Author
Listed:
- 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)
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.
Suggested Citation
Rui Peng Liu & Konstantina Mellou & Evelyn Xiao-Yue Gong & Beibin Li & Thomas Coffee & Jeevan Pathuri & David Simchi-Levi & Ishai Menache, 2025.
"Efficient Cloud Server Deployment Under Demand Uncertainty,"
Manufacturing & Service Operations Management, INFORMS, vol. 27(2), pages 425-440, March.
Handle:
RePEc:inm:ormsom:v:27:y:2025:i:2:p:425-440
DOI: 10.1287/msom.2023.0372
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormsom:v:27:y:2025:i:2:p:425-440. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.