Online Rack Placement in Large-Scale Data Centers: Online Sampling Optimization and Deployment

This paper optimizes the configuration of large-scale data centers toward cost-effective, reliable, and sustainable cloud supply chains. The problem involves placing incoming racks of servers within a data center to maximize demand coverage given space, power, and cooling restrictions. We formulate an online integer optimization model to support rack placement decisions. We propose a tractable online sampling optimization (OSO) approach to multistage stochastic optimization, which approximates unknown parameters with a sample path and reoptimizes decisions dynamically. We prove that OSO achieves a strong competitive ratio in canonical online resource allocation problems and sublinear regret in the online batched bin packing problem. Theoretical and computational results show it can outperform mean-based certainty-equivalent resolving heuristics. Our algorithm has been packaged into a software solution deployed across Microsoft’s data centers, contributing an interactive decision-making process at the human-machine interface. Using deployment data, econometric tests suggest that adoption of the solution has a negative and statistically significant impact on power stranding, estimated at one to three percentage points. At the scale of cloud computing, these improvements in data center performance result in significant cost savings and environmental benefits.

Funding: This work was partially supported by the MIT Center for Transportation and Logistics UPS PhD Fellowship.

Supplemental Material: All supplemental materials, including the code, data, and files required to reproduce the results, are available at https://doi.org/10.1287/opre.2025.1992.