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Multi-Dimensional Anomaly Detection and Fault Localization in Microservice Architectures: A Dual-Channel Deep

Suchuan Xing1, Yihan Wang2, Wenhe Liu3

  • 1Department of Electrical and Computer Engineering, Duke University, Durham, NC 27708, USA.

Sensors (Basel, Switzerland)
|September 19, 2025
PubMed
Summary

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Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
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This study introduces a dual-channel deep learning framework for anomaly detection and fault localization in complex data centers. The novel approach enhances system monitoring and reduces troubleshooting time by intelligently analyzing service metrics.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Data Center Management

Background:

  • Modern data centers utilize complex microservice architectures, increasing the difficulty of anomaly detection and fault localization.
  • Traditional monitoring tools face limitations with heterogeneous metrics, temporal correlations, and precise root cause analysis in distributed systems.

Purpose of the Study:

  • To propose a novel dual-channel deep learning framework for enhanced anomaly detection and fault localization in microservice environments.
  • To address the challenges posed by complex system metrics and temporal dependencies in data center monitoring.

Main Methods:

  • Integration of Temporal Convolutional Networks (TCNs) and Variational Autoencoders (VAEs) within a dual-channel deep learning framework.
  • Application of contrastive learning for unified representation of diverse service metrics.
Keywords:
anomaly detectionfault localizationmicroservice architecturemonitoring systemperformance sensing

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  • Incorporation of causal inference mechanisms for tracing fault propagation paths.
  • Utilizing a semi-supervised learning approach with both labeled and unlabeled data.

    • Main Results:

    • Achieved 95.4% accuracy in anomaly detection and 87.6% precision in fault component localization.
    • Demonstrated a 43% reduction in average troubleshooting time and a 31% decrease in false localization rates.
    • Maintained computational efficiency suitable for real-time monitoring applications.

    Conclusions:

    • The proposed deep learning framework effectively identifies and precisely localizes anomalies in complex microservice environments.
    • Intelligent sensing of system metrics enables proactive maintenance strategies, minimizing service disruptions.
    • The approach offers significant improvements over state-of-the-art methods in data center anomaly management.