CloudRCA
| Autor: | Liang Sun, Hengbo Liu, Junwei Jiang, Huajie Qian, Min Ke, Yingying Zhang, Lunting Fan, Zhengxiong Guan, Leili Xu, Qingsong Wen |
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| Rok vydání: | 2021 |
| Předmět: |
Signal Processing (eess.SP)
FOS: Computer and information sciences Computer Science - Machine Learning Service (systems architecture) business.industry Computer science Reliability (computer networking) Distributed computing Bayesian network Cloud computing Machine Learning (cs.LG) Software Engineering (cs.SE) Computer Science - Software Engineering Computer Science - Distributed Parallel and Cluster Computing FOS: Electrical engineering electronic engineering information engineering Systems architecture Anomaly detection Distributed Parallel and Cluster Computing (cs.DC) Performance indicator Electrical Engineering and Systems Science - Signal Processing business Root cause analysis |
| Zdroj: | CIKM |
| Popis: | As business of Alibaba expands across the world among various industries, higher standards are imposed on the service quality and reliability of big data cloud computing platforms which constitute the infrastructure of Alibaba Cloud. However, root cause analysis in these platforms is non-trivial due to the complicated system architecture. In this paper, we propose a root cause analysis framework called CloudRCA which makes use of heterogeneous multi-source data including Key Performance Indicators (KPIs), logs, as well as topology, and extracts important features via state-of-the-art anomaly detection and log analysis techniques. The engineered features are then utilized in a Knowledge-informed Hierarchical Bayesian Network (KHBN) model to infer root causes with high accuracy and efficiency. Ablation study and comprehensive experimental comparisons demonstrate that, compared to existing frameworks, CloudRCA 1) consistently outperforms existing approaches in f1-score across different cloud systems; 2) can handle novel types of root causes thanks to the hierarchical structure of KHBN; 3) performs more robustly with respect to algorithmic configurations; and 4) scales more favorably in the data and feature sizes. Experiments also show that a cross-platform transfer learning mechanism can be adopted to further improve the accuracy by more than 10\%. CloudRCA has been integrated into the diagnosis system of Alibaba Cloud and employed in three typical cloud computing platforms including MaxCompute, Realtime Compute and Hologres. It saves Site Reliability Engineers (SREs) more than $20\%$ in the time spent on resolving failures in the past twelve months and improves service reliability significantly. Comment: Accepted by CIKM 2021; 10 pages, 3 figures, 12 tables |
| Databáze: | OpenAIRE |
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