AI/ML techniques for predictive maintenance of cloud infrastructure
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DOI:
https://doi.org/10.67228/30713315/IJAIDT-2018PI9V6TPublished 04-05-2018
Predictive Maintenance, Cloud Infrastructure, Machine Learning, Anomaly Detection, Data Center Reliability, Time Series Forecasting, Fault Detection, Lstm, Edge Monitoring, Operational AI Issue
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ArticlesHow to Cite
[1]R. T, “AI/ML techniques for predictive maintenance of cloud infrastructure”, IJAIDT, vol. 1, no. 1, pp. 01–11, Apr. 2018, doi: 10.67228/30713315/IJAIDT-2018PI9V6T.Abstract
Cloud infrastructure plays a pivotal role in delivering uninterrupted computing services across the globe. However, unexpected failures in critical components such as cooling systems, networking hardware, and power supply units can result in significant downtime and economic loss. This paper investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques for predictive maintenance in cloud data centers. By analyzing historical sensor data, system logs, and performance metrics, various ML models—ranging from classical approaches like Random Forests to deep learning models such as LSTM and Autoencoders—are deployed to predict potential failures and trigger maintenance alerts. The paper also explores challenges like data imbalance, real-time inference, and deployment at scale in cloud environments. Experimental evaluations demonstrate the efficacy of ML models in identifying fault patterns, thereby improving operational reliability, reducing maintenance costs, and minimizing service disruptions.
References
[1] Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/10.1023/A:1010933404324
[2] Chen, T., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD, 785–794. https://doi.org/10.1145/2939672.2939785
[3] Sakurada, M., & Yairi, T. (2014). Anomaly detection using autoencoders with nonlinear dimensionality reduction. Proceedings of MLSDA, 4–11. https://doi.org/10.1145/2689746.2689747
[4] Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2008). Isolation forest. IEEE ICDM, 413–422. https://doi.org/10.1109/ICDM.2008.17
[5] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
[6] Cho, K., et al. (2014). Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078. https://arxiv.org/abs/1406.1078
[7] Sculley, D., et al. (2015). Hidden technical debt in machine learning systems. NeurIPS, 28. https://papers.nips.cc/paper_files/paper/2015/hash/86df7dcfd896fcaf2674f757a2463eba-Abstract.html
[8] Zaharia, M., et al. (2016). Apache Spark: A unified engine for big data processing. Communications of the ACM, 59(11), 56–65. https://doi.org/10.1145/2934664
[9] Satyanarayanan, M. (2017). The emergence of edge computing. Computer, 50(1), 30–39. https://doi.org/10.1109/MC.2017.9
[10] Mao, H., et al. (2016). Resource management with deep reinforcement learning. HotNets XV. https://doi.org/10.1145/2987550.2987555
[11] Ahmad, S., et al. (2017). Unsupervised real-time anomaly detection for streaming data. Neurocomputing, 262, 134–147. https://doi.org/10.1016/j.neucom.2017.04.070
[12] Feki, M. A., et al. (2017). Smart factory architecture for predictive maintenance. Procedia Computer Science, 109, 1150–1155. https://doi.org/10.1016/j.procs.2017.05.446
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How to Cite
[1]R. T, “AI/ML techniques for predictive maintenance of cloud infrastructure”, IJAIDT, vol. 1, no. 1, pp. 01–11, Apr. 2018, doi: 10.67228/30713315/IJAIDT-2018PI9V6T.