CURRENT STATE AND DEVELOPMENT TRENDS OF QUEUE MANAGEMENT SYSTEMS IN INFOCOMMUNICATION NETWORKS

Abstract

This work provides a systematic analysis of the current state of the art and identifies key trends in the development of queueing management mechanisms for next-generation information and communication networks. It is demonstrated that ensuring a specified level of quality of service under conditions of highly dynamic traffic, a heterogeneous environment, and limited network resources is a critical task. It is determined that classical extensive approaches to increasing network capacity are giving way to intensive methods of intelligent scheduling and resource allocation. The aim of the study is to classify existing methodological approaches to queue management and conduct a comparative analysis of them to identify limitations and the most promising directions and application areas. During the study, several tasks were addressed: an analysis of the specifics of modern network architecture operation was conducted, and the requirements these architectures impose on queue management means were formulated. Methodological approaches were systematized and grouped into three main categories: QoS-oriented, optimization-based, and artificial intelligence-based. The advantages, limitations, and practical application areas of each approach were summarized based on a review of recent scientific publications. The research results showed that traditional QoS-oriented approaches, despite their simplicity of implementation, have low adaptability to changing network conditions. Optimization methods provide strict analytical quality guarantees; however, their use is limited by high computational complexity in large-scale networks. It has been established that the most promising direction for development is the intellectualization of management through machine learning. Further research should focus on developing hybrid models that combine the advantages of classical optimization with the flexibility of artificial intelligence algorithms, which will ensure a balance between computational efficiency and the network’s ability to provide quality of service guarantees in a dynamic environment.