Online Rack Placement in Large-Scale Data Centers: Online Sampling Optimization and Deployment
TL;DR
A large-scale online discrete optimization model is formulates and a new online sampling optimization (OSO) algorithm is developed that anticipates future demand by repeatedly simulating future arrivals and reoptimizing decisions over time, demonstrating the real-world impact of optimization in cloud infrastructure management.
Abstract
Online Rack Placement in Large-Scale Data Centers: Online Sampling Optimization and Deployment Data centers have grown into major components of global supply chains. This paper develops, deploys, and assesses an optimization algorithm to improve how large-scale data centers place incoming server racks dynamically while balancing space, power, cooling, and reliability constraints. Poor placement decisions can leave valuable resources stranded, leading to high costs and reduced operating resilience. This paper formulates a large-scale online discrete optimization model and develops a new online sampling optimization (OSO) algorithm that anticipates future demand by repeatedly simulating future arrivals and reoptimizing decisions over time. Theoretical results provide performance guarantees, and computational results show its benefits against state-of-the-art reoptimization methods. The system was implemented as a decision-support tool and deployed across Microsoft’s global fleet of data centers. Using postdeployment data, the paper shows that adoption of the tool reduced power stranding by one to three percentage points. At Microsoft’s scale, these improvements translate into substantial financial savings and meaningful reductions in greenhouse gas emissions, demonstrating the real-world impact of optimization in cloud infrastructure management.