Efficient Cloud Server Deployment Under Demand Uncertainty

Rui Peng Liu (), Konstantina Mellou (), Evelyn Xiao-Yue Gong (), Beibin Li (), Thomas Coffee (), Jeevan Pathuri (), David Simchi-Levi () and Ishai Menache ()
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Rui Peng Liu: Machine Learning and Optimization, Microsoft Research, Redmond, Washington 98052
Konstantina Mellou: Machine Learning and Optimization, Microsoft Research, Redmond, Washington 98052
Evelyn Xiao-Yue Gong: Tepper School of Business, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213
Beibin Li: Machine Learning and Optimization, Microsoft Research, Redmond, Washington 98052
Thomas Coffee: Machine Learning and Optimization, Microsoft Research, Redmond, Washington 98052
Jeevan Pathuri: Cloud Supply Chain, Microsoft, Redmond, Washington 98052
David Simchi-Levi: Department of Civil and Environmental Engineering and Institute for Data, Systems, and Society, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139
Ishai Menache: Machine Learning and Optimization, Microsoft Research, Redmond, Washington 98052

Manufacturing & Service Operations Management, 2025, vol. 27, issue 2, 425-440

Abstract: Problem definition : Cloud computing is a multibillion-dollar business that draws substantial capital investments from large companies such as Amazon, Microsoft, and Google. Large cloud providers need to accommodate the growing demand for computing resources while avoiding unnecessary overprovisioning of hardware and operational costs. The underlying decision processes are challenging, as they involve long-term hardware and infrastructure investments under future demand uncertainty. In this paper, we introduce the cloud server deployment problem . One important aspect of the problem is that the infrastructure preparation work has to be planned for before server deployments can take place. Furthermore, a combination of temporal constraints has to be considered together with a variety of physical constraints. Methodology/results : We formulate the underlying optimization problem as a two-stage stochastic program. After carefully examining the demand data and on-the-ground deployment operations, we distill two structural properties on deployment throughput constraints and provide tightness results on a convex relaxation of the second stage. Based on that, we develop efficient cutting-plane methods that exploit the special structure of the problem and can accommodate different risk measures. We test our algorithms with real production traces from Microsoft Azure and demonstrate sizeable cost reductions. We show empirically that the algorithms remain optimal even when the two properties are not fully satisfied. Managerial implications : Cloud supply chain operations were largely executed manually due to their complexity and dynamic nature. In this paper, we show that the key decision processes can be systematically optimized. In particular, we demonstrate that accounting for the stochastic nature of demands results in substantial cost reductions in cloud server deployments. Another benefit of our stochastic optimization approach is the ability to seamlessly integrate configurable risk preferences of cloud providers.

Keywords: cloud supply chain; optimization; stochastic programming; benders decomposition; capacity expansion; inventory management (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:inm:ormsom:v:27:y:2025:i:2:p:425-440

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