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Improving datacenter operations is vital for the digital society. We positthat doing so requires our community to shift, from operational aspects takenin isolation to holistic analysis of datacenter resources, energy, andworkloads. In turn, this shift will require new analysis methods, andopen-access, FAIR datasets with fine temporal and spatial granularity. Weleverage in this work one of the (rare) public datasets providing fine-grainedinformation on datacenter operations. Using it, we show strong evidence thatfine-grained information reveals new operational aspects. We then propose amethod for holistic analysis of datacenter operations, providing statisticalcharacterization of node, energy, and workload aspects. We demonstrate thebenefits of our holistic analysis method by applying it to the operations of adatacenter infrastructure with over 300 nodes. Our analysis reveals bothgeneric and ML-specific aspects, and further details how the operationalbehavior of the datacenter changed during the 2020 COVID-19 pandemic. We makeover 30 main observations, providing holistic insight into the long-termoperation of a large-scale, public scientific infrastructure. We suggest suchobservations can help immediately with performance engineering tasks such aspredicting future datacenter load, and also long-term with the design ofdatacenter infrastructure.

A Holistic Analysis of Datacenter Operations: Resource Usage, Energy, and Workload Characterization -- Extended Technical Report