To ensure the reliability of cloud systems, their performance is monitored using KPIs (key performance indicators). When issues arise, root cause localization identifies KPIs responsible for service degradation, aiding in quick diagnosis and resolution. Traditional methods rely on similarity calculations, which can be ineffective in complex, interdependent cloud environments. While deep learning-based approaches model these dependencies better, they often face challenges such as high computational demands and lack of interpretability. To address these issues, KPIRoot is proposed as an efficient method combining similarity and causality analysis. It uses symbolic aggregate approximation for compact KPI representation, improving analysis efficiency. However, deployment in Cloud H revealed two drawbacks: 1) threshold-based anomaly detection misses some performance anomalies, and 2) SAX representation fails to capture intricate variation trends. KPIRoot+ addresses these limitations, outperforming eight state-of-the-art baselines by 2.9% to 35.7%, while reducing time cost by 34.7%. We also share our experience deploying KPIRoot in a large-scale cloud provider's production environment.
@article{arxiv.2506.04569,
title = {KPIRoot+: An Efficient Integrated Framework for Anomaly Detection and Root Cause Analysis in Large-Scale Cloud Systems},
author = {Wenwei Gu and Renyi Zhong and Guangba Yu and Xinying Sun and Jinyang Liu and Yintong Huo and Zhuangbin Chen and Jianping Zhang and Jiazhen Gu and Yongqiang Yang and Michael R. Lyu},
journal= {arXiv preprint arXiv:2506.04569},
year = {2025}
}