Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

10-2024

Abstract

To ensure the reliability of cloud systems, their run-time status reflecting the service quality is periodically monitored with monitoring metrics, i.e., KPIs (key performance indicators). When performance issues happen, root cause localization pinpoints the specific KPIs that are responsible for the degradation of overall service quality, facilitating prompt problem diagnosis and resolution. To this end, existing methods generally locate root-cause KPIs by identifying the KPIs that exhibit a similar anomalous trend to the overall service performance. While straightforward, solely relying on the similarity calculation may be ineffective when dealing with cloud systems with complicated interdependent services. Recent deep learning-based methods offer improved performance by modeling these intricate dependencies. However, their high computational demand often hinders their ability to meet the efficiency requirements of industrial applications. Furthermore, their lack of interpretability further restricts their practicality. To overcome these limitations, we propose KPIRoot, an effective and efficient method for root cause localization integrating both advantages of similarity analysis and causality analysis, where similarity measures the trend alignment of KPI and causality measures the sequential order of variation of KPI. Furthermore, we leverage symbolic aggregate approximation to produce a more compact representation for each KPI, enhancing the overall analysis efficiency of the approach. The experimental results show that KPIRoot outperforms seven state-of-the-art baselines by 7.9%~28.3%, while time cost is reduced by 56.9%. Moreover, we share our experience of deploying KPIRoot in the production environment of a large-scale cloud provider Cloud ${\mathcal{H}^{\ast}}$.

Discipline

Software Engineering

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

Proceedings of the 2024 IEEE 35th International Symposium on Software Reliability Engineering (ISSRE), Tsukuba, Japan, October 28-31

First Page

403

Last Page

414

Identifier

10.1109/ISSRE62328.2024.00046

Publisher

IEEE

City or Country

Pistacataway

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