Benchmarking Deep Learning Models for Anomaly Detection in Real-World Cloud Telemetry

Abstract:Modern cloud platforms generate high-dimensional, noisy telemetry from thousands of microservices. Unlike classical anomaly detection benchmarks, cloud anomalies are short-lived, irregular, and largely non-periodic, making timely detection challenging. We present an empirical benchmark of deep learning models for anomaly detection using a real-world IBM Cloud Console dataset. We evaluate GRU, Transformer, TimesNet, and Autoformer architectures using the Numenta Anomaly Benchmark (NAB), a timing-aware metric that accounts for early detection and false alarms. TimesNet achieves the highest NAB scores due to strong early-detection sensitivity. Transformer models also detect anomalies early but incur higher false-positive rates. An improved GRU autoencoder, incorporating robust scoring and persistence rules, achieves competitive performance with a more stable false-alarm profile. Overall, the modest performance differences suggest that real cloud anomalies are dominated by short-term deviations rather than persistent periodic structure, emphasizing the importance of timing-aware evaluation and robust post-processing.

Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC)

arXiv:2610.15818 [cs.LG]

(or arXiv:2610.15818v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.15818 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

2026 11th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA), Chengdu, China, 2026, pp. 709-714

https://doi.org/10.1109/ICCCBDA69573.2026.11676496 Focus to learn more DOI(s) linking to related resources

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