Оптимізація енергоспоживання в центрах даних на основі онтологічного підходу

The article is devoted to the development and substantiation of an ontological approach to optimizing energy consumption in data centers under conditions of increasing complexity of their computational and engineering infrastructure. The limitations of traditional optimization methods caused by insu...

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Bibliographic Details
Date:2026
Main Authors: Попик, Ю.І., Плес, Марек
Format: Article
Language:English
Published: Vinnytsia National Technical University 2026
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Online Access:https://oeipt.vntu.edu.ua/index.php/oeipt/article/view/822
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Journal Title:Optoelectronic Information-Power Technologies

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Optoelectronic Information-Power Technologies
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Summary:The article is devoted to the development and substantiation of an ontological approach to optimizing energy consumption in data centers under conditions of increasing complexity of their computational and engineering infrastructure. The limitations of traditional optimization methods caused by insufficient consideration of semantic dependencies between data center components, their operating modes, and management policies are analyzed. An applied domain ontology is proposed that provides a formalized representation of knowledge about data center structure, computational resources, engineering systems, workloads, and energy efficiency indicators. The paper performs a semantic decomposition of the domain into interrelated subsystems, identifies a subset of core concepts and relations of the general ontological model, and substantiates their use in the task of energy consumption optimization. A general scheme of the ontological approach is proposed, implementing a closed loop “data – knowledge – optimization – control” and ensuring the integration of the ontology with mathematical models and software-based management tools. The obtained results form a theoretical and methodological foundation for the development of intelligent energy management systems for data centers and can be applied in the design of energy-efficient and environmentally sustainable computing infrastructures.