Recently, the increasing need for computing resources has led to the prosperity of data centers, which poses challenges to the environmental impacts and calls for improvements in data center provisioning strategies. In this work, we show a comprehensive analysis based on profiling a variety of deep-learning inference applications on different generations of GPU servers. Our analysis reveals several critical factors which can largely affect the design space of provisioning strategies including the hardware embodied cost estimation, application-specific features, and the distribution of carbon cost each year, which prior works have omitted. Based on the observations, we further present a first-order modeling and optimization tool for data center provisioning and scheduling and highlight the importance of environmental impacts from data center management.

翻译：摘要：近年来，计算资源需求的持续增长推动了数据中心的繁荣发展，但也对环境影响提出了挑战，亟需改进数据中心配置策略。本研究通过对不同代际GPU服务器上多种深度学习推理应用的全面分析，揭示了若干关键因素——包括硬件隐含成本估算、应用特定特征以及年度碳排放成本分布——这些因素在先前的研究中被忽视，却对配置策略的设计空间产生重大影响。基于上述发现，我们进一步提出了首个数据中心配置与调度的建模优化工具，并强调了数据中心管理对环境影响的重要性。