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AI-Driven Observability in Cloud Native DevOps: Enhancing Release Management and Infrastructure Resilience
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
Abstract
Modern application architectures, including cloud-native, with their increasing complexity, have created a range of challenges for DevOps teams that must ensure system reliability and quick release cycles, beyond the potentialities of traditional monitoring and logging frameworks. This paper explores the advent of AI into observability practices in cloud native DevOps environments, with a view to enhancing release management and infrastructure resilience. AI observability systems harnessing machine learning, anomaly detection, predictive analytics, and intelligent alerting technologies provide greater insight into system behavior to recognize issues before they arise and take automated decisions from deployment pipelines. The framework comprises distributed tracing, real-time telemetry, and AIOps to reduce mean time to resolution (MTTR); minimize downtime; and automatically initiate rollback and remediation actions. The study furthers the holistic evaluation of AI observability pipelines with pragmatic case scenarios, architectural diagrams, and performance benchmarks. Results indicate that AI-enhanced observability has drastically improved release stability and resilience in distributed containerized infrastructure. This study intends to provide a conceptual framework for incorporating intelligent observability pipelines into modern DevOps workflows.
Keywords
Cloud-Native, DevOps, AI Observability, Infrastructure Resilience, Release Management, AIOps, CI/CD, Microservices, Intelligent Alerting, Anomaly Detection
How to cite this paper
@article{1709288,
author = {Ganesh Dhandapani},
title = {AI-Driven Observability in Cloud Native DevOps: Enhancing Release Management and Infrastructure Resilience},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {8},
number = {12},
pages = {1459-1471},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1709288.pdf},
abstract = {Modern application architectures, including cloud-native, with their increasing complexity, have created a range of challenges for DevOps teams that must ensure system reliability and quick release cycles, beyond the potentialities of traditional monitoring and logging frameworks. This paper explores the advent of AI into observability practices in cloud native DevOps environments, with a view to enhancing release management and infrastructure resilience. AI observability systems harnessing machine learning, anomaly detection, predictive analytics, and intelligent alerting technologies provide greater insight into system behavior to recognize issues before they arise and take automated decisions from deployment pipelines. The framework comprises distributed tracing, real-time telemetry, and AIOps to reduce mean time to resolution (MTTR); minimize downtime; and automatically initiate rollback and remediation actions. The study furthers the holistic evaluation of AI observability pipelines with pragmatic case scenarios, architectural diagrams, and performance benchmarks. Results indicate that AI-enhanced observability has drastically improved release stability and resilience in distributed containerized infrastructure. This study intends to provide a conceptual framework for incorporating intelligent observability pipelines into modern DevOps workflows.},
keywords = {Cloud-Native, DevOps, AI Observability, Infrastructure Resilience, Release Management, AIOps, CI/CD, Microservices, Intelligent Alerting, Anomaly Detection},
month = {June},
}