The versatile Markovian point process was introduced by M. F. Neuts in 1979. This is a rich class of point processes which contains many familiar arrival process as very special cases. Recently, the Batch Markovian Arrival Process, a class of point processes which was subsequently shown tobe equivalent to Neuts' point process, has been studied using a more transparent notation.
We study the performance of a statistical multiplexer whose inputs consist of a superposition of packetized voice sources and data. The performance analysis predicts voice packet delay distributions, which usually have a stringent requirement, as well as data packet delay distributions.