Адаптивна Динамічна Оптимізація Ресурсів В Системах З Multi-tenancy Архітектурою
Year: 2025
Type: article
Abstract: The article considers the problem of efficient allocation of computing resources in cloud software systems based on the principle of multi-tenant architecture. This approach allows to simultaneously serve several users within a single software instance while ensuring the isolation of their data and configurations. This approach reduces infrastructure costs and simplifies maintenance, but sharing resources creates new challenges associated with uneven load and potential overload of individual system components. The study analyzes classical approaches to distributing database connections among users, both static, which fix the restrictions in advance, and basic dynamic, which consider only the current number of requests. The limitations of these methods under conditions of variable and uneven load are revealed. A new adaptive methodology for dynamic resource optimization is proposed, which considers not only the intensity of requests but also the average processing time, historical activity indicators, and individual characteristics of each user. The methodology also allows considering the weighting factor that determines the impact of each factor on the final calculation. Experimental verification of the model based on three scenarios with different request intensities showed a significant reduction in the average response time by up to 20nbsp;% compared to the baseline method, without increasing the total number of connections used. The results demonstrate the effectiveness of the proposed approach in real-world conditions. This methodology can be implemented in modern cloud platforms to improve performance, peak load resilience, and rational use of infrastructure resources.
Authors: Вербівський, Владислав, Воловщиков, Валерій, Шапо, Владлен, Левінський, Максим, Левінський, Валерій
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7053820001
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