CP-based cloud workload annotation as a preprocessing for anomaly detection using deep neural networks

Abstract : Over the last years, supervised learning has been a subject of great interest. However, in presence of unlabelled data, we face the problem of deep unsupervised learning. To overcome this issue in the context of anomaly detection in a cloud workload, we propose a method that relies on constraint programming (CP). After defining the notion of quasi-periodic extreme pattern in a time series, we propose an algorithm to acquire a CP model that is further used to annotate the cloud workload dataset. We finally propose a neural network model that learns from the annotated data to predict anomalies in a cloud workload. The relevance of the proposed method is shown by running simulations on real-world data traces and by comparing the accuracy of the predictions with those of a state of the art unsupervised learning algorithm.
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Communication dans un congrès
ITISE 2018 - International Conference onTime Series and Forecasting, Sep 2018, Granada, Spain. pp.1-12
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https://hal.archives-ouvertes.fr/hal-01882896
Contributeur : Gilles Madi Wamba <>
Soumis le : jeudi 27 septembre 2018 - 15:00:35
Dernière modification le : dimanche 28 octobre 2018 - 01:13:08

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Gilles Madi Wamba, Nicolas Beldiceanu. CP-based cloud workload annotation as a preprocessing for anomaly detection using deep neural networks. ITISE 2018 - International Conference onTime Series and Forecasting, Sep 2018, Granada, Spain. pp.1-12. 〈hal-01882896〉

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