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Cloud-native architectures demand rapid, continuous delivery, stretching traditional DevOps workflows. Artificial intelligence (AI) and machine learning (ML) now supply the predictive insight and automation required to meet this pace. This paper introduces a unified AI-driven DevOps framework that optimizes the software-development lifecycle (SDLC) for cloud-native applications. We summarise recent advances in AI-enhanced CI/CD, predictive observability, proactive DevSecOps security, and self-healing infrastructure, and examine practical deployments across Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Finally, we outline future research directions—explainable and generative AI, federated learning, and responsible AI governance—charting a path toward sustainable, resilient, and secure cloud-native modernization.

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AI-Driven DevOps Automation for Cloud-Native Application Modernization

  • 会议论文

  • 发表日期：

    2026年

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  • Akshay Mittal

摘要

Cloud-native architectures demand rapid, continuous delivery, stretching traditional DevOps workflows. Artificial intelligence (AI) and machine learning (ML) now supply the predictive insight and automation required to meet this pace. This paper introduces a unified AI-driven DevOps framework that optimizes the software-development lifecycle (SDLC) for cloud-native applications. We summarise recent advances in AI-enhanced CI/CD, predictive observability, proactive DevSecOps security, and self-healing infrastructure, and examine practical deployments across Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). Finally, we outline future research directions—explainable and generative AI, federated learning, and responsible AI governance—charting a path toward sustainable, resilient, and secure cloud-native modernization.