旧指标已难适配生成式AI，新性能评估需兼顾能效与可观测性。
Performance Measurements in the AI-Centric Computing Continuum Systems
- 重构分布式计算连续体中的性能度量体系
- 提出能效、可持续性等新兴评估维度
- 为系统设计者提供选型参考，适合关注AI系统优化的读者
过去八十年间，计算范式从集中式大型系统演变为紧凑分布式的架构，催生了分布式计算连续体（DCC）模型。该模型整合云、边缘、物联网（IoT）及移动平台，支持多样化应用。近年来，生成式AI与大语言模型的兴起进一步加剧了对算力资源的需求。尽管传统性能指标奠定了坚实基础，但需重新审视并拓展以适应不断变化的计算需求与应用要求。精准的性能测量有助于提升系统效率，并促进系统目标对齐。本文综述了DCC与IoT环境中的常用度量指标，探讨了应对新型计算需求的新兴性能维度，如可持续性、能效与系统可观测性。同时，提出了选择合适度量标准的准则与考量因素，旨在推动该关键领域的未来研究与发展。
原文摘要 · Abstract (English)
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.
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