Heavy-traffic limits for Discriminatory Processor Sharing models with joint batch arrivals
We study the performance of Discriminatory Processor Sharing (DPS) systems, with exponential service times and in which batches of customers of different types may arrive simultaneously according to a Poisson process. We show that the stationary joint queue-length distribution exhibits state-space collapse in heavy traffic: as the load ρ tends to 1, the scaled joint queue-length vector (1−ρ)Q converges in distribution to the product of a deterministic vector and an exponentially distributed random variable, with known parameters. The result provides new insights into the behavior of DPS systems. It shows how the queue-length distribution depends on the system parameters, and in particular, on the simultaneity of the arrivals. The result also suggests simple and fast approximations for the tail probabilities and the moments of the queue lengths in stable DPS systems, capturing the impact of the correlation structure in the arrival processes. Numerical experiments indicate that the approximations are accurate for medium and heavily loaded systems.
|Keywords||Discriminatory Processor Sharing, Batch arrivals, Joint queue-length distribution, Heavy traffic|
|Journal||Operations Research Letters|
Vis, P, Bekker, R, van der Mei, R.D, & Núñez Queija, R. (2020). Heavy-traffic limits for Discriminatory Processor Sharing models with joint batch arrivals. Operations Research Letters, 48(2), 136–141. doi:10.1016/j.orl.2020.01.004