Improving Traffic Management in a Data Switched Network Using an Adaptive Discrete Time Markov Modulated Poisson Process
The demand for resources like bandwidth must be met overall due to the growing number of wireless network users. When the number of competing users exceeds the capacity, there are insufficient resources to meet all traffic needs, which results in information loss and necessitates packet retransmissions. By characterizing the throughput in a data network under study, designing the data network traffic model, and developing a Poisson Process Algorithm and a Discrete Time Markov Modulated Poisson Process for data throughput improvement, this paper proposed ways of eradicating these negative consequences as a way to counteract this trend. To improve traffic management, effective capacity and effective bandwidth for Quality of Service (QoS) requirements were achieved by MATLAB simulation. Data network characteristics revealed that there was no network congestion when bandwidth utilization was less than 20 mbps. The created model was adaptive since it allowed high priority packets to transmit first when two packets attempted to contact the same node at the same time, as evidenced by monitoring of traffic nodes. As flow throughput decreases when traffic exceeds network capacity, the Poisson Process Algorithm designed follows Gaussian normal distribution pattern when the departure and arrival timings of packets are mutually independent.
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