A Stochastic Reward Net Model for Performance Analysis of Prioritized DQDB MAN

The performance of prioritized DQDB MAN under bursty traffic environment is studied in this paper. The tagged node model is adopted to simplify the analysis. The processes of the packet arrivals to the tagged node, the empty-slot pattern from upstream, and the request pattern from downstream are assumed to be Markov Modulated Poisson Processes, a more general and appropriate model to capture the bursty characteristics existing in the traffic flows in high-speed networks. A stochastic reward nets model for the prioritized DQDB MAN is presented. With the help of the Stochastic Petri Net Package (SPNP) developed in Duke University, the steady state and as well as the transient behavior of packet loss ratio of tagged node are investigated. The influence of the burstiness of the empty-slot pattern, packet arrival and request pattern on the tagged node's performance are examined. Index Terms: DQDB, Priority, Stochastic Reward Nets, Stochastic Petri Net Package (SPNP) This research was supp..