- Saturday, Oct 17th, 2026
| TITLE | Intelligent Observability and Predictive Failure Detection for Large-Scale Cloud-Native Enterprise Systems |
|---|---|
| ABSTRACT | Large-scale cloud-native enterprise systems are increasingly composed of distributed microservices, containerized workloads, event-driven components, managed cloud services, and dynamically scaled infrastructure. While these architectural patterns improve scalability and deployment flexibility, they also introduce substantial operational complexity. Traditional monitoring approaches that rely primarily on isolated infrastructure metrics and threshold-based alerts are often insufficient for detecting subtle degradation patterns, cascading failures, and emerging service dependencies. Intelligent observability extends conventional monitoring by correlating metrics, logs, traces, events, and contextual operational data to create a more comprehensive understanding of system behavior. This article presents a generalized architectural approach for intelligent observability and predictive failure detection in large-scale cloud-native enterprise environments. The proposed framework combines distributed telemetry collection, centralized observability pipelines, service dependency analysis, anomaly detection, event correlation, and machine-learning-based predictive analytics. Rather than responding only after operational thresholds are violated, the approach focuses on identifying behavioral deviations and failure precursors before they develop into significant service disruptions. The architecture also incorporates topology awareness, historical incident knowledge, automated root-cause assistance, and reliability-oriented feedback mechanisms to improve operational decision making. The article further examines the role of scalable telemetry processing, dynamic baselining, multivariate anomaly detection, predictive risk scoring, and automated remediation within modern enterprise operations. Key implementation considerations including telemetry volume, model accuracy, explainability, alert fatigue, system heterogeneity, security, and governance are also discussed. The resulting framework demonstrates how observability can evolve from reactive monitoring toward a proactive reliability capability that supports resilient, scalable, and continuously operating cloud-native enterprise systems. |
| AUTHOR | Rishith Chinthamani Software Engineer, Walmart Global Tech, USA |
| VOLUME | 13 |
| DOI | DOI:10.15680/IJARETY.2026.1301016 |
| 16_Intelligent Observability and Predictive Failure Detection for Large-Scale Cloud-Native Enterprise Systems.pdf | |
| KEYWORDS | |
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