Autonomous Cloud Networking in 2024: Leveraging AI and Intent-Based Architectures for Self-Healing and Optimization

In 2024, the convergence of AI-driven operations (AIOps), intent-based networking (IBN), and cloud- native infrastructure gave rise to autonomous cloud networking—a paradigm where networks adapt, secure, and optimize themselves with minimal human intervention. This research explores the design and deployment of self- managing networks across hybrid and multi-cloud environments. It investigates how AI/ML models are integrated into network control planes to enable predictive fault detection, adaptive traffic engineering, and dynamic policy enforcement. Using a survey of IT professionals and cloud architects, this study captures industry readiness, adoption barriers, and expectations surrounding autonomous networking technologies. It also examines architectural challenges, including observability, configuration drift, and real-time compliance. Through statistical analysis, the paper identifies the maturity level of autonomous networking deployments and proposes an architectural blueprint to guide implementation.

Publication Details

Journal
PhilPapers (PhilPapers Foundation)
Published
2025-01-01
Primary Topic
Software-Defined Networks and 5G
Type
article
Field-Weighted Citation Impact
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article

Autonomous Cloud Networking in 2024: Leveraging AI and Intent-Based Architectures for Self-Healing and Optimization

PhilPapers (PhilPapers Foundation)
Software-Defined Networks and 5G
article

Autonomous Cloud Networking in 2024: Leveraging AI and Intent-Based Architectures for Self-Healing and Optimization

article en

Abstract

In 2024, the convergence of AI-driven operations (AIOps), intent-based networking (IBN), and cloud- native infrastructure gave rise to autonomous cloud networking—a paradigm where networks adapt, secure, and optimize themselves with minimal human intervention. This research explores the design and deployment of self- managing networks across hybrid and multi-cloud environments. It investigates how AI/ML models are integrated into network control planes to enable predictive fault detection, adaptive traffic engineering, and dynamic policy enforcement. Using a survey of IT professionals and cloud architects, this study captures industry readiness, adoption barriers, and expectations surrounding autonomous networking technologies. It also examines architectural challenges, including observability, configuration drift, and real-time compliance. Through statistical analysis, the paper identifies the maturity level of autonomous networking deployments and proposes an architectural blueprint to guide implementation.

PhilPapers (PhilPapers Foundation)
Industry, innovation and infrastructure
Openalex Percentile: Top 45%
Software-Defined Networks and 5G
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Autonomous Cloud Networking in 2024: Leveraging AI and Intent-Based Architectures for Self-Healing and Optimization · PhilPapers (PhilPapers Foundation) (2025) | TGRS Research Map | TGRS