Efficient estimation of the cell loss probability in a two-buffer PGPS scheduler

A key feature of integrated services networks is their ability to provide a variety of quality of service (QoS) guarantees to different applications. To this end scheduling systems can be employed. A primary QoS parameter is the cell loss probability (CLP), whose typical values are very small and difficult to estimate by means of standard simulation schemes. We propose an application of the importance sampling (IS) technique to efficiently estimate the CLP of an ideal two-queue generalized processor sharing (GPS) scheduling discipline. We subsequently apply this algorithm to simulate a realistic scheme, namely the packet-by-packet generalized processor sharing (PGPS). We model input traffic as Markov arrival processes (MAPs). The algorithm we present is based on large deviation results, which provide the asymptotic decay rate of per-session queue length tail distributions