A New Application of Machine Learning: Detecting Errors in Network Simulations | IEEE Conference Publication | IEEE Xplore

A New Application of Machine Learning: Detecting Errors in Network Simulations


Abstract:

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 clust...Show More

Abstract:

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.
Date of Conference: 11-14 December 2022
Date Added to IEEE Xplore: 23 January 2023
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Conference Location: Singapore

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