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In this paper, we develop performance models of the Broadcast and Unkn own Server (BUS) in the LANE. The traffic on the BUS is divided into two classes: the broadcast and multicast traffic, and the unicast relay flow. The broadcast and multicast traffic is assumed to form a Markov Modulated Poisson Process (MMPP). The traffic for a particular unicast relay flow is an MMPP as well. However, the number of active unicast relay flows sojourning on the BUS is determined by a tandem queueing system, where the flow arrival process is Poisson, the address resolution delay is exponentially distributed, and the connection setup delay is three-stage Erlang distributed. The size of data frames in traffic flows is a random variable with three possible values: short, medium, and large. In order to deal with the intractability (i.e., largeness and stiffness) of the underlying Markov chain, a hierarchical model is used to decompose the system. With the help of Stochastic Petri Net Package (SPNP), a software package for the automated generation and solution of Markovian stochastic systems, and the decomposition method, we study the performance of the BUS module under different loads. We also investigate the effect of address resolution delay and connection setup delay on the performance of the BUS.

This paper presents a new algorithm (PMS-BDD) based on the binary decision diagram (BDD) for reliability analysis of phased-mission systems (PMS). PMS-BDD uses phase algebra to deal with the dependence across the phases, and a new BDD operation to incorporate the phase algebra. Due to the nature of the BDD, cancellation of common components among the phases can be combined with the BDD generation, without additional operations; and the sum of disjoint products (SDP) can be implicitly represented by the final BDD. Several examples and experiments show that PMS-BDD is more efficient than the algorithm based on SDP, in both computation time and storage space; this efficiency allows the study of some practical, large phased-mission systems.