Anomaly Detection and Root Cause Analysis in Cloud Infrastructure Using GNN-LSTM-VAE

@article{1715888,
      author = {Md Faizan Shariff, Alok Kumar Dash, Anita Kumari Sahu, Kunal Raulo, Sunil Kumar Nahak},
      title = {Anomaly Detection and Root Cause Analysis in Cloud Infrastructure Using GNN-LSTM-VAE},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {10},
      pages = {171-177},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1715888.pdf},
      abstract = {Today's microservice-based architectures produce significant amounts of telemetry data, and detecting anomalies and performing root cause analysis have become challenging tasks. The conventional approach to monitoring does not effectively identify the dynamic relationships between microservices and temporal patterns of system behavior. This paper presents a spatiotemporal anomaly detection technique that utilizes a combination of Graph Neural Networks, Long Short-Term Memory, and Variational Autoencoders. The proposed technique effectively detects anomalies and identifies the reasons behind them. Additionally, this paper introduces application-level features, such as HTTP request information, to provide a better understanding of the reasons behind anomalies. The results of the experiments prove that the proposed technique effectively detects anomalies and identifies the reasons behind them. Index Terms—Anomaly Detection, GNN, LSTM, VAE, Microservices, Root Cause Analysis.},
      keywords = {Anomaly Detection, Microservices, Telemetry Data, Root Cause Analysis, Spatiotemporal Analysis, Graph Neural Networks (GNN), Long Short-Term Memory (LSTM), Variational Autoencoders (VAE), Dynamic Relationships Temporal Patterns ,Application-level Features HTTP Request Data, System Behaviour Monitoring},
      month = {April},
      doi = {https://doi.org/10.64388/IREV9I10-1715888}
  }