Analysis of windowing mechanisms with infinite-state stochastic Petri nets

In this paper we present a performance evaluation of windowing mechanisms in world-wide web applications. Previously, such mechanisms have been studied by means of measurements only, however, given suitable tool support, we show that such evaluations can also be performed conveniently using infinite-state stochastic Petri nets. We briefly present this class of stochastic Petri nets as well as the approach for solving the underlying infinite-state Markov chain using matrix-geometric methods. We then present a model of the TCP slow-start congestion avoidance mechanism, subject to a (recently published) typical world-wide web workload. The model is parameterized using measurement data for a national connection and an overseas connection. Our study shows how the maximum congestion window size, the connection release timeout and the packet loss probability influence the expected number of buffered segments at the server, the connection setup rate and the connection time. Furthermore, the crucial effect of correctly modeling the bursty nature of the system workload is illustrated by investigating several arrival models.