Time-Series Deep Learning for Proactive Server Reliability Management

  • Authors

    • Dr. Grace Ndlovu Associate Professor University of Pretoria, South Africa. Author
    • Samuel Johnson Senior Operations Manager MTN Group, South Africa. Author

    DOI:

    https://doi.org/10.67228/3142788X/IJMLPA-2022PI8N5K

    Published 02-03-2022

  • Time-Series Forecasting, Deep Learning, Server Reliability Management, Predictive Maintenance, Anomaly Detection, Lstm (Long Short-Term Memory), Temporal Convolutional, Networks, Transformer Models, Telemetry Analytics

    Issue

    Vol. 5 No. 1 (2022)

    Section

    Articles

    How to Cite

    [1]
    G. Ndlovu and S. Johnson, “Time-Series Deep Learning for Proactive Server Reliability Management”, IJMLPA, vol. 5, no. 1, pp. 01–12, Feb. 2022, doi: 10.67228/3142788X/IJMLPA-2022PI8N5K.

  • Abstract

    Modern server systems generate massive volumes of time-series operational data including metrics such as CPU utilization, memory usage, I/O throughput, and network latency. Traditional threshold-based monitoring techniques struggle to capture latent temporal dependencies and evolving performance trends, which often leads to delayed detection of anomalies and reactive maintenance strategies. This paper presents a deep learning-based time-series prediction framework designed to proactively anticipate server reliability issues before they escalate into critical failures. We leverage advanced architectures such as Long Short-Term Memory (LSTM), Temporal Convolutional Networks (TCN), and Transformer encoders to model complex temporal patterns from multivariate telemetry streams. Our approach incorporates adaptive retraining, feature engineering, and explainability methods to balance predictive accuracy with operational interpretability. Experimental results on real-world server telemetry datasets demonstrate significant improvements in early fault detection, reducing mean time to detection (MTTD) and enabling automated preemptive actions. We further discuss integration strategies within DevOps pipelines to transition from reactive monitoring to proactive reliability management.

  • References

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