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Simulation-based development of distributed control systems in energy-efficient data centres
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0001-8185-7118
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The main focus of this thesis is on the area of integrated automated control systems inmodern data centres. The data centres are mission-critical facilities since they provide services for transporting, storing and processing vast amounts of data, which can be considered the ”new oil” of the Industry 4.0 era. Reliability of data centres is crucial for providing their availability to customers; thus, they require the detecting and predicting faults and properly recovering from them on time or mitigating their effects. The sustainability of data centres is in reducing energy consumption and mitigating the negative impact on the environment. So the data centres require flexible management of IT- and cooling workload to save energy, as well as they are oriented on the use of renewable energy generation techniques and free cooling methods. Thus, the integrated automated control in modern data centres is expected to achieve sustainability and energy efficiency while maintaining reliability and availability. The thesis addresses the reliability and sustainability issues in modern data centres. The handling of such issues requires the development and validation of control strategies as well as the construction of comprehensive control and automation systems based on these strategies. Modern data centres have the modular architecture by providing clear and unified procedures for data centre components installation and replacement. Because of the modular structure of data centres, it is unreasonable for their control systems to remain centralised, static and rigid. Thus the thesis focuses on developing modular and flexible automation systems for data centres. Modular and flexible control assumes that controllers make their decisions autonomously based on their objectives and interact with each other to achieve some common goals for the holistic control system. Thus, the thesis’s first contribution is the proposition of a multi-agent control (MAC) as a distributed approach to implementing the required control functions by communication and interaction of controllers. This work suggests the general design of the multi-agent control, which focuses on base agents playing as individual controllers and interactions between the agents. The process of the automation system engineering requires progressive and continuous validation. The closed-loop approach, allowing the validation of the control system, uses a plant model as an essential part. The second contribution is a modular toolbox that enables building models of data centres of any scale and configuration with relative ease. The toolbox comprises Simulink blocks which model individual components of a regular data centre. Each block is a complete model of the corresponding component encapsulating all parameters and equations describing its behaviour. The system is extendable by adding new modifications to the existing blocks as well as by developing new blocks. Thus the constructed model is capable of substituting for the real data centre at examining the performance of different control strategies in a dynamic mode. And the third contribution, in addition to the modelling toolbox, the thesis also suggests a control toolbox, a set of Simulink blocks implementing the individual controllers, which utilise reinforcement learning algorithms. The control toolbox is capable of examining the different reinforcement learning algorithms and reward functions to select the most relevant ones to certain controllers. Thus the main outcome of the thesis is a collection of methods, algorithms and models enabling creation of the platform, which supports the development and validation of the distributed automated control systems for data centres. The platform is a modular toolbox aimed at constructing the data centre models and developing the control system in the data centre as a set of interacting autonomous agents. As well as the platform utilises the multi-agent approach as a promising approach in organising the agents’ interactions in both traditional methods, such as a voting procedure or an auction, and the multi-agent reinforcement learning approach.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2022.
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
National Category
Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
URN: urn:nbn:se:ltu:diva-92717ISBN: 978-91-8048-133-5 (print)ISBN: 978-91-8048-134-2 (electronic)OAI: oai:DiVA.org:ltu-92717DiVA, id: diva2:1691750
Public defence
2022-10-18, E238, Luleå, 10:00 (English)
Opponent
Supervisors
Available from: 2022-08-31 Created: 2022-08-30 Last updated: 2022-09-23Bibliographically approved
List of papers
1. Towards reinforcement learning approach to energy-efficient control of server fans in data centres
Open this publication in new window or tab >>Towards reinforcement learning approach to energy-efficient control of server fans in data centres
2021 (English)In: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ), IEEE, 2021, p. 1-4Conference paper, Published paper (Refereed)
Abstract [en]

Modern data centres require control, which aims to improve their energy efficiency and maintain their high availability. This work considers the implementation of a server fan agent, which is intended to minimise the power consumption of the corresponding server fan or group of fans. In the paper, the reinforcement learning approach to energy-efficient control of server fans is suggested. The reinforcement learning workflow is considered. The Simulink blocks simplifying the building of the environment for the reinforcement learning agent are developed. This work provides the framework for creating and training reinforcement learning agents of different types. As the paper is only a work-in-progress, possible type of agents and their training process is described, but training and deploying the agent is a work for the future.

Place, publisher, year, edition, pages
IEEE, 2021
Keywords
Training, Fans, Data centers, Power demand, Software packages, Conferences, Reinforcement learning, data centre, energy-efficient control, multi-agent control, reinforcement learning
National Category
Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-88441 (URN)10.1109/ETFA45728.2021.9613639 (DOI)000766992600217 ()2-s2.0-85122918349 (Scopus ID)
Conference
26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA 2021), Västerås, Sweden, September 7-10, 2021
Funder
Swedish Energy Agency, 43090–2
Note

ISBN för värdpublikation: 978-1-7281-2989-1, 978-1-7281-2990-7

Available from: 2021-12-16 Created: 2021-12-16 Last updated: 2022-08-30Bibliographically approved
2. Towards Multi-Agent Control in Energy-Efficient Data Centres
Open this publication in new window or tab >>Towards Multi-Agent Control in Energy-Efficient Data Centres
2020 (English)In: Proceedings: IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, IEEE, 2020, p. 3574-3579Conference paper, Published paper (Refereed)
Abstract [en]

Modern data centres consume electricity at the industrial scale; at the same time, most of them demonstrate redundancy in energy consumption. The two most significant energy consumers in a data centre are its computational system and cooling system. This work focuses on techniques, which adapt the system energy use based on resource requirements at run-time. Actually, this work is an inception phase, which determines the main requirements to control the energy-efficient data centre and develops its general project. For that aim, the general design of the multi-agent control is proposed. The different types of agents are identified and their objectives are determined. Based on agent types the architecture of the multi-agent control is developed and agents' interactions are considered. The paper presents an example of an agent controlling a server fan. The agent is examined using closed-loop co-simulation with a server model.

Place, publisher, year, edition, pages
IEEE, 2020
Series
Annual Conference of Industrial Electronics Society, E-ISSN 2577-1647
Keywords
data centre, energy-efficiency, multi-agent control
National Category
Other Physics Topics
Research subject
Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-81494 (URN)10.1109/IECON43393.2020.9255232 (DOI)2-s2.0-85097752105 (Scopus ID)
Conference
46th Annual Conference of the IEEE Industrial Electronics Society (IECON 2020), 19-21 October, 2020, Singapore (Online)
Note

ISBN för värdpublikation: 978-1-7281-5414-5

Available from: 2020-11-20 Created: 2020-11-20 Last updated: 2022-08-30Bibliographically approved
3. Improvement of Energy Efficiency in Data Centers via Flexible Humidity Control
Open this publication in new window or tab >>Improvement of Energy Efficiency in Data Centers via Flexible Humidity Control
Show others...
2016 (English)In: IECON Proceedings (Industrial Electronics Conference), Piscataway, NJ: IEEE Computer Society, 2016, p. 5585-5590, article id 7793777Conference paper, Published paper (Refereed)
Abstract [en]

Abstract- The main goal of climate control systems in data centres is to keep the temperature and humidity in a suitable level for computational devices. Therefore, cooling and humidification systems are essential parts of every Building Automation System (BAS), which is utilized in server rooms. Although the current climate control systems ensure appropriate thermal conditions to computational nodes such as servers, they waste substantial amount of energy. The main cause of this inefficiency is that the current climate control systems, which are responsible for thermal management of the data centres, follow rigid control strategies that maintain constant thermal conditions irrespective of climate changes caused by various computational loads in the plant. To address this issue, in our previous works we proposed a method of optimizing energy consumption in data centre cooling systems while maintaining an acceptable level of thermal comfort for CPUs in the server room. In this paper, we present the enhancement of our previous method by incorporating humidity control into it. The enhanced method consists of a thermal model of server room, and a simulation tool to find an energy efficient control strategy for the climate control system in different situations by comparing different control strategies. The effectiveness of the proposed method has been investigated via simulation and the result, which shows 41.5% reduction in total energy consumption is presented.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Computer Society, 2016
Series
IEEE Industrial Electronics Society, ISSN 1553-572X
Keywords
Building automation, Climate control, Cooling system, Data centre automation, Energy efficiency, Humidification, Thermal model
National Category
Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-61576 (URN)10.1109/IECON.2016.7793777 (DOI)000399031205143 ()2-s2.0-85010042111 (Scopus ID)9781509034741 (ISBN)
Conference
42nd Conference of the Industrial Electronics Society, IECON 2016, Florence, Italy, 24-27 October 2016
Projects
SAVESENDATE-EXTEND
Funder
Swedish Energy Agency, 381143Vinnova, 3811426
Available from: 2017-01-22 Created: 2017-01-22 Last updated: 2023-10-14Bibliographically approved
4. Reinforcement learning approach to implementation of individual controllers in data centre control system
Open this publication in new window or tab >>Reinforcement learning approach to implementation of individual controllers in data centre control system
2022 (English)In: 2022 IEEE 20th International Conference on Industrial Informatics (INDIN), IEEE, 2022, p. 41-46Conference paper, Published paper (Refereed)
Abstract [en]

Contemporary data centres consume electricity onan industrial scale and require control to improve energyefficiency and maintain high availability. The article proposes anidea and structure of the framework supporting development andvalidation of the multi-agent control for the energy-efficient datacentre. The framework comprises two subsystems: the modellingtoolbox and the controlling toolbox. This work focuses on suchessential components of the controlling toolbox, as an individualcontroller. The reinforcement learning approach is applied to thecontrollers’ implementation. The server fan controller, named SFagent, is implemented based on the framework infrastructureand reinforcement learning approach. The agent’s capability ofenergy-saving is demonstrated.

Place, publisher, year, edition, pages
IEEE, 2022
Keywords
data centre, modelling, energy-efficient control, multi-agent control, reinforcement learning
National Category
Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-92716 (URN)10.1109/INDIN51773.2022.9976179 (DOI)2-s2.0-85145768731 (Scopus ID)978-1-7281-7568-3 (ISBN)
Conference
2022 INDIN – 20th IEEE International Conference on Industrial Informatics, July 25 - 28, 2022, Perth, Australia
Funder
Swedish Energy Agency, 43090-2
Available from: 2022-08-30 Created: 2022-08-30 Last updated: 2023-05-08Bibliographically approved
5. Towards Extension of Data Centre Modelling Toolbox with Parameters Estimation
Open this publication in new window or tab >>Towards Extension of Data Centre Modelling Toolbox with Parameters Estimation
2021 (English)In: Technological Innovation for Applied AI Systems: 12th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2021, Costa de Caparica, Portugal, July 7–9, 2021, Proceedings / [ed] Luis M. Camarinha-Matos; Pedro Ferreira; Guilherme Brito, Springer, 2021, p. 189-196Conference paper, Published paper (Refereed)
Abstract [en]

Modern data centres consume a significant amount of electricity. Therefore, they require techniques for improving energy efficiency and reducing energy waste. The promising energy-saving methods are those, which adapt the system energy use based on resource requirements at run-time. These techniques require testing their performance, reliability and effect on power consumption in data centres. Generally, real data centres cannot be used as a test site because of such experiments may violate safety and security protocols. Therefore, examining the performance of different energy-saving strategies requires a model, which can replace the real data centre. The model is expected to accurately estimate the energy consumption of data centre components depending on their utilisation. This work presents a toolbox for data centre modelling. The toolbox is a set of building blocks representing individual components of a typical data centre. The paper concentrates on parameter estimation methods, which use data, collected from a real data centre and adjust parameters of building blocks so that the model represents the data centre most accurately. The paper also demonstrates the results of parameters estimation on an example of EDGE module of SICS ICE data centre located in Luleå, Sweden. 

Place, publisher, year, edition, pages
Springer, 2021
Series
IFIP Advances in Information and Communication Technology, ISSN 1868-4238, E-ISSN 1868-422X ; 626
Keywords
Data centre, Modelling, Power consumption, Parameter estimation, Matlab/Simulink
National Category
Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-86673 (URN)10.1007/978-3-030-78288-7_18 (DOI)2-s2.0-85112024294 (Scopus ID)
Conference
12th Advanced Doctoral Conference on Computing, Electrical and Industrial Systems (DoCEIS 2021) Costa de Caparica, Portugal, 7–9 July, 2021
Funder
EU, Horizon 2020, 775970
Note

ISBN för värdpublikation: 978-3-030-78287-0; 978-3-030-78288-7;

Forskningsfinansiär: ERA-Net SES 2018 joint call RegSys

Available from: 2021-08-17 Created: 2021-08-17 Last updated: 2022-08-30Bibliographically approved
6. A hybrid fault detection and diagnosis method in server rooms’ cooling systems
Open this publication in new window or tab >>A hybrid fault detection and diagnosis method in server rooms’ cooling systems
Show others...
2019 (English)In: 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), IEEE, 2019, p. 1405-1410Conference paper, Published paper (Other academic)
Abstract [en]

Data centers as all complex systems are prone to faults, and cost of them can be very high. This paper is focused on detecting the faults in the cooling systems, in particular on local fans level. In the paper, a hybrid approach is proposed. In the approach a model is used as substitute of the real system to generate dataset containing records of both normal and fault cases. On the generated data, machine learning algorithm or ensemble of algorithms are selected and trained to detect the faults. To demonstrate the approach, the rack model of real data center is created, and reliability of the model is shown. Using the model, the dataset with normal as well as abnormal records of data is generated. To detect faults of local fans, simple classifiers are built for all pairs: a local fan – a processor unit. Classifiers are trained on one part of generated data (training data), and then their accuracy is estimated on another part of generated data (test data). A real-time fault detection system is built based on the classifiers. The rack model is used as the substitute of the real plant to check operability of the system.

Place, publisher, year, edition, pages
IEEE, 2019
Series
IEEE International Conference on Industrial Informatics (INDIN), ISSN 1935-4576, E-ISSN 2378-363X
Keywords
data center, cooling system, fault detection, classification
National Category
Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-75445 (URN)10.1109/INDIN41052.2019.8971959 (DOI)000529510400210 ()2-s2.0-85079044437 (Scopus ID)
Conference
2019 IEEE 17th International Conference on Industrial Informatics (INDIN), 22-25 July, 2019, Helsinki-Espoo, Finland
Note

ISBN för värdpublikation: 978-1-7281-2927-3, 978-1-7281-2928-0

Available from: 2019-08-08 Created: 2019-08-08 Last updated: 2022-08-30Bibliographically approved
7. Modular Model of a Data Centre as a Tool for Improving Its Energy Efficiency
Open this publication in new window or tab >>Modular Model of a Data Centre as a Tool for Improving Its Energy Efficiency
Show others...
2020 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 8, p. 46559-46573Article in journal (Refereed) Published
Abstract [en]

For most modern data centres, it is of high value to select practical methods for improving energy efficiency and reducing energy waste. IT-equipment and cooling systems are the two most significant energy consumers in data centres, thus the energy efficiency of any data centre mainly relies on the energy efficiency of its computational and cooling systems. Existing techniques of optimising the energy usage of both these systems have to be compared. However, such experiments cannot be conducted in real plants as they may harm the electronic equipment. This paper proposes a modelling toolbox which enables building models of data centres of any scale and configuration with relative ease. The toolbox is implemented as a set of building blocks which model individual components of a typical data centre, such as processors, local fans, servers, units of cooling systems, it provides methods of adjusting the internal parameters of the building blocks, as well as contains constructors utilising the building blocks for building models of data centre systems of different levels from server to the server room. The data centre model is meant to accurate estimating the energy consumption as well as the evolution of the temperature of all computational nodes and the air temperature inside the data centre. The constructed model capable of substitute for the real data centre at examining the performance of different energy-saving strategies in dynamic mode: the model provides information about data centre operating states at each time point (as model outputs) and takes values of adjustable parameters as the control signals from system implementing energy-saving algorithm (as model inputs). For Module 1 of the SICS ICE data centre located in Luleå, Sweden, the model was constructed from the building blocks. After adjusting the internal parameters of the building blocks, the model demonstrated the behaviour quite close to real data from the SICS ICE data centre. Therefore the model is applicable to use as a substitute for the real data centre. Some examples of using the model for testing energy-saving strategies are presented at the end of the paper.

Place, publisher, year, edition, pages
IEEE, 2020
National Category
Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-78312 (URN)10.1109/ACCESS.2020.2978065 (DOI)000524577400011 ()2-s2.0-85082013461 (Scopus ID)
Note

Validerad;2020;Nivå 2;2020-04-21 (alebob)

Available from: 2020-04-02 Created: 2020-04-02 Last updated: 2022-08-30Bibliographically approved
8. Smart Distribution of IT Load in Energy Efficient Data Centers with Focus on Cooling Systems
Open this publication in new window or tab >>Smart Distribution of IT Load in Energy Efficient Data Centers with Focus on Cooling Systems
2018 (English)In: Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, IEEE, 2018, p. 4907-4912Conference paper, Published paper (Refereed)
Abstract [en]

Cooling system is the second most energy-consuming part of a modern data center; herewith it often consumes energy inefficiently. Therefore, any possibility allowing to reduce energy consumption of cooling systems should be studied and put into practice if successful. In this paper, the idea about impact of IT load distribution between servers on energy consumption of data center cooling system is proposed. The idea is in distribution of total IT load between servers according to the location of the servers inside racks. To test idea, the model simulating the thermal behaviour and energy consumption of a real data center located in Northern Sweden is developed. Comparing the data obtained from the real plant and the data generated at the simulation shows the reliability of the model. Two strategies of IT load distribution between servers are considered, and their impact on energy consumption of cooling system is demonstrated.

Place, publisher, year, edition, pages
IEEE, 2018
Series
Annual Conference of Industrial Electronics Society, ISSN 1553-572X, E-ISSN 2577-1647
Keywords
data center, cooling systems, IT load distribution, thermal behaviour, energy consumption, simulation, mathematical modelling
National Category
Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
urn:nbn:se:ltu:diva-73023 (URN)10.1109/IECON.2018.8591122 (DOI)000505811104132 ()2-s2.0-85061559519 (Scopus ID)
Conference
44th Annual Conference of the IEEE Industrial Electronics Society (IECON 2018) 21-23 October, 2018, Washington D.C., USA
Note

ISBN för värdpublikation: 978-1-5090-6684-1, 978-1-5090-6685-8

Available from: 2019-02-26 Created: 2019-02-26 Last updated: 2022-08-30Bibliographically approved

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