Development and application of an efficient method for the solution of stochastic activity networks with deterministic activities

Modeling and evaluation of communication and computing systems is an important undertaking. In many cases, large-scale systems are designed in an ad-hoc manner, with validation (or disappointment regarding) system performance coming only after an implementation is made. This does not need to be the case. Modern modeling tools and techniques can yield accurate performance predictions that can be used in the design process. Stochastic activity networks (SANs), stochastic Petri nets (SPNs) and analytic solution methods permit speci cation and fast solution of many complex system models. To enhance the modeling power of SANs (SPNs), new steady-state analysis methods have been proposed for SAN (SPN) models that include non-exponential activities (transitions). The underlying stochastic process is a Markov regenerative precess (MRP) when at most one non-exponential activity (transition) is enabled in each marking. Timee cient algorithms for constructing the Markov regenerative process have been developed. However, the space required to solve such models is often extremely large. This largeness is due to the large number of transitions in the MRP. Traditional analysis methods require all these transitions be stored in memory for e cient computation. If the size of available memory is smaller than that needed to store these transitions, a time-e cient computation is impossible using these methods. To use this class of SANs to model real systems, the space complexity of MRP analysis algorithms must be reduced. In this thesis, we propose a new steady-state analysis method that is time and space e cient. The new method takes advantage of the structure of the underlying process to reduce both