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  • 1.
    Brännvall, Rickard
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Luleå University of Technology, Sweden.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Sandin, Fredrik
    Luleå University of Technology, Sweden.
    Modular and Transferable Machine Learning for Heat Management and Reuse in Edge Data Centers2023In: Energies, E-ISSN 1996-1073, Vol. 16, no 5, article id 2255Article in journal (Refereed)
    Abstract [en]

    This study investigates the use of transfer learning and modular design for adapting a pretrained model to optimize energy efficiency and heat reuse in edge data centers while meeting local conditions, such as alternative heat management and hardware configurations. A Physics-Informed Data-Driven Recurrent Neural Network (PIDD RNN) is trained on a small scale-model experiment of a six-server data center to control cooling fans and maintain the exhaust chamber temperature within safe limits. The model features a hierarchical regularizing structure that reduces the degrees of freedom by connecting parameters for related modules in the system. With a RMSE value of 1.69, the PIDD RNN outperforms both a conventional RNN (RMSE: 3.18), and a State Space Model (RMSE: 2.66). We investigate how this design facilitates transfer learning when the model is fine-tuned over a few epochs to small dataset from a second set-up with a server located in a wind tunnel. The transferred model outperforms a model trained from scratch over hundreds of epochs.

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  • 2.
    Brännvall, Rickard
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Sarkinen, Jeffrey
    RISE Research Institutes of Sweden.
    Svartholm, Joar
    RISE Research Institutes of Sweden.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Summers, Jon
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Digital Twin for Tuning of Server Fan Controllers2019In: 2019 IEEE 17th International Conference on Industrial Informatics (INDIN), 2019, p. 1425-1428Conference paper (Refereed)
    Abstract [en]

    Cooling of IT equipment consumes a large proportion of a modern data centre’s energy budget and is therefore an important target for optimal control. This study analyses a scaled down system of six servers with cooling fans by implementing a minimal data driven time-series model in TensorFlow/Keras, a modern software package popular for deep learning. The model is inspired by the physical laws of heat exchange, but with all parameters obtained by optimisation. It is encoded as a customised Recurrent Neural Network and exposed to the time-series data via n-step Prediction Error Minimisation (PEM). The thus obtained Digital Twin of the physical system is then used directly to construct a Model Predictive Control (MPC) type regulator that executes in real time. The MPC is then compared in simulation with a self-tuning PID controller that adjust its parameters on-line by gradient descent.

  • 3.
    Brännvall, Rickard
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Luleå University of Technology, Sweden.
    Siltala, Mikko
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Sarkinen, Jeffrey
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Vesterlund, Mattias
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Summers, Jon
    EDGE: Microgrid Data Center with Mixed Energy Storage2020In: e-Energy 2020 - Proceedings of the 11th ACM International Conference on Future Energy Systems, Association for Computing Machinery, Inc , 2020, p. 466-473Conference paper (Refereed)
    Abstract [en]

    Low latency requirements are expected to increase with 5G telecommunications driving data and compute to EDGE data centers located in cities near to end users. This article presents a testbed for such data centers that has been built at RISE ICE Datacenter in northern Sweden in order to perform full stack experiments on load balancing, cooling, micro-grid interactions and the use of renewable energy sources. This system is described with details on both hardware components and software implementations used for data collection and control. A use case for off-grid operation is presented to demonstrate how the test lab can be used for experiments on edge data center design, control and autonomous operation. © 2020 Author.

  • 4.
    Brännvall, Rickard
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science. Luleå University of Technology, Sweden.
    Stark, Tina
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Eriksson, Mats
    Arctos Labs Scandinvia AB, Sweden.
    Summers, Jon
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Cost Optimization by Energy Aware Workload Placement for the Edge Cloud Continuum2022Report (Other academic)
    Abstract [en]

    This report investigates the problem of where to place computation workload in an edge-cloud network topology considering the trade-off between the location specific cost of computation and data communication.

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  • 5.
    Brännvall, Rickard
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Stark, Tina
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Eriksson, Mats
    Arctos Labs Scandinavia AB, Sweden.
    Summers, Jon
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Cost Optimization for the Edge-Cloud Continuum by Energy-Aware Workload Placement2023In: Companion Proceedings of the 14th ACM International Conference on Future Energy Systems, Association for Computing Machinery , 2023, p. 79-84Conference paper (Refereed)
    Abstract [en]

    This article investigates the problem of where to place the computation workload in an edge-cloud network topology considering the trade-off between the location-specific cost of computation and data communication. For this purpose, a Monte Carlo simulation model is defined that accounts for different workload types, their distribution across time and location, as well as correlation structure. Results confirm and quantify the intuition that optimization can be achieved by distributing a part of cloud computation to make efficient use of resources in an edge data center network, with operational energy savings of 4–6% and up to 50% reduction in its claim for cloud capacity.

  • 6.
    Efkarpidis, Nikolaos
    et al.
    University of Applied Sciences and Arts Northwestern Switzerland, Switzerland.
    Imoscopi, Stefano
    Universita della Svizzera italiana, Switzerland.
    Bratukhin, Aleksey
    University for Continuing Education Krem, Austria.
    Brännvall, Rickard
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Franzl, Gerald
    University for Continuing Education Krem, Austria.
    Leopold, Thomas
    Institute of Computer Technology, Austria.
    Bauer, Valentin
    Institute of Computer Technology, Austria.
    Goranovic, Andrija
    Institute of Computer Technology, Austria.
    Wilker, Stefan
    Institute of Computer Technology, Austria.
    Yang, Chen-Wei
    Luleå University of Technology, Sweden.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Geidl, Martin
    University of Applied Sciences and Arts Northwestern Switzerland, Switzerland.
    Sauter, Thilo
    Institute of Computer Technology, Austria.
    Proactive Scheduling of Mixed Energy Resources at Different Grid Levels2024In: IEEE Transactions on Sustainable Energy, ISSN 1949-3029, E-ISSN 1949-3037, Vol. 15, p. 952-Article in journal (Refereed)
    Abstract [en]

    The optimal utilisation of distribution grids requires the proactive management of volatilities caused by mixed energy resources installed into different grid levels, such as buildings, energy communities (ECs) and substations. In this context, proactive control based on predictions for energy demand and generation is applied. The mitigation of conflicts between the stakeholders' objectives is the main challenge for the control of centralized and distributed energy resources. In this paper, a bi-level approach is proposed for the control of stationary battery energy storage systems (SBES) supporting the local distribution system operator (DSO) at the transformer level, as well as distributed energy resources (DERs) operated by end customers, i.e., EC-members. Model predictive control (MPC)- based and hybrid approaches merging rule- and MPC-based control schemes are evaluated. Simulation studies based on a typical European low voltage (LV) feeder topology yield the performance assessment in terms of technical and economic criteria. The results show an advantage of hybrid approaches with respect to the DSO's cost savings from peak shaving. From the EC's perspective, both hybrid and MPC-based schemes can achieve effective cost savings from proactive energy management.

  • 7.
    Fredriksson, Sebastian
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Eleftheriadis, Lackis
    Ericsson Research, Sweden.
    Brännvall, Rickard
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Bäckman, Nils
    Delta Electronics AB, Sweden.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    ANIARA: Experimental Investigation of Micro Edge Data Centers with Battery Support on Power-Constrained Grids2023In: Companion Proceedings of the 14th ACM International Conference on Future Energy Systems, Association for Computing Machinery , 2023, p. 72-78Conference paper (Refereed)
    Abstract [en]

    As the demand for data privacy and low latency grows, edge computation carried out at edge data center nodes is believed to become increasingly important for future telecom applications. Providers must consider various factors, including power consumption, thermal dynamics, and the ability to maintain high-quality service, in addition to deployment and service orchestration. This paper presents a detailed description of two different prototype edge data centers designed to investigate the power performance and thermal dynamics of edge nodes under various applied services. The prototypes were developed and tested at the RISE ICE Datacenter research facility. We present the results of power flow experiments in which input current from the grid was limited while the computational load was maintained using the energy stored in batteries. We further discuss implications for placing edge data center nodes in locations with temporal power constraints and opportunities for participation in support services at the grid level.

  • 8.
    Gustafsson, Jonas
    et al.
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Fredriksson, Sebastian
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Nilsson-Mäki, Magnus
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Olsson, Daniel
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS. RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Sarkinen, Jeffrey
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Niska, Henrik
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Seyvet, Nicolas
    OP5, Sweden.
    Minde, Tor Björn
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Summers, Jon
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    A demonstration of monitoring and measuring data centers for energy efficiency using opensource tools2018In: e-Energy 2018 - Proceedings of the 9th ACM International Conference on Future Energy Systems, 2018, p. 506-512Conference paper (Refereed)
    Abstract [en]

    Data centers are complex systems that require sophisticated operational management approaches to provide the availability of digital services against the backdrop of cost and energy efficiency. To achieve this, data center telemetry data is required since, as is commonly said it is not possible to manage what cannot be measured. This paper details how it is possible to construct the key data center infrastructure management (DCIM) elements of monitoring and measuring by a combination of available opensource software tools that permit both scalability and an environment where analytics can be employed on the data center operation, which can offer relevant insight into energy efficient operational practices.

  • 9.
    Heimerson, Albin
    et al.
    Lund University, Sweden.
    Brännvall, Rickard
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Sjölund, Johannes
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Eker, Johan
    Luleå University of Technology, Sweden; Ericsson Research, Sweden.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Towards a Holistic Controller: Reinforcement Learning for Data Center Control2021In: Proceedings of the Twelfth ACM International Conference on Future Energy Systems, Association for Computing Machinery , 2021, p. 424-429Conference paper (Refereed)
    Abstract [en]

    The increased use of cloud and other large scale datacenter IT services and the associated power usage has put the spotlight on more energy-efficient datacenter management. In this paper, a simple model was developed to represent the heat rejection system and energy usage in a small DC setup. The model was then controlled by a reinforcement learning agent that handles both the load balancing of the IT workload, as well as cooling system setpoints. The main contribution is the holistic approach to datacenter control where both facility metrics, IT hardware metric and cloud service logs are used as inputs. The application of reinforcement learning in the proposed holistic setup is feasible and achieves results that outperform standard algorithms. The paper presents both the simplified DC model and the reinforcement learning agent in detail and discusses how this work can be extended towards a richer datacenter model.

  • 10.
    Heimerson, Albin
    et al.
    Lund University, Sweden.
    Sjölund, Johannes
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Brännvall, Rickard
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Eker, Johan
    Lund University, Sweden; Ericsson Research, Sweden.
    Adaptive Control of Data Center Cooling using Deep Reinforcement Learning2022In: Proceedings - 2022 IEEE International Conference on Autonomic Computing and Self-Organizing Systems Companion, ACSOS-C 2022, Institute of Electrical and Electronics Engineers Inc. , 2022Conference paper (Refereed)
    Abstract [en]

    In this paper, we explore the use of Reinforcement Learning (RL) to improve the control of cooling equipment in Data Centers (DCs). DCs are inherently complex systems, and thus challenging to model from first principles. Machine learning offers a way to address this by instead training a model to capture the thermal dynamics of a DC. In RL, an agent learns to control a system through trial-and-error. However, for systems such as DCs, an interactive trial-and-error approach is not possible, and instead, a high-fidelity model is needed. In this paper, we develop a DC model using Computational Fluid Dynamics (CFD) based on the Lattice Boltzmann Method (LBM) Bhatnagar-Gross-Krook (BGK) algorithm. The model features transient boundary conditions for simulating the DC room, heat-generating servers, and Computer Room Air Handlers (CRAHs) as well as rejection components outside the server room such as heat exchangers, compressors, and dry coolers. This model is used to train an RL agent to control the cooling equipment. Evaluations show that the RL agent can outperform traditional controllers and also can adapt to changes in the environment, such as equipment breaking down. 

  • 11.
    John, Wolfgang
    et al.
    Ericsson, Sweden.
    Balador, Ali
    Ericsson, Sweden.
    Taghia, Jalil
    Ericsson, Sweden.
    Johnsson, Andreas
    Ericsson, Sweden.
    Sjöberg, Johan
    Ericsson, Sweden.
    Marsh, Ian
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Tonini, Federico
    Chalmers University of Technology, Sweden.
    Monti, Paolo
    Chalmers University of Technology, Sweden.
    Sköldström, Pontus
    Qamcom AB, Sweden.
    Dowling, Jim
    Hopsworks AB, Sweden.
    ANIARA Project - Automation of Network Edge Infrastructure and Applications with Artificial Intelligence2023In: ACM SIGAda Ada Letters, Vol. 42, no 2, p. 92-95Article in journal (Refereed)
    Abstract [en]

    Emerging use-cases like smart manufacturing and smart cities pose challenges in terms of latency, which cannot be satisfied by traditional centralized infrastructure. Edge networks, which bring computational capacity closer to the users/clients, are a promising solution for supporting these critical low latency services. Different from traditional centralized networks, the edge is distributed by nature and is usually equipped with limited compute capacity. This creates a complex network to handle, subject to failures of different natures, that requires novel solutions to work in practice. To reduce complexity, edge application technology enablers, advanced infrastructure and application orchestration techniques need to be in place where AI and ML are key players.

  • 12.
    Ljungdahl Eriksson, Martin
    et al.
    Luleå University of Technology, Sweden.
    Lucchese, Riccardo
    Luleå University of Technology, Sweden.
    Gustafsson, Jonas
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Ljung, Anna-Lena
    Luleå University of Technology, Sweden.
    Mousavi, Arash
    Luleå University of Technology, Sweden.
    Varagnolo, Damiano
    Luleå University of Technology, Sweden.
    Monitoring and modelling open compute servers2017In: Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society, Institute of Electrical and Electronics Engineers Inc. , 2017, p. 7177-7184Conference paper (Refereed)
    Abstract [en]

    Energy efficient control of server rooms in modern data centers can help reducing the energy usage of this fast growing industry. Efficient control, however, cannot be achieved without: i) continuously monitoring in real-time the behavior of the basic thermal nodes within these infrastructures, i.e., the servers; ii) analyzing the acquired data to model the thermal dynamics within the data center. Accurate data and accurate models are indeed instrumental for implementing efficient data centers cooling strategies. In this paper we focus on a class of Open Compute Servers, designed in an open-source fashion and currently deployed by Facebook. We thus propose a set of methods for collecting real-time data from these platforms and a control-oriented model describing the thermal dynamics of the CPUs and RAMs of these servers as a function of both manipulable and exogenous inputs (e.g., the CPU utilization levels and the air mass flow produced by the server's fans). We identify the parameters of this model from real data and make the results available to other researchers.

  • 13.
    Marsh, Ian
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Paladi, Nicolae
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Abrahamsson, Henrik
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Sjöberg, Johan
    Ericsson AB, Sweden.
    Johnsson, Andreas
    Ericsson AB, Sweden.
    Sköldström, Pontus
    Ericsson AB, Sweden.
    Dowling, Jim
    Logical Clocks AB, Sweden.
    Monti, Paolo
    Chalmers University of Technology, Sweden.
    Vruna, Melina
    Opel Automobile GmbH, Germany.
    Amiribesheli, Mohsen
    Konica Minolta Global RandD, UK.
    Evolving 5G: ANIARA, an edge-cloud perspective2021In: Proceedings of the 18th ACM International Conference on Computing Frontiers 2021, CF 2021, Association for Computing Machinery, Inc , 2021, p. 206-207Conference paper (Refereed)
    Abstract [en]

    ANIARA (https://www.celticnext.eu/project-ai-net) attempts to enhance edge architectures for smart manufacturing and cities. AI automation, orchestrated lightweight containers, and efficient power usage are key components of this three-year project. Edge infrastructure, virtualization, and containerization in future telecom systems enable new and more demanding use cases for telecom operators and industrial verticals. Increased service flexibility adds complexity that must be addressed with novel management and orchestration systems. To address this, ANIARA will provide en-ablers and solutions for services in the domains of smart cities and manufacturing deployed and operated at the network edge(s). © 2021 Owner/Author.

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  • 14.
    Marsh, Ian
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Paladi, Nicolae
    RISE Research Institutes of Sweden.
    Abrahamsson, Henrik
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Sjöberg, Johan
    Ericsson AB, Sweden.
    Johnsson, Andreas
    Ericsson AB, Sweden.
    Sköldström, Pontus
    Ericsson AB, Sweden.
    Dowling, Jim
    Logical Clocks AB, Sweden.
    Monti, Paolo
    Chalmers University of Technology, Sweden.
    Vruna, Melina
    Opel Automobile GmbH, Germany.
    Amiribesheli, Mohsen
    Konica Minolta Global R&D, UK.
    Evolving 5G: ANIARA, an edge-cloud perspective2021In: CF '21: Proceedings of the 18th ACM International Conference on Computing FrontiersMay 2021, Association for Computing Machinery , 2021, p. 206-207Conference paper (Refereed)
    Abstract [en]

    ANIARA (https://www.celticnext.eu/project-ai-net) attempts to enhance edge architectures for smart manufacturing and cities. AI automation, orchestrated lightweight containers, and efficient power usage are key components of this three-year project. Edge infrastructure, virtualization, and containerization in future telecom systems enable new and more demanding use cases for telecom operators and industrial verticals. Increased service flexibility adds complexity that must be addressed with novel management and orchestration systems. To address this, ANIARA will provide en-ablers and solutions for services in the domains of smart cities and manufacturing deployed and operated at the network edge(s).

  • 15.
    Sarkinen, Jeffrey
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Brännvall, Rickard
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Summers, Jon
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Experimental Analysis of Server Fan Control Strategies for Improved Data Center Air-based Thermal Management*2020In: 2020 19th IEEE Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic Systems (ITherm), 2020, p. 341-349Conference paper (Refereed)
    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.

  • 16.
    Siltala, Mikko
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Brännvall, Rickard
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Zhou, Quan
    Aalto University, Finland.
    Physical and Data-Driven Models for Edge Data Center Cooling System2020In: 2020 Swedish Workshop on Data Science, SweDS 2020, Institute of Electrical and Electronics Engineers Inc. , 2020Conference paper (Refereed)
    Abstract [en]

    Edge data centers are expected to become prevalent providing low latency computing power for 5G mobile and IoT applications. This article develops two models for the complete cooling system of an edge data center: One model based on the laws of thermodynamics and one data-driven model based on LSTM neural networks. The models are validated against an actual edge data center experimental set-up showing root mean squared errors (RMSE) for most individual components below 1 °C over a simulation period of approximately 10 hours; which compares favourably to state-of-the-art models. 

  • 17.
    Simonazzi, Emanuele
    et al.
    University of Padova, Italy.
    Galrin, Miguel Ramos
    KTH Royal Institute of Technology, Sweden.
    Varagnolo, Damiano
    Luleå University of Technology, Sweden.
    Gustafsson, Jonas
    RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.
    Garcia-Gabin, Winston
    ABB Corporate Research, Sweden.
    Detecting and modelling air flow overprovisioning / underprovisioning in air-cooled datacenters2018In: Proceedings: IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, 2018, p. 4893-4900Conference paper (Refereed)
    Abstract [en]

    When cooling and exhaust air flows in air-cooled datacenters mix, the energetic efficiency of the cooling operations drops. One way to prevent this mixing of happening is by augmenting the air tightness of the hot and cold aisles; this, however, requires installing opportune hardware that may be expensive and require time consuming installations. Alternatively, one may try to minimize cooling and exhaust air flows mixing by opportunely controlling the speeds of the fans of the Computer Room Air Handling (CRAH) units so that the distribution of the air pressure field within the computer room is favorable. Implementing this type of flow control requires both detecting when there actually is some type of flow mixing somewhere, plus understanding how to operate the cooling infrastructure so that these mixings do not happen. To this aim, there is the need for models that can both help deciding whether these mixing events occur, plus designing automatic control strategies for reducing the risks that they will happen. In this manuscript, we propose an ad-hoc methodology for the data-driven derivation of control-oriented models that serve the purposes above. The methodology is built on classical Prediction Error Method (PEM) approaches to the system identification problem, and on laddering on the peculiarities of the physics of the phenomena under consideration. Moreover, we test and assess the methodology on a industrial-scale air-cooled datacenter with an installed capacity of 240 kW, and verify that the obtained models are able to capture the dynamics of the system in all its potential regimes.

  • 18.
    Taddeo, Paolo
    et al.
    IREC Catalonia Institute for Energy Research, Spain.
    Romaní, Joaquim
    IREC Catalonia Institute for Energy Research, Spain.
    Summers, Jon
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Gustafsson, Jonas
    RISE Research Institutes of Sweden, Digital Systems, Data Science.
    Martorell, Ingrid
    Universitat de Lleida, Spain.
    Salom, Jaume
    IREC Catalonia Institute for Energy Research, Spain.
    Experimental and numerical analysis of the thermal behaviour of a single-phase immersion-cooled data centre2023In: Applied Thermal Engineering, ISSN 1359-4311, E-ISSN 1873-5606, Vol. 234, article id 121260Article in journal (Refereed)
    Abstract [en]

    Server power densities are foreseen to increase, and conventional air-cooling systems will struggle to cope with thermal demand. Single-phase immersion systems are a promising alternative to operate very intensive workload such as high-performance computing, cryptocurrencies mining or research activities. However, few companies deal with this kind of system and there is a lack of energy models that can reproduce an accurate analysis of the system behaviour. This study addresses the experimentation, data collection, and model validation of a single-phase immersion cooling system where 54 open compute project servers, each with a peak power of 400 Watts that are submerged and operated in a dielectric coolant. Results show the evolution of the thermal profile of the system under static and dynamic workloads, and it provides a correlation of server energy use under various system temperatures. The energy model is presented, validated against real data, and exploited to investigate the system response to different cooling conditions. In conclusion, the study demonstrates the validation of the energy model and supports the basis for further investigation. © 2023 The Authors

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