EuroSys 2026, Edinburgh, Scotland, April 27th—30th, 2026.
Timothy Kim, Saurabh Kadekodi§, Arif Merchant§, Prashant Nema†, Jai Menon†, K. V. Rashmi, Gregory R. Ganger
Carnegie Mellon University
§Google
†Microsoft
Recent changes in data temperatures and storage device char- acteristics, both mechanical disk-drives (HDDs) and solid- state drives (SSDs), expand the set of deployment options for exascale storage. Until recently, exascale storage systems fol- lowed a pattern of placing most data on HDDs with smaller amounts of SSD storage used for caching and performance- critical workloads. Exascale storage provisioning and dataset placement trade-offs have now changed.
This paper describes a total cost of ownership (TCO) model that captures primary aspects of modern deployments and uses it to explore the new trade-off space. Using capacity and performance telemetry information for 43 production datasets+workloads at two large hyperscalers, we show sig- nificant changes from prior analyses of workloads and stor- age placement decisions across a multitude of storage device types. We also introduce a storage cluster TCO optimizer that identifies the lowest-TCO grouping and assignment of datasets to device types, exposing a number of insights that can help guide future deployments. For example, our analysis shows that the highest-density SSDs are particularly favor- able for clusters with heavy AI/ML workloads but are only cost-effective at exascale when combined with high-density HDDs. Finally, we use our framework to evaluate how stor- age provisioning and overall TCO change as a function of key parameters like device write amplification, cluster power bounds, and the maximum number of device types allowed.
FULL PAPER: pdf