Mathematical modeling and simulation of data-driven systems for efficient network performance optimization

Abstract

The high rate of heterogeneous networks growth and exponential increase in internet traffic has posed unprecedented challenges in ensuring optimum network performance. The problem of using traditional analytical models which are mostly on the basis of static assumptions is becoming insufficient to explain the dynamic, nonlinear behavior that is seen in modern data-driven communication systems. In this paper, it is described that stochastic queuing theory, Markov chain analysis, and machine learning-based digital twin (DT) technology are combined in a comprehensive mathematical modeling and simulation framework to optimize network performance metrics, such as latency, throughput, package delivery rate (PDR), and packet loss rate. The proposed hybrid model uses Deep Neural Network (DNN) architecture that is trained on high volume synthetic and real world traffic data that predicts congestion patterns and dynamically reconfigures routing decisions in real time. A digital twin layer is a version of the physical network topology that is simulated to allow experimental and what-if analysis to be done safely and offline (without affecting live traffic). Large-scale simulations with NS-3 and Python-based machine learning pipelines illustrate that the proposed model is able to achieve throughput efficiency 96.8, average end-to-end latency 3.2 ms, and a packet delivery ratio 97.5 in the case of heavy load. The statistical significance of improvements over baseline models, such as traditional M/M/1 queuing, Markov chain models, linear regression, and standalone DNN methods, are statistically significant in all key performance indicators. These findings confirm the feasibility of mathematical modeling based on data as a useful method of proactive, self-optimizing network management in practice. This paper will provide new knowledge about the combination of formal mathematical modeling with modern techniques of machine learning, providing a scalable and understandable solution to the optimization of network performance in the next generation.