Massive emerging applications are driving demand for the ubiquitous deployment of computing power today. This trend not only spurs the recent popularity of the \emph{Computing and Network Convergence} (CNC), but also introduces an urgent need for the intelligentization of a management platform to coordinate changing resources and tasks in the CNC. Therefore, in this article, we present the concept of an intelligence-endogenous management platform for CNCs called \emph{CNC brain} based on artificial intelligence technologies. It aims at efficiently and automatically matching the supply and demand with high heterogeneity in a CNC via four key building blocks, i.e., perception, scheduling, adaptation, and governance, throughout the CNC's life cycle. Their functionalities, goals, and challenges are presented. To examine the effectiveness of the proposed concept and framework, we also implement a prototype for the CNC brain based on a deep reinforcement learning technology. Also, it is evaluated on a CNC testbed that integrates two open-source and popular frameworks (OpenFaas and Kubernetes) and a real-world business dataset provided by Microsoft Azure. The evaluation results prove the proposed method's effectiveness in terms of resource utilization and performance. Finally, we highlight the future research directions of the CNC brain.

翻译：海量新兴应用正推动当今计算能力普遍部署的需求。这一趋势不仅催生了近期流行的**算网融合**（CNC），还亟需一种智能化的管理平台来协调CNC中动态变化的资源与任务。为此，本文基于人工智能技术提出了面向CNC的智能内生管理平台概念——**CNC大脑**。该平台旨在通过感知、调度、适配、治理四个关键模块，在CNC全生命周期内高效自动匹配高度异构的供需关系。本文阐述了各模块的功能、目标及面临的挑战。为验证所提概念与框架的有效性，我们基于深度强化学习技术实现了CNC大脑原型系统，并在集成OpenFaas和Kubernetes两个开源流行框架的CNC测试平台及微软Azure提供的真实业务数据集上进行了评估。评估结果证明了所提方法在资源利用率和性能方面的有效性。最后，我们展望了CNC大脑的未来研究方向。