METHODS OF PERFORMANCE OPTIMISATION IN DISTRIBUTED SYSTEMS WITH HIGH LOAD: STATE OF THE ART AND PROSPECTS

The study's relevance is stipulated by the growing need to improve the methods of optimising the performance of distributed systems with high load, which ensure stability and efficiency of operation in various industries, particularly in e-commerce, financial services and telecommunications.The study aims to analyse modern optimisation methods, identify the main problems and limitations of existing solutions, and develop recommendations for implementing innovative approaches to improve the performance and reliability of distributed systems.A systematic analysis of scientific publications was carried out as part of the work to identify key approaches to optimising the performance of distributed systems with high load.A comparative analysis of existing methods, including load balancing, caching, and scaling, was carried out.A survey methodology was used to identify the main problems and limitations, as well as to analyse the practical aspects of implementing modern technologies, in particular artificial intelligence and hybrid models, in real-world operating conditions.It is established that traditional methods, although providing basic stability of systems, do not take into account the dynamism of modern digital environments, which limits their adaptability and scalability.The key problems are identified, including dependence on data quality, high costs of implementing new technologies and the complexity of integrating adaptive mechanisms.It has been proved that the use of artificial intelligence, machine learning, and hybrid models that combine edge computing and cloud computing can significantly improve the efficiency of load management, reduce delays, and ensure high system performance.It is recommended to implement adaptive load monitoring and forecasting systems, use intelligent algorithms for resource management, and develop energy-efficient solutions to ensure scalability.Prospects for further research include the development of new adaptive resource management models, assessment of the economic efficiency of implementing innovative solutions, and analysis of their impact on the long-term stability of systems.