Modelling parallel oracle for performance prediction

Euan W. Dempster; N. T. Tomov; M. H. Williams; H. Taylor; A. Burger; P. Trinder; J. Lü; P. Broughton

doi:10.1023/A:1022875525392

Modelling parallel oracle for performance prediction

Euan W. Dempster, N. T. Tomov, M. H. Williams, H. Taylor, A. Burger, P. Trinder, J. Lü, P. Broughton

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

Abstract

The problem of predicting the performance of a parallel relational DBMS for a set of queries applied to a particular data set on a shared nothing parallel architecture without transferring the application to a parallel system is a challenging one. An analytical approach has been developed to assist with this task and has been applied to the ICL GoldRush machine, a parallel machine with a shared-nothing architecture. This paper describes how the Oracle Parallel Server and the Parallel Query Option are modelled by the method and compares the predictions of the model against actual measurements obtained.

Original language	English
Pages (from-to)	251-269
Number of pages	19
Journal	Distributed and Parallel Databases
Volume	13
Issue number	3
DOIs	https://doi.org/10.1023/A:1022875525392
Publication status	Published - May 2003
Externally published	Yes

Keywords

Analytical modelling
Parallel Oracle
Performance prediction
Parallel database

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10.1023/A:1022875525392

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AB - The problem of predicting the performance of a parallel relational DBMS for a set of queries applied to a particular data set on a shared nothing parallel architecture without transferring the application to a parallel system is a challenging one. An analytical approach has been developed to assist with this task and has been applied to the ICL GoldRush machine, a parallel machine with a shared-nothing architecture. This paper describes how the Oracle Parallel Server and the Parallel Query Option are modelled by the method and compares the predictions of the model against actual measurements obtained.

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Modelling parallel oracle for performance prediction

Abstract

Keywords

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