# Adaptive Performance Anomaly Detection for Online Service Systems via Pattern Sketching

arXiv: https://arxiv.org/abs/2201.02944 · HTML: https://mlsanity.com/paper/2201.02944/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2022.

## Built on by (most cited first, 13 of 13)

- [TFAD: A Decomposition Time Series Anomaly Detection Architecture with Time-Frequency Analysis](https://mlsanity.com/paper/2210.09693/) (2022)
- [TimeSeriesBench: An Industrial-Grade Benchmark for Time Series Anomaly Detection Models](https://mlsanity.com/paper/2402.10802/) (2024)
- [Heterogeneous Anomaly Detection for Software Systems via Semi-supervised Cross-modal Attention](https://mlsanity.com/paper/2302.06914/) (2023)
- [METER: A Dynamic Concept Adaptation Framework for Online Anomaly Detection](https://mlsanity.com/paper/2312.16831/) (2023)
- [Multivariate Time Series Anomaly Detection by Capturing Coarse-Grained Intra- and Inter-Variate Dependencies](https://mlsanity.com/paper/2501.16364/) (2025)
- [Identifying Performance Issues in Cloud Service Systems Based on Relational-Temporal Features](https://mlsanity.com/paper/2307.10869/) (2023)
- [Robust Root Cause Diagnosis using In-Distribution Interventions](https://mlsanity.com/paper/2505.00930/) (2025)
- [A Survey of Time Series Anomaly Detection Methods in the AIOps Domain](https://mlsanity.com/paper/2308.00393/) (2023)
- [Practical Anomaly Detection over Multivariate Monitoring Metrics for Online Services](https://mlsanity.com/paper/2308.09937/) (2023)
- [Outage-Watch: Early Prediction of Outages using Extreme Event Regularizer](https://mlsanity.com/paper/2309.17340/) (2023)
- [KGroot: Enhancing Root Cause Analysis through Knowledge Graphs and Graph Convolutional Neural Networks](https://mlsanity.com/paper/2402.13264/) (2024)
- [EventADL: Open-Box Anomaly Detection and Localization Framework for Events in Cloud-Based Service Systems](https://mlsanity.com/paper/2605.00936/) (2026)
- [Evaluation of a Foundational Model and Stochastic Models for Forecasting Sporadic or Spiky Production Outages of High-Performance Machine Learning Services](https://mlsanity.com/paper/2507.01067/) (2025)

## Abstract (arXiv)

To ensure the performance of online service systems, their status is closely monitored with various software and system metrics. Performance anomalies represent the performance degradation issues (e.g., slow response) of the service systems. When performing anomaly detection over the metrics, existing methods often lack the merit of interpretability, which is vital for engineers and analysts to take remediation actions. Moreover, they are unable to effectively accommodate the ever-changing services in an online fashion. To address these limitations, in this paper, we propose ADSketch, an interpretable and adaptive performance anomaly detection approach based on pattern sketching. ADSketch achieves interpretability by identifying groups of anomalous metric patterns, which represent particular types of performance issues. The underlying issues can then be immediately recognized if similar patterns emerge again. In addition, an adaptive learning algorithm is designed to embrace unprecedented patterns induced by service updates or user behavior changes. The proposed approach is evaluated with public data as well as industrial data collected from a representative online service system in Huawei Cloud. The experimental results show that ADSketch outperforms state-of-the-art approaches by a significant margin, and demonstrate the effectiveness of the online algorithm in new pattern discovery. Furthermore, our approach has been successfully deployed in industrial practice.