Guest Editorial: Managing Next-Generation Networks for Intelligent Services and Applications

Abstract

Next-generation networks (NGNs) integrate the functionalities of a plethora of established and emerging technologies such as 5G, artificial intelligence (AI), edge intelligence, network softwarization, and data plane programmability. NGNs promise to achieve ultra-fast data rates and minimal latency in wireless communication and networking. They enable smart and autonomous services and applications through AI and machine learning (ML). Coupled with advances in end-user devices, NGNs enable a plethora of smart services and user-defined applications. Given the rise in system complexity and the exponential increase in the amount of data exchanged through networks, new state-of-the-art NGN management solutions are needed. By adapting cooperative and distributed management solutions, more reliable and efficient services and applications are conveyed to end users. Moreover, with distributed learning solutions, NGNs could be optimized to support the dynamic nature of network configuration and enable end-to-end system automation. For instance, the integration of federated learning (FL), deep reinforcement learning (DRL), and blockchain to NGNs can support scalable, secure, and diversified services and applications. Furthermore, through data plane programmability, network intelligence could be implemented directly on programmable devices in the network core. An intelligent forwarding plane would enable faster reaction to network events without depending on time-consuming exchange between the data and control planes.