Maat: Performance Metric Anomaly Anticipation for Cloud Services with Conditional Diffusion

Ensuring the reliability and user satisfaction of cloud services necessitates prompt anomaly detection followed by diagnosis. Existing techniques for anomaly detection focus solely on real-time detection, meaning that anomaly alerts are issued as soon as anomalies occur. However, anomalies can propa...

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Vydané v:IEEE/ACM International Conference on Automated Software Engineering : [proceedings] s. 116 - 128
Hlavní autori: Lee, Cheryl, Yang, Tianyi, Chen, Zhuangbin, Su, Yuxin, Lyu, Michael R.
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 11.09.2023
Predmet:
Anomaly Anticipation
Could Computing
Denosing Diffusion Model
Feature extraction
Forestry
Measurement
Noise reduction
Performance Metric
Predictive models
Real-time systems
Reliability
ISSN:2643-1572
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Shrnutí:Ensuring the reliability and user satisfaction of cloud services necessitates prompt anomaly detection followed by diagnosis. Existing techniques for anomaly detection focus solely on real-time detection, meaning that anomaly alerts are issued as soon as anomalies occur. However, anomalies can propagate and escalate into failures, making faster-than-real-time anomaly detection highly desirable for expediting downstream analysis and intervention. This paper proposes Maat, the first work to address anomaly anticipation of performance metrics in cloud services. Maat adopts a novel two-stage paradigm for anomaly anticipation, consisting of metric forecasting and anomaly detection on forecasts. The metric forecasting stage employs a conditional denoising diffusion model to enable multi-step forecasting in an auto-regressive manner. The detection stage extracts anomaly-indicating features based on domain knowledge and applies isolation forest with incremental learning to detect upcoming anomalies. Thus, our method can uncover anomalies that better conform to human expertise. Evaluation on three publicly available datasets demonstrates that Maat can anticipate anomalies faster than real-time comparatively or more effectively compared with state-of-the-art real-time anomaly detectors. We also present cases highlighting Maat's success in forecasting abnormal metrics and discovering anomalies.
ISSN:2643-1572
DOI:10.1109/ASE56229.2023.00082