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.

翻译：在过去的八十年间，计算范式已从大型集中式系统转向紧凑的分布式架构，从而催生了分布式计算连续体（DCC）的兴起。在此模型中，云、边缘、物联网（IoT）和移动平台等多个层级协同工作，以支持广泛的应用。最近，生成式人工智能和大语言模型的出现进一步加剧了该连续体内计算资源的需求。尽管传统性能指标已提供了坚实基础，但为跟上不断变化的计算需求和应用要求，仍需重新审视并扩展这些指标。准确的性能测量通过支持效率提升并促进与系统目标的一致性，使系统设计者和用户均能受益。在此背景下，我们回顾了DCC和IoT环境中常用的度量指标。同时，我们讨论了应对不断演进的计算需求的新兴性能维度，例如可持续性、能效和系统可观测性。我们还概述了选择合适指标的标准与考量，旨在激发这一关键领域未来的研究与发展。