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  • 1.
    Berezovskaya, Yulia
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Simulation-based development of distributed control systems in energy-efficient data centres2022Doctoral 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.

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  • 2.
    Berezovskaya, Yulia
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Berezovsky, Vladimir
    M.V. Lomonosov Northern (Arctic) Federal University, Arkhangelsk, Russia.
    Undozerova, Margarita
    M.V. Lomonosov Northern (Arctic) Federal University, Arkhangelsk, Russia.
    Data Exchange Between JADE and Simulink Model for Multi-agent Control Using NoSQL Database Redis2020In: Advances in Computer, Communication and Computational Sciences: Proceedings of IC4S 2019 / [ed] Sanjiv K. Bhatia, Shailesh Tiwari, Su Ruidan, Munesh Chandra Trivedi, K. K. Mishra, Springer, 2020, p. 695-705Conference paper (Refereed)
    Abstract [en]

    This paper describes the way for data exchange between Simulink model and JADE multi-agent control. Simulink model is the predictive thermal model for datacenter. Multi-agent control is aimed to optimize energy consumption of the datacenter ventilation system. The data exchange is carried out via NoSQL database Redis. The paper offers reasons for choosing Redis as middleware in the interaction of multi-agent control with Simulink model as well as the paper describes the Simulink blocks and agent in JADE that are developed for interaction (reading/writing) with Redis.

  • 3.
    Berezovskaya, Yulia
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Mousavi, Arash
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Vyatkin, Valeriy
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Department of Electrical Engineering and Automation, Aalto University, Finland.
    Zhang, Xiaojing
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. ABB Corporate Research, Västerås, Sweden .
    Smart Distribution of IT Load in Energy Efficient Data Centers with Focus on Cooling Systems2018In: Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, IEEE, 2018, p. 4907-4912Conference 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.

  • 4.
    Berezovskaya, Yulia
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Mousavi, Arash
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Vyatkin, Valeriy
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Zhang, Xiaojing
    ABB Corporate Research, Västerås, Sweden.
    Minde, Tor Björn
    SICS Swedish ICT, Luleå, Sweden.
    Improvement of Energy Efficiency in Data Centers via Flexible Humidity Control2016In: IECON Proceedings (Industrial Electronics Conference), Piscataway, NJ: IEEE Computer Society, 2016, p. 5585-5590, article id 7793777Conference 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.

  • 5.
    Berezovskaya, Yulia
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Yang, Chen-Wei
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Mousavi, Arash
    IT Department, SCANIA CV AB, Södertälje, Sweden.
    Vyatkin, Valeriy
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Department of Electrical Engineering and Automation, Aalto University, 02150 Helsinki, Finland.
    Minde, Tor Björn
    RISE SICS North, Luleå, Sweden.
    Modular Model of a Data Centre as a Tool for Improving Its Energy Efficiency2020In: IEEE Access, E-ISSN 2169-3536, Vol. 8, p. 46559-46573Article in journal (Refereed)
    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.

  • 6.
    Berezovskaya, Yulia
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Yang, Chen-Wei
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Mousavi, Arash
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Zhang, Xiaojing
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. ABB Corporate Research, Västerås, Sweden.
    Vyatkin, Valeriy
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Department of Electrical Engineering and Automation, Aalto University, Finland .
    A hybrid fault detection and diagnosis method in server rooms’ cooling systems2019In: 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), IEEE, 2019, p. 1405-1410Conference 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.

  • 7.
    Berezovskaya, Yulia
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Yang, Chen-Wei
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Vyatkin, Valeriy
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Reinforcement learning approach to implementation of individual controllers in data centre control system2022In: 2022 IEEE 20th International Conference on Industrial Informatics (INDIN), IEEE, 2022, p. 41-46Conference 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.

  • 8.
    Berezovskaya, Yulia
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Yang, Chen-Wei
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Vyatkin, Valeriy
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland.
    Towards Extension of Data Centre Modelling Toolbox with Parameters Estimation2021In: 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 (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. 

  • 9.
    Berezovskaya, Yulia
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Yang, Chen-Wei
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Vyatkin, Valeriy
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Department of Electrical Engineering and Automation, Aalto University, Finland .
    Towards Multi-Agent Control in Energy-Efficient Data Centres2020In: Proceedings: IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society, IEEE, 2020, p. 3574-3579Conference 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.

  • 10.
    Berezovskaya, Yulia
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Yang, Chen-Wei
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Vyatkin, Valeriy
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Aalto University, Helsinki, Finland.
    Towards reinforcement learning approach to energy-efficient control of server fans in data centres2021In: 2021 26th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA ), IEEE, 2021, p. 1-4Conference 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.

  • 11.
    Galkin, Nikolai
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Yang, Chen-Wei
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Berezovskaya, Yulia
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Vesterlund, Mattias
    ICE Data Center, RISE Research Institutes of Sweden AB, 97347 Luleå, Sweden.
    Vyatkin, Valeriy
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Department of Electrical Engineering and Automation, Aalto University, 02150 Espoo, Finland.
    On Modelling of Edge Datacentre Microgrid for Participation in Smart Energy Infrastructures2022In: IEEE Open Journal of the Industrial Electronics Society, E-ISSN 2644-1284, Vol. 3, p. 50-64Article in journal (Refereed)
    Abstract [en]

    Datacentres are becoming a sizable part of the energy system and are one of the biggest consumers of the energy grid. The so-called “Green Datacentre” is capable of not only consuming but also producing power, thus becoming an important kind of prosumers in the electric grid. Green datacentres consist of a microgrid with a backup uninterrupted power supply and renewable generation, e.g., using photovoltaic panels. As such, datacentres could realistically be important participants in demand/response applications. However, this requires reconsidering their currently rigid control and automation systems and the use of simulation models for online estimation of the control actions impact. This paper presents such a microgrid simulation model modelled after a real edge datacentre. A case study consumption scenario is presented for the purpose of validating the developed microgrid model against data traces collected from the green edge datacentre. Both simulation and real-time validation tests are performed to validate the accuracy of the datacentre model. Then the model is connected to the automation environment to be used for the online impact estimation and virtual commissioning purposes.

  • 12.
    Mousavi, Arash
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Berezovskaya, Yulia
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Vyatkin, Valeriy
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Department of Electrical Engineering a nd Automation, Aalto University.
    Zhang, Xiaojing
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Energy efficient decision making in data centers with multiple cooling methods2017In: Proceedings IECON 2017: 43rd Annual Conference of the IEEE Industrial Electronics Society, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 8785-8790Conference paper (Refereed)
    Abstract [en]

    Cooling systems consume around 40% of modern data centres' total energy consumption, thus reducing the energy waste in this sector will have positive environmental impact. There exist several cooling methods appropriate for particular conditions. Since environmental conditions, such as air temperature, change on seasonal bases, no single cooling method can be claimed to be the best. In contrast, data centres with multiple cooling methods can perform more efficiently. However, the decision-making process of which cooling system is the most appropriate one should be an automated process. In this paper, a decision-making process based on simulation is proposed. The simulation tool comprises of a mathematical model and a multi-agent control. The mathematical model simulates the thermal behaviour of SICS ICE data centre, which is a real facility located in Northern Sweden. The main aim of the simulation is to calculate thermal conditions and energy consumption of different cooling methods in cold and hot seasons. The result then will be used by multi-agent control to choose the most appropriate cooling method.

  • 13.
    Mousavi, Arash
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Vyatkin, Valeriy
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Berezovskaya, Yulia
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Zhang, Xiaojing
    ABB AB, Corporate research.
    Cyber-physical Design of Data Centers Cooling Systems Automation2015In: IEEE TrustCom-BigDataSE-ISPA 2015: Helsinki, 20-22 Aug. 2015, Piscataway, NJ: IEEE Communications Society, 2015, p. 254-260, article id 7345657Conference paper (Refereed)
    Abstract [en]

    Modern data centers in many aspects are akin to industrial plants that generate a lot of heat by consuming substantial amount of energy and require powerful cooling and ventilation. Cooling system contributes with 30 to 50% of the total energy consumption of data centers. An effective way to address energy efficiency in such cooling systems is to apply advanced automation solutions, similar to that of industrial and building automation systems. However, existing automation solutions are not flexible enough to meet requirements of cooling systems in modern data centres. This paper is an endeavour to utilize distributed adaptive automation architecture in order to improve energy efficiency of cooling. The proposed automation algorithms are validated in a simulation environment which models the thermal behaviour of a server room and helps to find the most energy efficient control strategy for controlling thecooling devices. This paper describes the simulation tool comprising of thermal behaviour modelling in MATLAB/SIMULINK connected in closed-loop with the distributed control environment of IEC 61499 standard. Simulation of a typical server room under certain constraints using the proposed tool is described and the results are presented. The results demonstrate the potential of improving higher energy efficiency, flexibility and better decision-making ability for controlling the cooling systems.

  • 14.
    Mousavi, Arash
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Vyatkin, Valeriy
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Berezovskaya, Yulia
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Zhang, Xiaojing
    ABB AB, Corporate research.
    Towards Energy Smart Data Centers: Simulation of Server Room Cooling System2015In: Proceedings of 2015 IEEE 20th International Conference on Emerging Technologies & Factory Automation (ETFA 2015): Luxembourg, 8-11 Sept. 2015, Piscataway, NJ: IEEE Communications Society, 2015, article id 7301573Conference paper (Refereed)
    Abstract [en]

    Cooling is an extremely important process in modern data centers. Cooling systems of server rooms ensure appropriate operation conditions to IT systems, such as servers and data storage, but, on the other side, they consume a lot of energy. Current control systems, which are installed in data centers and are responsible for thermal management of the facilities, are following conservative control strategies that maintain constant thermal conditions irrespective of computer load and outside temperature, thus efficient use of energy has not been appropriately addressed by them. In this paper, a method of optimizing energy consumption while maintaining an acceptable level of thermal comfort for CPUs in the server room is proposed.In the proposed method, a behavioral thermal model for serverroom should be created first, and then thermal behavior of theserver room would be simulated under different circumstancesusing its thermal model, in order to find an optimum controlstrategy capable of retaining balance between thermal comfortand efficient use of energy. The effectiveness of the proposedmethod has been investigated via simulating a typical serverroom using MATLAB and SIMULINK and the results aredemonstrated.

  • 15.
    Zhabelova, Gulnara
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Vesterlund, Mattias
    RISE SICS North, 973 47, Luleå.
    Eschmann, Sascha
    National Institute of Applied Sciences, Strasbourg, France.
    Berezovskaya, Yulia
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Vyatkin, Valeriy
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Department of Electrical Engineering and Automation, Aalto University, Helsinki .
    Flieller, Damien
    National Institute of Applied Sciences, Strasbourg, France.
    A Comprehensive Model of Data Center: from CPU to Cooling Tower2018In: IEEE Access, E-ISSN 2169-3536, Vol. 6, p. 2169-3536Article in journal (Refereed)
    Abstract [en]

    Aiming at addressing environmental challenges, large data centers such as Facebook, Google, Yahoo, etc., are increasing share of green power in their daily energy consumption. Such trends drive research into new directions, e.g. sustainable data centers. The research often relies on expressive models that provides sufficient details however practical to re-use and expand. There is a lack of available data center models that capture internal operating states of the facility from the CPU to the cooling tower. It is a challenge to develop a model that allows to describe complete data center of any scale including its connection to the grid. This paper proposes such a model building on existing work. The challenge was to put the pieces of data center together and model behavior of each element so that interdependencies between components and parameters and operating states are captured correctly and in sufficient details. The proposed model was used in the project “Data center microgrid integration” and proven to be adequate and important to support such study.

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