  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.
