Modeling And Performance Analysis Of Network Link Transfers Control Based On Stochastic Petri Nets

Self-similarity nature of network traffic has severe effect on packet loss rate, throughput and queue capacity of network link transfer control. This leads to significant results difference between the traditional model and self-similarity traffic in network performance. Therefore, the traditional Markovian model is no longer applicable to analyze performance of self-similarity traffic. There is a great need to adopt new models and tools to study the performance of network link transfer control with self-similarity nature.Based on deep analysis of the characteristics of different traffic flow in network link transfer control, stochastic Petri net models for different traffic flow are presented. As far as the effects of self-similarity on network communication are concerned, diversified transfer source stochastic Petri net models are put forward in this paper. Then, a non markovian stochastic Petri net method is adopted to analyze the proposed models with simulation and verification based on SPNP tool.Simulation results show that throughput, packet loss rate and retransmission rate of network transfer control grows slowly for self-similarity traffic flow, but the average queue capacity increases quickly characterized with bursts, therefore more bandwidth is needed. Our work provides a quantitative analysis approach for improving the performance of network link control in different conditions and presents a non markvian model to analyze the performance.