A Bring-Your-Own-Model Approach for ML-Driven Storage Placement in Warehouse-Scale Computers#

Chenxi Yang, Yan Li, Martin Maas, Mustafa Uysal, Ubaid Hafeez, Arif Merchant, Richard McDougall (anonymized hyperscaler "AnonCorp")

• Why: Less about storage than about why ML-for-infra hasn't shipped — the paper diagnoses the failure modes of a decade of academic ML-placement papers in a real hyperscale environment. That diagnosis transfers to every "why doesn't anyone deploy this" ML-for-networking idea a PM has ever sponsored.
• What: A cross-layer design: ML prediction moves out of the storage layer and into the application above it ("bring your own model"), with a co-designed heuristic in the storage layer that consumes those predictions. Small interpretable application-level models + adaptive storage-layer scheduling, tested in a production distributed-computation framework at a hyperscaler; up to 3.47× TCO savings over state-of-the-art baselines in test deployment and production-trace simulations. (from abstract only)
• How: Prior work assumed a monolithic model resident inside the storage system — unrealistic in real deployments. Instead, the application provides predictions as hints; the storage layer runs a scheduling algorithm robust to online drift and heterogeneous model quality.
• Where applicable: The specific TCO numbers are hyperscaler- and trace-specific, but the architectural pattern — predictions as hints from the layer that has the data, heuristics as the enforcement point that must stay robust — is broadly applicable, including to ML-driven network traffic engineering. Proof-of-concept stage, not full production.