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.
Recent studies have shown that the superposition of packet sequences generated by packetized voice sources with speech detection exhibit high burstiness due to inherent correlations between successive interarrival times in the superposition stream.
We present an overview of recent results related to the single server queue with general independent and identically distributed service times and a batch Markovian arrival process (BMAP).
We develop an algorithm for numerically inverting multi-dimensional transforms.
Much energy is being devoted to studying the promising new asynchronous transfer mode (ATM) technology for supporting multiservice high-speed communication networks-e.g., see Roberts . As indicated in , interest in ATM is stimulated by two factors: First, by new technology making it possible to transmit and switch at very high bandwidths; and, second, by the growing demand for more sophisticated and powerful communication services.
Two of the many many applications of queueing models in communications networks are sizing links in transport networks and buffers in routers. A fundamental part of a queueing model is the arrival process. A Markov-modulated Poisson process (MMPP) is an attractive model for describing backbone packet traffic.