AI-Driven Predictive Operations: Transforming Cloud Infrastructure Management Through Intelligent Automation

Traditional reactive infrastructure management cannot meet the demands of modern cloud environments characterized by scale, complexity, and constant change. AI-driven operations introduce a paradigm shift by enabling systems to learn behavioral patterns, predict failures before impact, and autonomously resolve issues. Through the integration of statistical modeling, anomaly detection, trend forecasting, and autonomous remediation, organizations can transition from reactive troubleshooting to proactive prevention. Core capabilities—such as deviation analysis, dynamic load balancing, scheduled optimization, and self-healing mechanisms—reduce downtime, minimize manual intervention, and enhance system reliability. Embedding intelligent automation into cloud infrastructure not only resolves current operational inefficiencies but also lays the groundwork for self-improving systems. As digital transformation accelerates, adopting predictive and preventive operational models is critical for achieving resilience, scalability, and innovation enablement.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-07-15
DOI
https://doi.org/10.5281/zenodo.15916543
Primary Topic
Software System Performance and Reliability
Type
article
Field-Weighted Citation Impact
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article

AI-Driven Predictive Operations: Transforming Cloud Infrastructure Management Through Intelligent Automation

Zenodo (CERN European Organization for Nuclear Research)
Software System Performance and Reliability
article

AI-Driven Predictive Operations: Transforming Cloud Infrastructure Management Through Intelligent Automation

article en

Abstract

Traditional reactive infrastructure management cannot meet the demands of modern cloud environments characterized by scale, complexity, and constant change. AI-driven operations introduce a paradigm shift by enabling systems to learn behavioral patterns, predict failures before impact, and autonomously resolve issues. Through the integration of statistical modeling, anomaly detection, trend forecasting, and autonomous remediation, organizations can transition from reactive troubleshooting to proactive prevention. Core capabilities—such as deviation analysis, dynamic load balancing, scheduled optimization, and self-healing mechanisms—reduce downtime, minimize manual intervention, and enhance system reliability. Embedding intelligent automation into cloud infrastructure not only resolves current operational inefficiencies but also lays the groundwork for self-improving systems. As digital transformation accelerates, adopting predictive and preventive operational models is critical for achieving resilience, scalability, and innovation enablement.

Zenodo (CERN European Organization for Nuclear Research)
Google (United States) (US)
Industry, innovation and infrastructure
Openalex Percentile: Top 50%
Software System Performance and Reliability
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AI-Driven Predictive Operations: Transforming Cloud Infrastructure Management Through Intelligent Automation · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS