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A New Application of Machine Learning: Detecting Errors in Network Simulations
KTH, Skolan för elektroteknik och datavetenskap (EECS), Intelligenta system, Robotik, perception och lärande, 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..
Rekke forfattare: 42022 (engelsk)Inngår i: Proceedings of the 2022 Winter Simulation Conference, WSC 2022, Institute of Electrical and Electronics Engineers (IEEE) , 2022, s. 653-664Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Institute of Electrical and Electronics Engineers (IEEE) , 2022. s. 653-664
HSV kategori
Identifikatorer
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
Konferanse
2022 Winter Simulation Conference, WSC 2022, Guilin, China, Dec 11 2022 - Dec 14 2022
Merknad

Part of ISBN 9798350309713

QC 20230801

Tilgjengelig fra: 2023-08-01 Laget: 2023-08-01 Sist oppdatert: 2023-09-04bibliografisk kontrollert

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Wozniak, Maciej K.

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