Application of queueing theory for the analysis of diagnostic processes in data communication systems

This paper presents an in-depth investigation into the application of queuing theory for the modeling, analysis, and optimization of diagnostic processes in data communication networks (DCNs). The research focuses on modeling both closed and open queuing systems (QS) to accurately describe the operational behavior of diagnostic subsystems within DCNs. Key performance indicators, including system load, queue lengths, waiting times, and throughput, are systematically analyzed for each type of queuing structure. To validate the theoretical models and assess system performance under varying load conditions, a detailed statistical simulation is conducted using the GPSS World simulation environment. The developed simulation models enable the evaluation of different diagnostic strategies and their impact on the overall efficiency and reliability of DCN operations. The findings contribute to the development of effective diagnostic frameworks that can enhance fault detection, reduce downtime, and improve the resilience of data transmission networks. The proposed approach can be employed for designing adaptive diagnostic systems capable of maintaining high performance in dynamic network environments.