kth.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
A New Application of Machine Learning: Detecting Errors in Network Simulations
KTH, School of Electrical Engineering and Computer Science (EECS), Intelligent systems, Robotics, Perception and Learning, RPL.ORCID iD: 0000-0002-3432-6151
Miami University, Department of Computer Science & Software Engineering, St. Oxford, OH, USA..
Miami University, Department of Computer Science & Software Engineering, St. Oxford, OH, USA..
Miami University, Department of Computer Science & Software Engineering, St. Oxford, OH, USA..
Number of Authors: 42022 (English)In: Proceedings of the 2022 Winter Simulation Conference, WSC 2022, Institute of Electrical and Electronics Engineers (IEEE) , 2022, p. 653-664Conference paper, Published paper (Refereed)
Abstract [en]

After designing a simulation and running it locally on a small network instance, the implementation can be scaled-up via parallel and distributed computing (e.g., a cluster) to cope with massive networks. However, implementation changes can create errors (e.g., parallelism errors), which are difficult to identify since the aggregate behavior of an incorrect implementation of a stochastic network simulation can fall within the distributions expected from correct implementations. In this paper, we propose the first approach that applies machine learning to traces of network simulations to detect errors. Our technique transforms simulation traces into images by reordering the network's adjacency matrix, and then training supervised machine learning models. Our evaluation on three simulation models shows that we can easily detect previously encountered types of errors and even confidently detect new errors. This work opens up numerous opportunities by examining other simulation models, representations (i.e., matrix reordering algorithms), or machine learning techniques.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE) , 2022. p. 653-664
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:kth:diva-333413DOI: 10.1109/WSC57314.2022.10015484ISI: 000991872900054Scopus ID: 2-s2.0-85147416696OAI: oai:DiVA.org:kth-333413DiVA, id: diva2:1785192
Conference
2022 Winter Simulation Conference, WSC 2022, Guilin, China, Dec 11 2022 - Dec 14 2022
Note

Part of ISBN 9798350309713

QC 20230801

Available from: 2023-08-01 Created: 2023-08-01 Last updated: 2023-09-04Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Wozniak, Maciej K.

Search in DiVA

By author/editor
Wozniak, Maciej K.
By organisation
Robotics, Perception and Learning, RPL
Computer Systems

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 11 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf