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Thursday, January 6, 2022

"Performance Anomaly and Change Point Detection for Large-Scale System Management" - my paper published at Springer

 


Intelligent Sustainable Systems pp 403-407Cite as

Performance Anomaly and Change Point Detection for Large-Scale System Management

Conference paper
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Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 334)

Abstract

The presentation starts with the short overview of the classical statistical process control (SPC)-based anomaly detection techniques and tools including Multivariate Adaptive Statistical Filtering (MASF); Statistical Exception and Trend Detection System (SETDS), Exception Value (EV) meta-metric-based change point detection; control charts; business driven massive prediction and methods of using them to manage large-scale systems such as on-prem servers fleet or massive clouds. Then, the presentation is focused on modern techniques of anomaly and normality detection, such as deep learning and entropy-based anomalous pattern detections.

Keywords

Anomaly detection Change point detection Business driven forecast Control chart Deep Learning Entropy analysis 

References

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    Trubin, I., et al.: Systems and methods for modeling computer resource metrics. US Patent 10,437,697 (2016)Google Scholar
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    Trubin, I.: Capturing workload pathology by statistical exception detection. In: Proceedings of Computer Measurement Group (2005)Google Scholar
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    Loboz, C.: Quantifying imbalance in computer systems. In: Proceedings of Computer Measurement Group (2011)Google Scholar

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