Sensitivity Analysis and Uncertainty Quantification of State-Based Discrete-Event Simulation Models Through a Stacked Ensemble of Metamodels

Michael Rausch, William H. Sanders

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Realistic state-based discrete-event simulation models are often quite complex. The complexity frequently manifests in models that (a) contain a large number of input variables whose values are difficult to determine precisely, and (b) take a relatively long time to solve. Traditionally, models that have a large number of input variables whose values are not well-known are understood through the use of sensitivity analysis (SA) and uncertainty quantification (UQ). However, it can be prohibitively time consuming to perform SA and UQ. In this work, we present a novel approach we developed for performing fast and thorough SA and UQ on a metamodel composed of a stacked ensemble of regressors that emulates the behavior of the base model. We demonstrate the approach using a previously published botnet model as a test case, showing that the metamodel approach is several orders of magnitude faster than the base model, more accurate than existing approaches, and amenable to SA and UQ.

Original languageEnglish (US)
Title of host publicationQuantitative Evaluation of Systems - 17th International Conference, QEST 2020, Proceedings
EditorsMarco Gribaudo, David N. Jansen, Anne Remke
PublisherSpringer Science and Business Media Deutschland GmbH
Pages276-293
Number of pages18
ISBN (Print)9783030598532
DOIs
StatePublished - 2020
Event17th International Conference on Quantitative Evaluation Systems, QEST 2020 - Vienna, Austria
Duration: Aug 31 2020Sep 3 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12289 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference17th International Conference on Quantitative Evaluation Systems, QEST 2020
CountryAustria
CityVienna
Period8/31/209/3/20

Keywords

  • Emulators
  • Metamodels
  • Optimization
  • Reliability models
  • Security models
  • Sensitivity analysis
  • Surrogate models
  • Uncertainty quantification

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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