Queueing at large resources driven by long-tailed M/G/∞-modulated processes

Abstract

We analyze the queue at a buffer with input comprising sessions whose arrival is Poissonian, whose duration is long-tailed, and for which individual session detail is modeled as a stochastic fluid process. We obtain a large deviation result for the buffer occupation in an asymptotic regime in which the arrival rate nr, service rate ns, and buffer level nb are scaled to infinity with a parameter n. This can be used to approximate resources which multiplex many sources, each of which only uses a small proportion of the whole capacity, albeit for long-tailed durations. We show that the probability of overflow in such systems is exponentially small in n, although the decay in b is slower, reflecting the long tailed session durations. The requirements on the session detail process are, roughly speaking, that it self-averages faster than the cumulative session duration. This does not preclude the possibility that the session detail itself has a long-range dependent behavior, such as fractional Brownian motion, or another long-tailed M/G/∞ process. We show how the method can be used to determine the multiplexing gain available under the constraint of small delays (and hence short buffers) for multiplexers of large aggregates, and to compare the differential performance impact of increased buffering as opposed to load reduction.