International Journal of Science, Engineering and TechnologyAuthors: Narendra Reddy Burramukku
Abstract: The rapid evolution of enterprise networks toward highly distributed, hybrid, and multi-cloud architectures has significantly increased their operational complexity, making traditional reactive network management approaches insufficient. Network Digital Twins (NDTs) have emerged as a promising paradigm that enables real-time monitoring, predictive analysis, and proactive optimization by maintaining a continuously synchronized virtual replica of the physical network. This paper presents a comprehensive review and architectural analysis of Network Digital Twin frameworks in the context of modern enterprise networks. It systematically examines the evolution from conventional network simulation and emulation to dynamic, data-driven digital twins and classifies existing NDT architectures into layered, centralized, distributed, and hybrid models. The study further analyzes key architectural components, including data acquisition, synchronization, modeling, analytics, and closed-loop control mechanisms. Enabling technologies such as software-defined networking, network function virtualization, artificial intelligence, machine learning, telemetry, and big data analytics are discussed in detail. Additionally, the paper highlights practical enterprise applications of NDTs, including network design and optimization, fault prediction, performance management, security analysis, and autonomous network operations. Finally, challenges related to scalability, data fidelity, integration with legacy systems, and security are identified, along with future research directions toward AI-native and fully autonomous enterprise networks. This work aims to serve as a reference for researchers and practitioners seeking to design, deploy, and leverage scalable and intelligent Network Digital Twin architectures for modern enterprise environments.
