Rizky, Pratama and Siti Nurhaliza, Putri (2024) NEXT-GENERATION NETWORK AUTOMATION: LEVERAGING AI AND MACHINE LEARNING FOR AUTONOMOUS INFRASTRUCTURE. Journal of Engineering, Mechanics and Modern Architecture, 3 (11). pp. 112-120. ISSN 2181-4384

Text 112-120+NEXT-GENERATION+NETWORK+AUTOMATION+LEVERAGING+AI+AND+MACHINE+.pdf Download (288kB)

Abstract

As network infrastructures grow increasingly complex and dynamic, traditional manual management approaches are becoming unsustainable. This article explores the transformative potential of artificial intelligence (AI) and machine learning (ML) in ushering a new era of next-generation network automation. By integrating AI-driven analytics, predictive insights, and autonomous decision-making, network operations can evolve from reactive, labor-intensive tasks to proactive, self-optimizing systems. We delve into core technologies enabling autonomous infrastructure, including intelligent traffic management, anomaly detection, adaptive resource allocation, and automated fault resolution. The discussion highlights real-world implementations and frameworks that harness AI/ML to enhance network scalability, reliability, and security while reducing operational costs and human error. This comprehensive examination provides network engineers, architects, and IT leaders with practical guidance on adopting AI-powered automation tools, addressing challenges in integration, data quality, and trustworthiness. Ultimately, the article underscores how leveraging AI and ML not only streamlines network management but also lays the foundation for truly autonomous, resilient, and future-proof digital infrastructures.

Item Type:	Article
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Postgraduate > Master's of Islamic Education
Depositing User:	Journal Editor
Date Deposited:	04 Jun 2025 11:44
Last Modified:	04 Jun 2025 11:44
URI:	http://eprints.umsida.ac.id/id/eprint/16173

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