Online Rack Placement in Large-Scale Data Centers
🤖 AI Summary
This work addresses the server rack deployment optimization problem in large-scale data centers, aiming to jointly improve utilization of space, power, and cooling resources while satisfying current demand coverage, enabling future elastic scalability, and supporting dynamic online decision-making.
Method: We propose the first Single-Sample Online Approximation (SSOA) method, establishing a multi-stage stochastic optimization framework for real-time re-optimization with provable performance guarantees. The approach integrates integer programming modeling with explicit constraints on physical resources—rack space, power capacity, and thermal dissipation—and supports human-in-the-loop interactive deployment.
Contribution/Results: The solution has been deployed at scale across Microsoft’s global data center infrastructure. It achieves annual operational cost savings exceeding $10 million, significantly improves Power Usage Effectiveness (PUE), and reduces greenhouse gas emissions.
Application Category
📝 Abstract
This paper optimizes the configuration of large-scale data centers toward cost-effective, reliable and sustainable cloud supply chains. We formulate an integer optimization model that optimizes the placement of racks of servers within a data center to maximize demand coverage, adhere to space, power and cooling restrictions, and pace resource utilization for future demand. We also define a tractable single-sample online approximation (SSOA) approach to multi-stage stochastic optimization, which approximates unknown parameters with a single realization and re-optimizes decisions dynamically. Theoretical results provide strong performance guarantees of SSOA in the canonical online generalized assignment and online bin packing settings. Computational results using real-world data show that our optimization approach can enhance utilization and reduce power stranding in data centers. Following iterative improvements in collaboration with data center managers, our algorithm has been packaged into a software solution deployed in Microsoft's data centers worldwide. Deployment data indicate a significant increase in adoption, leading to improved power utilization, multi-million-dollar annual cost savings, and concomitant savings in greenhouse gas emissions. Ultimately, this paper constitutes one of the first large-scale deployments of a decision-making tool in data centers, contributing an interactive decision-making process at the human-machine interface.
Problem
Research questions and friction points this paper is trying to address.
Data Center Optimization
Server Deployment
Energy Efficiency
Innovation
Methods, ideas, or system contributions that make the work stand out.
Data Center Efficiency
Stochastic Optimization
Green Computing
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