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On clustering in database servers for supporting mobile clients

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Abstract

The use of mobile computers is gaining popularity. There is an increasing trend in the number of users with laptops, PDAs, and smart phones. Access to information repositories in the future will be dominated by mobile clients rather than traditional “fixed” clients. These mobile clients download information by periodically connecting to repositories of data stored in either databases or file systems. Such mobile clients constitute a new and different kind of workload and exhibit a different access pattern than seen in traditional client server systems. Though file systems have been modified to handle clients that can download information, disconnect, and later reintegrate, databases have not been redesigned to accommodate mobile clients. There is a need to support mobile clients in the context of client server databases. This paper is about organizing the database server to take into consideration the access patterns of mobile clients. We propose the concept of hoard attributes which capture these access patterns. Three different techniques for organizing data on the server based on the hoard attribute are presented. We argue that each technique is suited for a particular workload. The workload is a combination of requests from mobile clients and traditional clients. This reorganization also allows us to address issues of concurrency control, disconnection and replica control in mobile databases. We present simulation results that show the performance of server reorganization using hoard attributes. We also provide an elaborate discussion of issues resulting from this reorganization in this new paradigm taking into account both mobile and traditional clients.

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Authors and Affiliations

  1. Department of Computer Science, Rutgers University, New Brunswick, NJ, 08903, USA

    B.R. Badrinath & Shirish H. Phatak

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  1. B.R. Badrinath
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  2. Shirish H. Phatak
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Badrinath, B., Phatak, S.H. On clustering in database servers for supporting mobile clients. Cluster Computing 1, 149–159 (1998). https://doi.org/10.1023/A:1019081315033

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