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Experimental Analysis of Server Fan Control Strategies for Improved Data Center Air-based Thermal Management
RISE ICE, RISE Research Institutes of Sweden, Björkskataleden 112, 973 47 Luleå, Sweden.
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab. RISE ICE, RISE Research Institutes of Sweden, Björkskataleden 112, 973 47 Luleå, Sweden.ORCID iD: 0000-0003-4293-6408
RISE ICE, RISE Research Institutes of Sweden, Björkskataleden 112, 973 47 Luleå, Sweden.
RISE ICE, RISE Research Institutes of Sweden, Björkskataleden 112, 973 47 Luleå, Sweden. Institute of Thermfluids, School of Mechanical Engineering, University of Leeds, United Kingdom.
2020 (English)In: Proceedings of the Nineteenth InterSociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems: ITherm 2020, IEEE, 2020, p. 341-349Conference paper, Published paper (Other academic)
Abstract [en]

This paper analyzes the prospects of a holistic air-cooling strategy that enables synchronisation of data center facility fans and server fans to minimize data center energy use. Each server is equipped with a custom circuit board which controls the fans using a proportional, integral and derivative (PID) controller running on the servers operating system to maintain constant operating temperatures, irrespective of environmental conditions or workload. Experiments are carried out in a server wind tunnel which is controlled to mimic data center environmental conditions. The wind tunnel fan, humidifier and heater are controlled via separate PID controllers to maintain a prescribed pressure drop across the server with air entering at a defined temperature and humidity. The experiments demonstrate server operating temperatures which optimally trade off power losses versus server fan power, while examining the effect on the temperature difference, ∆T. Furthermore the results are theoretically applied to a direct fresh air cooled data center to obtain holistic sweet spots for the servers, revealing that the minimum energy use is already attained by factory control. Power consumption and Power Usage Effectiveness (PUE) are also compared, confirming that decreasing the PUE can increase the overall data center power consumption. Lastly the effect of decreased server inlet temperatures is examined showing that lower inlet temperatures can reduce both energy consumption and PUE.

Place, publisher, year, edition, pages
IEEE, 2020. p. 341-349
Series
IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm), ISSN 1087-9870, E-ISSN 2577-0799
Keywords [en]
server thermal management, holistic data center cooling control, energy efficiency, current leakage, data center heat reuse, power usage effectiveness (PUE), server fan control
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-78335DOI: 10.1109/ITherm45881.2020.9190337Scopus ID: 2-s2.0-85091784050OAI: oai:DiVA.org:ltu-78335DiVA, id: diva2:1445938
Conference
19th InterSociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm 2020), 21-23 July, 2020, Virual conference
Note

Konferensbidraget har tidigare förekommit som manuskript i avhandling.

Available from: 2020-06-23 Created: 2020-06-23 Last updated: 2020-10-12Bibliographically approved
In thesis
1. Machine learning based control of small-scale autonomous data centers
Open this publication in new window or tab >>Machine learning based control of small-scale autonomous data centers
2020 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The low-latency requirements of 5G are expected to increase the demand for distributeddata storage and computing capabilities in the form of small-scale data centers (DC)located at the edge, near the interface between mobile and wired networks. These edgeDC will likely be of modular and standardized designs, although configurations, localresource constraints, environments and load profiles will vary and thereby increase theDC infrastructure diversity. Autonomy and energy efficiency are key objectives for thedesign, configuration and control of such data centers. Edge DCs are (by definition)decentralized and should continue operating without human intervention in the presenceof disturbances, such as intermittent power failures, failing components and overheating.Automatic control is also required for efficient use of renewable energy, batteries and theavailable communication, computing and data storage capacity.

These objectives demand data-driven models of the internal thermal and electricprocesses of an autonomous edge DC, since the resources required to manually defineand optimize the models for each DC would be prohibitive. In this thesis machinelearning methods that are implemented in a modular design are evaluated for thermalcontrol of such modular DCs. Experiments with small server clusters are presented, whichwere performed in order to investigate what parameters that are important in the designof advanced control strategies for autonomous edge DC. Furthermore, recent transferlearning results are discussed to understand how to develop data driven models thatcan be deployed to modular DC in varying configurations and environmental contextswithout training from scratch.

The first study demonstrates how a data driven thermal model for a small clusterof servers can be calibrated to sensor data and used for constructing a model predictivecontroller for the server cooling fan. The experimental investigations of cooling fancontrol continues in the next study which explores operational sweet-spots and energyefficient holistic control strategies. The machine learning based controller from the firststudy is then re-purposed to maintain environmental conditions in an exhaust chamberfavourable for drying apples, as part of a practical study how excess heat produced bycomputation can be used in the food processing industry. A fourth study describes theRISE EDGE lab - a test bed for small data centers - built with the intention to exploreand evaluate related technologies for micro-grids with renewable energy and batteries,5G connectivity and coolant storage. Finally the last work presented develops the modelfrom the first study towards an application for thermal based load balancing.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2020
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
National Category
Computer Systems Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-78337 (URN)978-91-7790-623-0 (ISBN)978-91-7790-624-7 (ISBN)
Presentation
2020-09-03, A109, Luleå tekniska universitet, Luleå, 10:00 (English)
Opponent
Supervisors
Available from: 2020-06-29 Created: 2020-06-23 Last updated: 2023-09-04Bibliographically approved

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