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arXiv · 2501.12725

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

Saumil Baxi·Kayla Cummings·Alexandre Jacquillat·Sean Lo·Rob McDonald·Konstantina Mellou·Ishai Menache·Marco Molinaro
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Abstract

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 multi-stage stochastic optimization, which approximates unknown parameters with a sample path and re-optimizes 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 1-3 percentage point. At the scale of cloud computing, these improvements in data center performance result in significant cost savings and environmental benefits.

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Saumil Baxi, Kayla Cummings, Alexandre Jacquillat, Sean Lo, Rob McDonald, Konstantina Mellou, Ishai Menache, Marco Molinaro. 2025-01-22. Online Rack Placement in Large-Scale Data Centers: Online Sampling Optimization and Deployment. https://arxiv.org/abs/2501.12725

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