An aggregation-based solution method for M/G/1-type processes

We extend the ETAQA approach, initially proposed for the efficient numerical solution of a class of quasi birth-death processes, to the more complex case of M/G/1-type Markov processes. The proposed technique can be used for the exact solution of a class of M/G/1-type models by computing the solution of a finite linear system. We further demonstrate the utility of the method by describing the exact computation of an extensive set of Markov reward functions such as the expected queue length or its higher moments. We illustrate the method, discuss its complexity, present comparisons with other traditional techniques, and illustrate its applicability in the area of computer system modeling.