Performance analysis of Markov regenerative reward models

The problem considered in the paper can not be solved by the traditional technique available for the analysis of Markov Regenerative Processes (MRGP). The widely used description of MRGPs, i.e. by the local and the global kernels, do not contain sufficient information on the process to evaluate the distribution of reward measures. A new analytical approach is proposed and studied to utilize better the Markov regenerative property for the analysis of Markov Regenerative Reward Models (MRRM). The distribution of the accumulated reward and the completion time of MRRMs is provided in transform domain, as well as considerations about the time domain numerical evaluation of these measures. As a simple application example the performance of a finite queueing system is analyzed.