title: ÐÐ¿ÑÐ¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ñ ÑÐ°ÑÐ¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð½Ð°Ð³ÑÑÐ·ÐºÐ¸ Ð² ÑÐ°ÑÐ¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð½ÑÑ Ð±Ð°Ð·Ð°Ñ Ð´Ð°Ð½Ð½ÑÑ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²ÑÑ ÑÐµÑ Ð½Ð¾Ð»Ð¾Ð³Ð¸Ð¹
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Optimization of load balancing in distributed databases using neural network technologies
Creators: name: ÐÑÑÐ¾Ð², ÐÐ¼Ð¸ÑÑÐ¸Ð¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2025
Subjects: ÑÐ°ÑÐ¿ÑÐµÐ´ÐµÐ»ÑÐ½Ð½ÑÐµ Ð±Ð°Ð·Ñ Ð´Ð°Ð½Ð½ÑÑ
Ð½ÐµÐ¹ÑÐ¾ÑÐµÑÐµÐ²ÑÐµ ÑÐµÑ Ð½Ð¾Ð»Ð¾Ð³Ð¸Ð¸
Ð±Ð°Ð»Ð°Ð½ÑÐ¸ÑÐ¾Ð²ÐºÐ° Ð½Ð°Ð³ÑÑÐ·ÐºÐ¸
Ð¿ÑÐ¾Ð¸Ð·Ð²Ð¾Ð´Ð¸ÑÐµÐ»ÑÐ½Ð¾ÑÑÑ
Ð¼Ð°ÑÑÑÐ°Ð±Ð¸ÑÑÐµÐ¼Ð¾ÑÑÑ
Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
distributed databases
neural network technologies
load balancing
performance
scalability
machine learning algorithms

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description: This work is devoted to a comparative analysis of solutions for load balancing in distributed databases and the development of a modern solution based on neural network technologies. The main focus of the work is on improving system performance and stability through self-learning and prediction under high load and scaling conditions. The objectives addressed in the research are: 1) To analyze the existing load balancing techniques in RDBs. 2) Studying the role and capabilities of neural network technologies for optimizing load distribution. 3) Developing a load balancing algorithm using machine learning. 4) Implementation of the developed algorithm and its testing on a data set close to real conditions. 5) Comparative analysis of the proposed approach with classical methods. The work was performed using Python programming language, TensorFlow program library and SQLite database. To model distributed systems, virtual environments were used to simulate the work of server solutions under high load. During development, performance statistics were collected and used to analyze and train neural meshes and load visualization tools. As a result of the research, a comparative analysis of existing solutions on the market was made and the main approaches based on the use of machine learning technologies were studied. A neural network-based solution was developed and tested, which showed better results compared to existing solutions. As a result, based on this research, a solution was developed that is applicable in e-commerce systems, banking, analytics and cloud technologies. In practice, the proposed solution will allow companies to reduce the cost of purchasing additional equipment and ensure high performance and fault tolerance of systems.