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

TL;DR
This paper presents an online sampling optimization method for rack placement in large-scale data centers, improving cost-efficiency, reliability, and sustainability through dynamic decision-making and real-world deployment.
Contribution
It introduces a novel online sampling optimization approach for rack placement, with theoretical guarantees and practical deployment in Microsoft data centers.
Findings
OSO achieves strong competitive ratio and sublinear regret
Outperforms mean-based heuristics in simulations
Deployment reduces power stranding by 1-3 percentage points
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…
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Taxonomy
TopicsDistributed and Parallel Computing Systems · Cloud Computing and Resource Management · Advanced Data Storage Technologies
