标题： 人工智能中心计算连续体系统的性能测量 显示英文标题

标题： Performance Measurements in the AI-Centric Computing Continuum Systems

摘要： 在过去八十年中，计算范式已从大型集中式系统转向紧凑的分布式架构，从而推动了分布式计算连续体（DCC）的兴起。 在这个模型中，多个层次如云、边缘、物联网（IoT）和移动平台协同工作，以支持各种应用。 最近，生成式人工智能和大型语言模型的出现进一步加剧了这一连续体中对计算资源的需求。 尽管传统性能指标已经提供了坚实的基础，但它们需要被重新审视和扩展，以跟上不断变化的计算需求和应用要求。 准确的性能测量通过提高效率并促进与系统目标的对齐，为系统设计者和用户带来了好处。 在此背景下，我们回顾了DCC和IoT环境中常用的指标。 我们还讨论了应对不断变化的计算需求的新性能维度，例如可持续性、能效和系统可观测性。 我们还列出了选择适当指标的标准和考虑因素，旨在激发该关键领域未来的研究所和开发工作。 显示英文摘要

摘要： Over the Eight decades, computing paradigms have shifted from large, centralized systems to compact, distributed architectures, leading to the rise of the Distributed Computing Continuum (DCC). In this model, multiple layers such as cloud, edge, Internet of Things (IoT), and mobile platforms work together to support a wide range of applications. Recently, the emergence of Generative AI and large language models has further intensified the demand for computational resources across this continuum. Although traditional performance metrics have provided a solid foundation, they need to be revisited and expanded to keep pace with changing computational demands and application requirements. Accurate performance measurements benefit both system designers and users by supporting improvements in efficiency and promoting alignment with system goals. In this context, we review commonly used metrics in DCC and IoT environments. We also discuss emerging performance dimensions that address evolving computing needs, such as sustainability, energy efficiency, and system observability. We also outline criteria and considerations for selecting appropriate metrics, aiming to inspire future research and development in this critical area.