  Improving datacenter operations is vital for the digital society. We posit
that doing so requires our community to shift, from operational aspects taken
in isolation to holistic analysis of datacenter resources, energy, and
workloads. In turn, this shift will require new analysis methods, and
open-access, FAIR datasets with fine temporal and spatial granularity. We
leverage in this work one of the (rare) public datasets providing fine-grained
information on datacenter operations. Using it, we show strong evidence that
fine-grained information reveals new operational aspects. We then propose a
method for holistic analysis of datacenter operations, providing statistical
characterization of node, energy, and workload aspects. We demonstrate the
benefits of our holistic analysis method by applying it to the operations of a
datacenter infrastructure with over 300 nodes. Our analysis reveals both
generic and ML-specific aspects, and further details how the operational
behavior of the datacenter changed during the 2020 COVID-19 pandemic. We make
over 30 main observations, providing holistic insight into the long-term
operation of a large-scale, public scientific infrastructure. We suggest such
observations can help immediately with performance engineering tasks such as
predicting future datacenter load, and also long-term with the design of
datacenter infrastructure.
