A Practical Cross-Layer Approach for ML-Driven Storage Placement in Warehouse-Scale Computers

Storage systems account for a major portion of the total cost of ownership (TCO) of warehouse-scale computers, and thus have a major impact on the overall system's efficiency. Machine learning (ML)-based methods for solving key problems in storage system efficiency, such as data placement, have shown significant promise. However, there are few known practical deployments of such methods. Studying this problem in the context of real-world hyperscale data center deployments at Google, we identify a number of challenges that we believe cause this lack of practical adoption. Specifically, prior work assumes a monolithic model that resides entirely within the storage layer, an unrealistic assumption in real-world data center deployments. We propose a cross-layer approach that moves ML out of the storage system and performs it in the application running on top of it, co-designed with a scheduling algorithm at the storage layer that consumes predictions from these application-level models. This approach combines small, interpretable models with a co-designed heuristic that adapts to different online environments. We build a proof-of-concept of this approach in a production distributed computation framework at Google. Evaluations in a test deployment and large-scale simulation studies using production traces show improvements of as much as 3.47x in TCO savings compared to state of the art baselines. We believe this work represents a significant step towards more practical ML-driven storage placement in warehouse-scale computers.