In the context of advancing 6G, a substantial paradigm shift is anticipated, highlighting comprehensive everything-to-everything interactions characterized by numerous connections and stringent adherence to Quality of Service/Experience (QoS/E) prerequisites. The imminent challenge stems from resource scarcity, prompting a deliberate transition to Computing-Network Convergence (CNC) as an auspicious approach for joint resource orchestration. While CNC-based mechanisms have garnered attention, their effectiveness in realizing future services, particularly in use cases like the Metaverse, may encounter limitations due to the continually changing nature of users, services, and resources. Hence, this paper presents the concept of Adaptable CNC (ACNC) as an autonomous Machine Learning (ML)-aided mechanism crafted for the joint orchestration of computing and network resources, catering to dynamic and voluminous user requests with stringent requirements. ACNC encompasses two primary functionalities: state recognition and context detection. Given the intricate nature of the user-service-computing-network space, the paper employs dimension reduction to generate live, holistic, abstract system states in a hierarchical structure. To address the challenges posed by dynamic changes, Continual Learning (CL) is employed, classifying the system state into contexts controlled by dedicated ML agents, enabling them to operate efficiently. These two functionalities are intricately linked within a closed loop overseen by the End-to-End (E2E) orchestrator to allocate resources. The paper introduces the components of ACNC, proposes a Metaverse scenario to exemplify ACNC's role in resource provisioning with Segment Routing v6 (SRv6), outlines ACNC's workflow, details a numerical analysis for efficiency assessment, and concludes with discussions on relevant challenges and potential avenues for future research.

翻译：在6G发展的背景下，预计将出现重大的范式转变，其特点是实现全面的万物互联交互，涉及海量连接并严格遵循服务质量/体验（QoS/E）的先决条件。迫在眉睫的挑战源于资源稀缺性，这促使业界有意识地转向计算-网络融合（CNC），将其作为一种前景广阔的联合资源编排方法。尽管基于CNC的机制已受到关注，但由于用户、服务和资源持续变化的特性，其在实现未来服务（特别是元宇宙等用例）方面的有效性可能面临局限。因此，本文提出了自适应CNC（ACNC）的概念，作为一种自主的机器学习（ML）辅助机制，专为计算与网络资源的联合编排而设计，以满足动态、海量且要求严格的用户请求。ACNC包含两项主要功能：状态识别与上下文检测。鉴于用户-服务-计算-网络空间的复杂性，本文采用降维方法，以分层结构生成实时、整体、抽象的系统状态。为应对动态变化带来的挑战，系统采用持续学习（CL）技术，将系统状态分类为由专用ML代理控制的上下文，使其能够高效运行。这两项功能在端到端（E2E）编排器的闭环监督下紧密关联，以实现资源分配。本文介绍了ACNC的组成部分，提出了一个元宇宙场景以例证ACNC在结合段路由v6（SRv6）进行资源供给中的作用，概述了ACNC的工作流程，详述了用于效率评估的数值分析，并最后讨论了相关挑战及未来研究的潜在方向。