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

References

[1] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997.

[2] P. Malhotra, L. Vig, G. Shroff, and P. Agarwal, “Long short-term memory networks for anomaly detection in time series,” in Proc. European Symposium on Artificial Neural Networks (ESANN), 2015.

[3] P. Malhotra, A. Ramakrishnan, G. Anand, L. Vig, P. Agarwal, and G. Shroff, “LSTM-based encoder-decoder for multi-sensor anomaly detection,” arXiv preprint arXiv:1607.00148, 2016.

[4] V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,” ACM Computing Surveys, vol. 41, no. 3, pp. 1–58, 2009.

[5] R. Chalapathy and S. Chawla, “Deep learning for anomaly detection: A survey,” ACM Computing Surveys, vol. 54, no. 2, pp. 1–38, 2019.

[6] G. E. Hinton and R. R. Salakhutdinov, “Reducing the dimensionality of data with neural networks,” Science, vol. 313, no. 5786, pp. 504–507, 2006.

[7] D. P. Kingma and M. Welling, “Auto- encoding variational Bayes,” in Proc. International Conference on Learning Representations (ICLR), 2014.

[8] F. Chollet, Deep Learning with Python. Manning Publications, 2018.

[9] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016.

[10] J. Brownlee, Deep Learning for Time Series Forecasting. Machine Learning Mastery, 2018.

[11] A. Lavin and S. Ahmad, “Evaluating real-time anomaly detection algorithms – the Numenta anomaly benchmark,” in Proc. IEEE International Conference on Machine Learning and Applications (ICMLA), 2015.

[12] H. Xu, Y. Chen, J. Zhao, and D. Xie, “Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in web applications,” in Proc. World Wide Web Conference (WWW), 2018.

[13] M. Goldstein and S. Uchida, “A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data,” PLoS ONE, vol. 11, no. 4, 2016.

[14] T. Ahmad, B. M. Khan, and C. A. Kerrache, “Network anomaly detection using deep learning: A systematic review,” IEEE Access, vol. 9, pp. 121174–121195, 2021.

[15] Amazon Web Services, “AWS Cloud Monitoring and Logging,” 2023. [Online]. Available: https://aws.amazon.com

[16] Google Cloud, “Cloud Monitoring and Operations Suite,” 2023. [Online]. Available: https://cloud.google.com

[17] Microsoft Azure, “Azure Monitor Documentation,” 2023. [Online]. Available: https://azure.microsoft.com

[18] Y. Su, Y. Zhao, C. Niu, R. Liu, W. Sun, and D. Pei, “Robust anomaly detection for multivariate time series through stochastic recurrent neural network,” in Proc. ACM SIGKDD, 2019.

[19] K. Hundman, V. Constantinou, C. Laporte, I. Colwell, and T. Soderstrom, “Detecting spacecraft anomalies using LSTMs and nonparametric dynamic thresholding,” in Proc. ACM SIGKDD, 2018.

[20] Z. Chen, C. K. Yeo, B. S. Lee, and C. T. Lau, “Autoencoder-based network anomaly detection,” in Proc. IEEE Wireless Communications and Networking Conference (WCNC), 2018.

@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}
  }