迈向动态未来：可适应的计算与网络融合（ACNC）
Towards a Dynamic Future with Adaptable Computing and Network Convergence (ACNC)
AI总结：
本文提出可适应的计算与网络融合（ACNC）机制，利用机器学习、降维和持续学习实现动态环境下的计算与网络资源联合编排，以应对6G万物互联场景中的资源稀缺与服务需求挑战。
摘要：
在推进6G的背景下，预计将出现重大的范式转变，突出表现为全面的万物互联交互，其特征是大量的连接和对服务质量/体验（QoS/E）前提条件的严格遵守。迫在眉睫的挑战源于资源稀缺，促使人们有意识地转向计算与网络融合（CNC），将其作为联合资源编排的有前途的方法。尽管基于CNC的机制已引起关注，但其在实现未来服务（特别是在元宇宙等用例中）的有效性可能会遇到限制，这是由于用户、服务和资源的持续变化性质。因此，本文提出了可适应CNC（ACNC）的概念，作为一种自主的机器学习（ML）辅助机制，专为联合编排计算和网络资源而设计，以满足具有严格要求的大量动态用户请求。ACNC包含两个主要功能：状态识别和上下文检测。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的、整体的、抽象的系统状态。为了应对动态变化带来的挑战，采用了持续学习（CL），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。这两个功能在端到端（E2E）编排器监督的闭环中紧密相连，以分配资源。本文介绍了ACNC的组件，提出了一个元宇宙场景，以举例说明ACNC在使用段路由v6（SRv6）进行资源调配中的作用，概述了ACNC的工作流程，详细说明了用于效率评估的数值分析，并最后讨论了相关挑战和未来研究的潜在途径。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
