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Autonomous Service Reliability Engineering Using AI-Driven Observability in Enterprise Cloud Platforms

Vincent PINET

Groupe Vicat, Clermont-Ferrand, Auvergne-Rhône-Alpes, France

Abstract

Enterprise cloud platforms have become increasingly complex as organizations adopt microservices, containers, serverless applications, multi-cloud architectures, and continuously delivered software. This complexity makes conventional service reliability engineering increasingly dependent on large volumes of telemetry and continuous human intervention. Traditional monitoring systems generally rely on predefined thresholds, dashboards, and manually constructed alerts, which can struggle to identify emerging failures, correlate distributed events, and distinguish genuine incidents from normal variations. AI-driven observability provides an opportunity to transform reliability engineering from reactive incident management into an autonomous and predictive discipline. This research proposes an AI-driven observability framework for autonomous service reliability engineering in enterprise cloud platforms. The proposed framework integrates metrics, logs, traces, events, and topology information into a unified observability layer and applies machine learning for anomaly detection, event correlation, failure prediction, root-cause analysis, and intelligent incident prioritization. A closed-loop automation mechanism subsequently recommends or executes remediation actions such as service restart, workload scaling, traffic redistribution, configuration adjustment, and resource reallocation under predefined safety constraints. The methodology evaluates the framework using representative enterprise workloads and compares it with conventional threshold-based monitoring and human-driven incident response. Performance is assessed through incident detection accuracy, mean time to detect, mean time to recover, false-positive rate, prediction accuracy, remediation success rate, service availability, and operational overhead. The research aims to demonstrate how AI-enabled observability can improve reliability, reduce operational burden, and support resilient self-healing enterprise cloud platforms

Article Information

Journal

International Journal of Science, Research and Technology

Volume (Issue)

Vol. 8 No. 6 (2025): International Journal of Science, Research and Technology (IJSRAT)

DOI

https://doi.org/10.15662/IJSRAT.2025.0806026

Pages

15524-15531

Published

December 21, 2025
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Vincent PINET (%2025). Autonomous Service Reliability Engineering Using AI-Driven Observability in Enterprise Cloud Platforms. International Journal of Science, Research and Technology , Vol. 8 No. 6 (2025): International Journal of Science, Research and Technology (IJSRAT) , pp. 15524-15531. https://doi.org/10.15662/IJSRAT.2025.0806026

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