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技术发展的背景下，预计将出现重大范式转变，其核心特征是涵盖万物互联的全面交互，包括海量连接以及对服务质量/体验（QoS/E）指标的严格遵循。资源稀缺性带来的紧迫挑战促使业界战略性地转向算网融合（CNC），将其作为联合资源编排的一种可行方案。尽管基于CNC的机制已引起广泛关注，但由于用户、服务及资源的持续动态变化，其在实现未来服务（特别是元宇宙等应用场景）中的效能可能面临限制。为此，本文提出自适应算网融合（ACNC）概念，这是一种基于机器学习（ML）的自主机制，专为动态且具有严苛需求的海量用户请求场景下的计算与网络资源联合编排而设计。ACNC包含两项核心功能：状态识别与上下文检测。鉴于用户-服务-计算-网络空间的复杂特性，本文采用降维方法生成具有分层结构的实时、整体、抽象系统状态。为应对动态变化带来的挑战，采用持续学习（CL）技术，将系统状态分类为由专属ML代理控制的上下文，从而使其高效运行。这两项功能通过由端到端（E2E）编排器监督的闭环系统紧密耦合以实现资源分配。本文介绍了ACNC的组件，提出元宇宙场景示例以阐明ACNC在基于分段路由v6（SRv6）的资源调配中的作用，概述了ACNC的工作流程，详述了效率评估的数值分析，并最终讨论了相关挑战与未来研究方向。