Cooling Control Strategies in Data Centers for Energy Efficiency and Heat Recovery

Document identifier: oai:DiVA.org:ltu-75917
Keyword: Engineering and Technology, Electrical Engineering, Electronic Engineering, Information Engineering, Control Engineering, Teknik och teknologier, Elektroteknik och elektronik, Reglerteknik
Publication year: 2019
Relevant Sustainable Development Goals (SDGs):
SDG 9 Industry, innovation and infrastructure
The SDG label(s) above have been assigned by OSDG.ai

Abstract:

Data centers are facilities dedicated to the processing, storage, and relay of large amounts of digital information. As a whole, it is an energy intensive industry, characterized by a sizable carbon footprint and a short-term exponential growth rate. At a macroscopic level, their operation requires balancing the offer and demand of computational, cooling, and electrical power resources. The computational workload is influenced by external factors such as the end-users’ activity, while the overall run-time costs depend on the weather conditions and the fluctuating pricing of electricity. In this context, the adoption of optimizing control strategies and co-design methodologies that address simultaneously both the mechanical and control aspects, has the potential to unlock more sustainable designs. Improvements in the overall energetic efficiency open to larger-scale deployments in less favorable geographical locations. Recovery systems addressing the vast amounts of by-product heat can support other heat intensive processes such as district networks, wood drying, greenhouses, and food processing. This work focuses on how to adapt the provisioning of the cooling resources to the cooling demand, without negotiating the computational throughput. We devise top-down designs, that address unexplored control possibilities in existing deployments. We moreover apply a bottomup perspective, by modeling and studying co-designed cooling setups which bring significant simplifications to data center level optimal provisioning problems. The analysis aims at the different levels of the data center infrastructure hierarchy, and provides answers to centerpiece questions such as i) what are the optimal flow provisioning policies at different levels of the data centers?; ii) how to design simple but effective control strategies that address the complexity induced by the large scales?; iii) what are the exhaust heat properties that can be expected in air-cooled and liquid-cooled data centers?. Exploiting a model-centric approach we demonstrate the effectiveness of tailored control strategies in both achieving better cooling efficiency and a higher quality of the heat harvest. This thesis presents opportunities to simplify data center control structures while retaining or improving their performance. Furthermore, it lays modeling and control methodologies toward the holistic control-oriented treatment of the computing, cooling, and power distribution infrastructures. The results have a practical character and the model-based analysis establishes important development directions, confirming existing trends. Enabling intelligent data center management systems might not need to imply more complex tools; rather, a co-design effort might yield both simpler and effective control systems.

Authors

Riccardo Lucchese

Luleå tekniska universitet; Signaler och system
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Andreas Johansson

Luleå tekniska universitet; Signaler och system
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Wolfgang Birk

Luleå tekniska universitet; Signaler och system
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Thomas Brunschwiler

IBM Research, IBM, Zürich, Switzerland
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