Modelling parallel databases with process algebra

C. S. Pua, Howard Williams, D. H. Marwick

Research output: Contribution to journalArticle

Abstract

With the current interest in using parallel computers as database servers to provide a scaleable parallel application which satisfies a real commercial need, there is a corresponding interest in performance prediction of parallel database systems. Both analytical and simulation approaches have been used and reported in the literature. This paper reports on an investigation into how a stochastic extension to classical process algebra (performance evaluation process algebra, PEPA) may be used for this purpose. This paradigm has a small but powerful set of elements which offers great flexibility for performance modelling. The paper describes how the approach has been adapted to handle database models, including the development of a technique, the decompositional approach, to handle the state-space explosion of parallel database models. It concludes with a comparison between the results obtained using this approach and those obtained using a different analytical approach.

Original languageEnglish
Pages (from-to)1909-1924
Number of pages16
JournalParallel Computing
Volume26
Issue number13-14
DOIs
Publication statusPublished - Dec 2000

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Pua, C. S., Williams, H., & Marwick, D. H. (2000). Modelling parallel databases with process algebra. Parallel Computing, 26(13-14), 1909-1924. https://doi.org/10.1016/S0167-8191(00)00059-4
Pua, C. S. ; Williams, Howard ; Marwick, D. H. / Modelling parallel databases with process algebra. In: Parallel Computing. 2000 ; Vol. 26, No. 13-14. pp. 1909-1924.
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Pua, CS, Williams, H & Marwick, DH 2000, 'Modelling parallel databases with process algebra', Parallel Computing, vol. 26, no. 13-14, pp. 1909-1924. https://doi.org/10.1016/S0167-8191(00)00059-4

Modelling parallel databases with process algebra. / Pua, C. S.; Williams, Howard; Marwick, D. H.

In: Parallel Computing, Vol. 26, No. 13-14, 12.2000, p. 1909-1924.

Research output: Contribution to journalArticle

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