Using Robust Queueing to Expose the Impact of Dependence in Single-Server Queues

Queueing applications are often complicated by dependence among interarrival times and service times. Such dependence is common in networks of queues, where arrivals are departures from other queues or superpositions of such complicated processes, especially when there are multiple customer classes with class-dependent service-time distributions. We show that the robust queueing approach for single-server queues proposed in the literature can be extended to yield improved steady-state performance approximations in the standard stochastic setting that includes dependence among interarrival times and service times. We propose a newfunctional robust queueing formulation for the steady-state workload that is exact for the steady-state mean in the M/GI/1 model and is asymptotically correct in both heavy traffic and light traffic. Simulation experiments show that it is effective more generally.