A modular method for evaluating the performance of picture archiving and communication systems

William H Sanders, Latha A. Kant, Abhijit Kudrimoti

Research output: Contribution to journalArticle

Abstract

Modeling can be used to predict the performance of picture archiving and communication system (PACS) configurations under various load conditions at an early design stage. This is important because choices made early in the design of a system can have a significant imapct on the performance of the resulting implementation. Because PACS consist of many types of components, it is important to do such evaluations in a modular manner, so that alternative configurations and designs can be easily investigated. Stochastic activity networks (SANs) and reduced base model construction methods can aid in doing this. SANs are a model type particularly suited to the evaluation of systems in which several activities may be in progress concurrently, and each activity may affect the others through the results of its completion. Together with SANs, reduced base model construction methods provide a means to build highly modular models, in which models of particular components can be easily reused. In this article, we investigate the use of SANs and reduced base model construction techniques in evaluating PACS. Construction and solution of the models is done using UltraSAN, a graphic-oriented software tool for model specification, analysis, and simulation. The method is illustrated via the evaluation of a realistically sized PACS for a typical United States hospital of 300 to 400 beds, and the derivation of system response times and component utilizations.

Original languageEnglish (US)
Pages (from-to)172-193
Number of pages22
JournalJournal of Digital Imaging
Volume6
Issue number3
DOIs
StatePublished - Aug 1 1993
Externally publishedYes

Keywords

  • performance evaluation
  • picture archiving and communication systems
  • stochastic Petri nets
  • stochastic activity networks

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications
  • Computer Vision and Pattern Recognition

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