Anomaly Detection and Failure Root Cause Analysis in Microservice-Based Cloud Applications

  • Authors

    • Amina Diallo Horizon University, Tunisie, USA. Author
    • Noor-Al-Hassan Horizon University, Tunisie, USA. Author

    Published 2026-01-05

  • Anomaly Detection, Root Cause Analysis, Microservice Architectures, Cloud Applications, Causal Discovery, Graph-Based Analysis, Machine Learning, AI-Powered Observability

    Issue

    Vol. 1 No. 1 (2026)

    Section

    Articles

    How to Cite

    [1]
    A. Diallo and N. Al-Hassan, “Anomaly Detection and Failure Root Cause Analysis in Microservice-Based Cloud Applications”, IJAIDT, vol. 1, no. 1, pp. 29–33, Jan. 2026, Accessed: Mar. 02, 2026. [Online]. Available: https://worldcometresearchgroup.com/index.php/ijaidt/article/view/69

  • Abstract

    Microservice-based cloud applications, characterized by their distributed and dynamic nature, often face challenges in maintaining performance and reliability. Detecting anomalies and accurately identifying their root causes are critical for ensuring system stability and user satisfaction. This paper provides a comprehensive survey of existing techniques for anomaly detection and failure root cause analysis in microservice architectures. We categorize these methods based on their approaches, such as causal discovery, graph-based analysis, machine learning, and AI-powered observability. Furthermore, we discuss the challenges inherent in diagnosing failures within complex microservice ecosystems and highlight potential research directions to address these challenges.

  • References

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