IMPROVING TRAFFIC MANAGEMENT IN A DATA SWITCHED NETWORK USING AN ADAPTIVE DISCRETE TIME MARKOV MODULATED POISSON PROCESS

In the ever-evolving landscape of modern telecommunications, the increased demands on data networks driven by the proliferation of connected devices and the escalating requirements of bandwidth-intensive applications, the need for advanced traffic management strategies has become increasingly pronounced, thus presenting the aim of this paperto improving traffic management in a data switched network using an adaptive discrete-time Markov modulated Poisson process. The characterization of the data switched network and development ofa Poisson process algorithm was done for data throughput improvement. MATLAB Simulation was carried out to determine how to improve traffic Management by achieving effective capacity and effective bandwidth for QoS requirements. The result from the characterization of the data network showed that bandwidth usage was above 20Mbps most times and traffic congestion set in always.Results showed that the Poisson Process algorithm developed follows a Gaussian normal distribution pattern as flow throughput declines when traffic exceeds network capacity. The result also showed that traffic capacity is a function of transmission rate and that the higher the transmission rate, more the bandwidth is required. This work was able to achieve a 16.4% improvement in traffic management by implementing a hybrid of Poisson and Markov processes for effective capacity management in fixed-rate wireless transmission networks.