SDFVAE: Static and dynamic factorized vae for anomaly detection of multivariate CDN KPIs

Liang Dai, Tao Lin, Chang Liu, Bo Jiang, Yanwei Liu, Zhen Xu, Zhi Li Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationThe Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021
PublisherAssociation for Computing Machinery, Inc
Pages3076-3086
Number of pages11
ISBN (Electronic)9781450383127
DOIs
StatePublished - Apr 19 2021
Event2021 World Wide Web Conference, WWW 2021 - Ljubljana, Slovenia
Duration: Apr 19 2021Apr 23 2021

Publication series

NameThe Web Conference 2021 - Proceedings of the World Wide Web Conference, WWW 2021

Conference

Conference2021 World Wide Web Conference, WWW 2021
Country/TerritorySlovenia
CityLjubljana
Period4/19/214/23/21

Bibliographical note

Funding Information:
This work was supported in part by National Natural Science Foundation of China under Grant 61572497, 61771469, 62072302 and 61960206002.

Publisher Copyright:
© 2021 ACM.

Keywords

  • Content Delivery Network
  • Latent Variable Model
  • Multivariate Anomaly Detection
  • Static and Dynamic Factorization

Fingerprint

Dive into the research topics of 'SDFVAE: Static and dynamic factorized vae for anomaly detection of multivariate CDN KPIs'. Together they form a unique fingerprint.

Cite this