Paper Info
Title
SDFVAE: Static and Dynamic Factorized VAE for Anomaly Detection of Multivariate CDN KPIs
Abstract
ABSTRACT Content Delivery Networks (CDNs) are critical for providing good user experience of cloud services. CDN providers typically collect various multivariate Key Performance Indicators (KPIs) time series to monitor and diagnose system performance. State-of-the-art anomaly detection methods mostly use deep learning to extract the normal patterns of data, due to its superior performance. However, KPI data usually exhibit non-additive Gaussian noise, which makes it difficult for deep learning models to learn the normal patterns, resulting in degraded performance in anomaly detection. In this paper, we propose a robust and noise-resilient anomaly detection mechanism using multivariate KPIs. Our key insight is that different KPIs are constrained by certain time-invariant characteristics of the underlying system, and that explicitly modelling such invariance may help resist noise in the data. We thus propose a novel anomaly detection method called SDFVAE, short for Static and Dynamic Factorized VAE, that learns the representations of KPIs by explicitly factorizing the latent variables into dynamic and static parts. Extensive experiments using real-world data show that SDFVAE achieves a F1-score ranging from 0.92 to 0.99 on both regular and noisy dataset, outperforming state-of-the-art methods by a large margin.
Year
DOI
Venue
2021
10.1145/3442381.3450013
International World Wide Web Conference
Keywords
DocType
Citations 
Multivariate Anomaly Detection, Content Delivery Network, Static and Dynamic Factorization, Latent Variable Model
Conference
6
PageRank 
References 
Authors
0.43
0
7
Name
Order
Citations
PageRank
Liang Dai181.83
Tao Lin260.43
Chang Liu3571117.41
Bo Jiang4879.66
Yanwei Liu57014.92
Zhen Xu62117.33
Zhi-Li Zhang7303.99