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.

基于人工智能技术，本文提出了一种用于计算和网络融合的智能内生管理平台 ——CNC 大脑，通过感知、调度、适应和治理等四个关键构建模块，在整个计算和网络融合生命周期中实现高异构性的供需匹配，通过在 OpenFaas、Kubernetes 和 Microsoft Azure 的真实业务数据集上进行评估，证明了该方法在资源利用和性能方面的有效性，并提出了 CNC 大脑的未来研究方向。