Navigating Next‐Generation Network Architecture
Monika Dubey, Snehlata, Ashutosh Kumar Singh, Richa Mishra, Mohit Kumar · 2025
The framework for existing legacy network architecture is massive and complex. It mainly relies on inflexible and expensive equipment, typically constructed from a massive number of switches, routers, firewalls, and hubs. Moreover, this vendor-specific network configuration and complex control protocols are not flexible enough to offer customized quality of services (QoS). Provisioning of next-gen (Next Generation, 5G, and beyond) technologies, software-defined networking (SDN), network function virtualization (NFV), and network slicing (NS) work as catalysts to offer simplified, customized, and clever networking. To provide centralized positioning, SDN decouples the control plane (CP) and data plane (DP) from the traditional router. In the SDN architecture, decision making and network control are now done at a centralized place known as the controller. However, DP is still intact with the routing device. This arrangement privileges the network administrators to control, manage, and alter network behavior dynamically. To contrast the vender-specific networking, NFV allows network functions (NFs) to run on generic hardware. In this direction, NS pioneers QoS-specific use cases as a new business model. NS involves the slicing of a single physical network in the form of multiple slices. It not only supports the customization of QoS services for diverse use cases, but it also improves isolation, independence, multitenancy, dynamic resource allocation, and end-to-end service provisioning. In this chapter, we first delved into NexGen's promising technologies and explored their intertwined role and impact on the modern networking framework. We accessed various SDN and NFV architectures and discussed network-slicing framework. Secondly, we have shed light on the importance of AI-driven automated network management over traditional network approaches. In this sequence, we conducted a comparative analysis of AI-driven machine learning (ML) and deep learning (DL) approaches in the context of NextGen technologies. In this chapter, we intend to systematically and intricately navigate the multifaceted landscape of NexGen technologies. This chapter will offer researchers, industry stakeholders, and practitioners a timely and deeper understanding of transformative technology and its impact on modern network paradigms.