International Journal of Science, Engineering and TechnologyAuthors: Vinay Kumar Reddy Vangoor
Abstract: Enterprise Linux environments form the operational foundation of modern digital infrastructure, hosting mission-critical workloads across bare metal servers, virtual machines, containers, and hybrid cloud deployments. At scale, these environments generate monitoring data volumes that dwarf human capacity to analyse effectively. A mid-sized enterprise operating 500 Linux hosts generates upward of five million metric data points and two terabytes of log data daily, yet the alerting systems governing incident response remain predominantly static: threshold-based rules authored once and applied uniformly, regardless of the dynamic behaviour of the systems they monitor. This rigidity produces two simultaneous failure modes that collectively define the enterprise monitoring crisis: alert fatigue, caused by excessive false positives from thresholds that fire on normal variance; and missed detections, caused by thresholds that cannot capture the complex, multi-dimensional patterns characteristic of real system failures. This research presents and evaluates an AI-driven monitoring and alerting framework for enterprise-scale Linux deployments that replaces static threshold rules with five cooperating machine learning models: an LSTM autoencoder for time-series metric anomaly detection, a transformer-based classifier for log stream anomaly detection, a graph neural network for root cause localisation across service dependency graphs, an XGBoost and ARIMA hybrid for predictive failure forecasting, and a clustering model for alert noise reduction and intelligent deduplication. The framework integrates with existing Prometheus, ELK Stack, and Grafana deployments without requiring infrastructure changes. Experimental evaluation across 540 controlled incident events on a 120-host Linux testbed demonstrates a 92.4% average anomaly detection accuracy versus 62.8% for threshold-based baselines, an 87% reduction in daily alert volume, a 12-fold improvement in mean time to detect critical incidents, and a predictive failure lead time of 7.8 hours at the end of the 12-month evaluation period. These results demonstrate that AI-driven monitoring is production-viable for enterprise Linux deployments and represents a step-change improvement over conventional alerting approaches.
DOI: https://doi.org/10.5281/zenodo.19183645
