Multi-Dimensional Anomaly Detection and Fault Localization in Microservice Architectures: A Dual-Channel Deep Learning Approach with Causal Inference for Intelligent Sensing
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Modern data centers face increasing complexity with distributed microservice architectures, making anomaly detection and fault localization challenging yet critical. Traditional monitoring sensor tools struggle with heterogeneous metrics, temporal correlations, and precise root cause analysis in these environments. This paper proposes a dual-channel deep learning framework that integrates Temporal Convolutional Networks with Variational Autoencoders to address these challenges. Our approach employs contrastive learning to create unified representations of diverse service metrics and incorporates causal inference mechanisms to trace fault propagation paths. We evaluated our framework using a semi-supervised learning approach that leveraged both labeled anomalies and abundant normal data, achieving 95.4% detection accuracy, 93.8% F1-score, and 87.6% precision in fault component localization. The system reduced the average troubleshooting time by 43% and false localization rates by 31% compared to state-of-the-art methods, while maintaining a computational efficiency suitable for real-time monitoring. These results demonstrate the effectiveness of our approach in identifying and precisely localizing anomalies in complex microservice environments through intelligent sensing of system metrics, enabling proactive maintenance strategies that minimize service disruptions.
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In production microservice settings, anomalies typically need to be detected within 2-5 minutes to prevent cascading failures and minimize service impact.
“This design choice is consistent with operational expectations that in production microservice settings, anomalies typically need to be detected within 2-5 minutes to prevent cascading failures and minimize service impact [41].”
From Toward Context-Aware Anomaly Detection for AIOps in Microservices Using Dynamic Knowledge Graphs (2026) · cites this paper as [41]
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- Authors
- Xing, S., Wang, Y., & Liu, W.
- Affiliations
- Duke University (US)
- University of Pennsylvania (US)
- Carnegie Mellon University (US)
- Corresponding author
- Wang, Yihan
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2025-05-28
- Volume
- 25
- Issue
- 11
- Pages
- 3396
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC12157131
- OpenAlex
- W4410818268
- Cited by
- 23
- References
- 43
- Citations by year
- 2026: 15 · 2025: 8
- FWCI
- 9.62
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software System Performance and Reliability
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Engineering › Control and Systems Engineering › Fault Detection and Control Systems
- Keywords
- anomaly detection, fault location, microservices, temporal convolutional network, variational autoencoder, contrastive learning, causal inference, semi-supervised learning, data center monitoring, smart sensing, root cause analysis
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