  Datacenter power demand has been continuously growing and is the key driver
of its cost. An accurate mapping of compute resources (CPU, RAM, etc.) and
hardware types (servers, accelerators, etc.) to power consumption has emerged
as a critical requirement for major Web and cloud service providers. With the
global growth in datacenter capacity and associated power consumption, such
models are essential for important decisions around datacenter design and
operation. In this paper, we discuss two classes of statistical power models
designed and validated to be accurate, simple, interpretable and applicable to
all hardware configurations and workloads across hyperscale datacenters of
Google fleet. To the best of our knowledge, this is the largest scale power
modeling study of this kind, in both the scope of diverse datacenter planning
and real-time management use cases, as well as the variety of hardware
configurations and workload types used for modeling and validation. We
demonstrate that the proposed statistical modeling techniques, while simple and
scalable, predict power with less than 5% Mean Absolute Percent Error (MAPE)
for more than 95% diverse Power Distribution Units (more than 2000) using only
4 features. This performance matches the reported accuracy of the previous
started-of-the-art methods, while using significantly less features and
covering a wider range of use cases.
