Univ. Stuttgart, Stuttgart, Germany ; Tiebler, D. ; Durr, F. ; Rothermel, K.

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 multiattribute 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 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, our system achieves several orders of magnitude improved performance.

Published in:

Performance Computing and Communications Conference (IPCCC), 2010 IEEE 29th International

Date of Conference:

9-11 Dec. 2010