research-article

Automatic Runtime Adaptation for Component-Based Simulation Algorithms

Authors:

Stefan Rybacki,

Adelinde M. UhrmacherAuthors Info & Claims

ACM Transactions on Modeling and Computer Simulation (TOMACS), Volume 26, Issue 1

Article No.: 7, Pages 1 - 24

https://doi.org/10.1145/2821509

Published: 19 October 2015 Publication History

Abstract

The state and structure of a model may vary during a simulation and, thus, also its computational demands. Adapting simulation algorithms to these demands at runtime can therefore improve their performance. While this is a general and cross-cutting concern, only few simulation systems offer reusable support for this kind of runtime adaptation. We present a flexible and generic mechanism for the runtime adaptation of component-based simulation algorithms. It encapsulates simulation algorithms applicable to a given problem and employs reinforcement learning to explore the algorithms’ performance during a simulation run. We evaluate our approach on a modeling formalism from computational biology and on a benchmark model defined in PDEVS, thereby investigating a broad range of options for improving its learning capabilities.

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Cited By

Uhrmacher AFrazier PHähnle RKlügl FLorig FLudäscher BNenzi LRuiz-Martin CRumpe BSzabo CWainer GWilsdorf P(2024)Context, Composition, Automation, and Communication: The C2AC Roadmap for Modeling and SimulationACM Transactions on Modeling and Computer Simulation10.1145/367322634:4(1-51)Online publication date: 13-Aug-2024
https://dl.acm.org/doi/10.1145/3673226
Köster TWarnke TUhrmacher A(2022)Generating Fast Specialized Simulators for Stochastic Reaction Networks via Partial EvaluationACM Transactions on Modeling and Computer Simulation10.1145/348546532:2(1-25)Online publication date: 4-Mar-2022
https://dl.acm.org/doi/10.1145/3485465
Wilsdorf PFischer NHaack FUhrmacher A(2021)Exploiting provenance and ontologies in supporting best practices for simulation experimentsProceedings of the Winter Simulation Conference10.5555/3522802.3522965(1-12)Online publication date: 13-Dec-2021
https://dl.acm.org/doi/10.5555/3522802.3522965
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Index Terms

Automatic Runtime Adaptation for Component-Based Simulation Algorithms
1. Computing methodologies
  1. Machine learning
  2. Modeling and simulation
    1. Simulation support systems

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Published In

cover image ACM Transactions on Modeling and Computer Simulation

ACM Transactions on Modeling and Computer Simulation Volume 26, Issue 1

Special Issue on PADS

December 2015

210 pages

ISSN:1049-3301

EISSN:1558-1195

DOI:10.1145/2798338

Editor:
Adelinde M. Uhrmacher
University of Rostock, Germany

Issue’s Table of Contents

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 19 October 2015

Accepted: 01 September 2015

Revised: 01 May 2015

Received: 01 January 2014

Published in TOMACS Volume 26, Issue 1

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Cited By

Uhrmacher AFrazier PHähnle RKlügl FLorig FLudäscher BNenzi LRuiz-Martin CRumpe BSzabo CWainer GWilsdorf P(2024)Context, Composition, Automation, and Communication: The C2AC Roadmap for Modeling and SimulationACM Transactions on Modeling and Computer Simulation10.1145/367322634:4(1-51)Online publication date: 13-Aug-2024
https://dl.acm.org/doi/10.1145/3673226
Köster TWarnke TUhrmacher A(2022)Generating Fast Specialized Simulators for Stochastic Reaction Networks via Partial EvaluationACM Transactions on Modeling and Computer Simulation10.1145/348546532:2(1-25)Online publication date: 4-Mar-2022
https://dl.acm.org/doi/10.1145/3485465
Wilsdorf PFischer NHaack FUhrmacher A(2021)Exploiting provenance and ontologies in supporting best practices for simulation experimentsProceedings of the Winter Simulation Conference10.5555/3522802.3522965(1-12)Online publication date: 13-Dec-2021
https://dl.acm.org/doi/10.5555/3522802.3522965
Wilsdorf PFischer NHaack FUhrmacher A(2021)Exploiting Provenance and Ontologies In Supporting Best Practices For Simulation Experiments: A Case Study On Sensitivity Analysis2021 Winter Simulation Conference (WSC)10.1109/WSC52266.2021.9715362(1-12)Online publication date: 12-Dec-2021
https://doi.org/10.1109/WSC52266.2021.9715362
Lynch CDiallo SKavak HPadilla J(2020)A content analysis-based approach to explore simulation verification and identify its current challengesPLOS ONE10.1371/journal.pone.023292915:5(e0232929)Online publication date: 13-May-2020
https://doi.org/10.1371/journal.pone.0232929
Helms TWarnke TUhrmacher A(2019)Multi-Level Modeling and Simulation of Cellular Systems: An Introduction to ML-RulesModeling Biomolecular Site Dynamics10.1007/978-1-4939-9102-0_6(141-160)Online publication date: 4-Apr-2019
https://doi.org/10.1007/978-1-4939-9102-0_6
Helms TWarnke TMaus CUhrmacher A(2017)Semantics and Efficient Simulation Algorithms of an Expressive Multilevel Modeling LanguageACM Transactions on Modeling and Computer Simulation10.1145/299849927:2(1-25)Online publication date: 18-May-2017
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Pierce MWarnke TKrumme UHelms THammer CUhrmacher A(2017)Developing and validating a multi-level ecological model of eastern Baltic cod ( Gadus morhua ) in the Bornholm Basin – A case for domain-specific languagesEcological Modelling10.1016/j.ecolmodel.2017.07.012361(49-65)Online publication date: Oct-2017
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Bock CDandashi FFriedenthal SHarrison NJenkins SMcGinnis LSztipanovits JUhrmacher AWeisel EZhang L(2017)Conceptual ModelingResearch Challenges in Modeling and Simulation for Engineering Complex Systems10.1007/978-3-319-58544-4_3(23-44)Online publication date: 19-Aug-2017
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Warnke TReinhardt OUhrmacher AHuschka TChick S(2016)Population-based CTMCS and agent-based modelsProceedings of the 2016 Winter Simulation Conference10.5555/3042094.3042258(1253-1264)Online publication date: 11-Dec-2016
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