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.
|Original language||English (US)|
|Title of host publication||The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||11|
|State||Published - Apr 19 2021|
|Event||2021 World Wide Web Conference, WWW 2021 - Ljubljana, Slovenia|
Duration: Apr 19 2021 → Apr 23 2021
|Name||The Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021|
|Conference||2021 World Wide Web Conference, WWW 2021|
|Period||4/19/21 → 4/23/21|
Bibliographical noteFunding Information:
This work was supported in part by National Natural Science Foundation of China under Grant 61572497, 61771469, 62072302 and 61960206002.
Â© 2021 ACM.
- Content Delivery Network
- Latent Variable Model
- Multivariate Anomaly Detection
- Static and Dynamic Factorization