Adaptive feature selection for predicting application performance degradation in edge cloud environments

Applications deployed in edge cloud environments can have stringent requirements such as high throughput and high availability. However, they may suffer from performance degradation caused by infrastructure-related faults or other underlying factors, so predicting and proactively handling application performance degradation is critical. Machine learning models can be used for this – but the performance of the models can degrade over time due to feature drift.

Research paper

#FaultPrediction #FeatureSelection #EricssonResearch

Feature drift means changes in the relevancy of features used for training the ML model for application performance degradation. In this paper, we predict application performance degradation in edge clouds and propose a framework for adapting to the feature drifts that may occur in this environment.

Full abstract in IEEEXplore DOI: 10.1109/TNSM.2024.3462831

Authors

Behshid Shayesteh, CIISE, Concordia University, Montréal, Canada

Chunyan Fu, Ericsson Research

Amin Ebrahimzadeh, CIISE, Concordia University, Montréal, Canada

Roch H. Glitho, CIISE, Concordia University, Montréal, Canada and University of Western Cape, Cape Town, South Africa

Published in: IEEE Transactions on Network and Service Management, Sep 2024

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Adaptive feature selection for predicting application performance degradation in edge cloud environments

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