Data centers, including edge data centers strategically positioned for critical applications, constitute vital components of today's technological infrastructure. Traditional data centers serve as centralized hubs supporting services like cloud computing, while edge centers, located nearer to end-users, play a pivotal role in applications such as augmented and virtual reality. These centers collectively ensure the efficient operation of digital services, providing necessary computing resources and minimizing delays for an optimal user experience. Addressing the dynamic challenges of these environments, effective cooling control strategies are imperative to mitigate energy consumption and optimize performance. Inadequate cooling not only impacts equipment functionality but also results in energy wastage, emphasizing the importance of tailored approaches to meet the dynamic demands of data center operations. The challenges in data center cooling, stemming from the dynamic workload and evolving computing demands, underscore the significance of developing model-based cooling control strategies. Traditional cooling methods may struggle to adapt, causing ineffective temperature regulation and potential hotspots. Intelligent cooling control strategies, rooted in models that dynamically adjust cooling resources based on real-time data and workload fluctuations, offer a solution. These model-based strategies enhance cooling efficiency, ensuring consistent temperature regulation while minimizing energy consumption. This approach becomes pivotal in supporting the sustainability and cost-effectiveness of data center operations amidst increasing computational demands. This licentiate thesis comprises three results that lead to solving the model-based data centers cooling controlproblems. The first result involves adaptive decoupling of multivariable systems,utilizing the extremum-seeking approach to dynamically adjust cooling resources based on real-time data, ensuring optimal efficiency. The second result focuses on online estimation of PID controllers and plant dynamics, enhancing precision and effectiveness through real-time adaptation to changing conditions within the dynamic landscape ofdata centers. The third result specifically applies empirical transfer function estimation for model fitting in a data center cooling model. These results provide guidance and insights to address cooling control design challenges that will be the future focus of this research.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:ltu-103555 |
| Date | January 2024 |
| Creators | Zaman, Amirreza |
| Publisher | Luleå tekniska universitet, Signaler och system, Luleå |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Licentiate thesis, comprehensive summary, info:eu-repo/semantics/masterThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | Licentiate thesis / Luleå University of Technology, 1402-1757 |
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