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.

中文翻译：

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客座社论：管理智能服务和应用的下一代网络


下一代网络 (NGN) 集成了众多成熟和新兴技术的功能，例如 5G、人工智能 (AI)、边缘智能、网络软件化和数据平面可编程性。 NGN 有望在无线通信和网络中实现超快的数据速率和最小的延迟。它们通过人工智能和机器学习 (ML) 实现智能、自主的服务和应用程序。结合最终用户设备的进步，NGN 支持大量智能服务和用户定义的应用程序。鉴于系统复杂性的增加以及通过网络交换的数据量的指数级增长，需要新的最先进的 NGN 管理解决方案。通过采用协作和分布式管理解决方案，可以向最终用户提供更可靠、更高效的服务和应用。此外，通过分布式学习解决方案，可以优化NGN以支持网络配置的动态特性并实现端到端系统自动化。例如，将联邦学习（FL）、深度强化学习（DRL）和区块链与NGN集成可以支持可扩展、安全和多样化的服务和应用。此外，通过数据平面可编程性，网络智能可以直接在网络核心的可编程设备上实现。智能转发平面可以更快地对网络事件做出反应，而无需依赖数据和控制平面之间耗时的交换。