On the power of (even a little) resource pooling

We propose and analyze a multi-server model that captures a performance trade-off between centralized and distributed processing. In our model,a fraction p of an available resource is deployed in a centralized manner (e.g., to serve a most-loaded station) while the remaining fraction 1 p is allocated to local servers that can only serve requests addressed specifically to their respective stations. Using a fluid model approach, we demonstrate a surprising phase transition in the steady-state delay scaling, as p changes:in the limit of a large number of stations, and when any amount of centralization is available (p > 0), the average queue length insteady state scales as $log_{frac{1}{1-p}}{frac{1}{1-lambda}}$ when the traffic intensity λ goes to 1. This is exponentially smaller than the usual M/M/1-queue delay scaling of $frac{1}{1-lambda}$, obtained when all resources are fully allocated to local stations (p = 0). This indicates a strong qualitative impact of even a small degree of resource pooling. We prove convergence to a fluid limit, and characterize both the transient and steady-state behavior of the actual system, in the limit as the number of stations N goes to infinity. We show that the sequence of queue-length processes converges to a unique fluid trajectory (over any finite time interval, as N → ∞),and that this fluid trajectory converges to a unique invariant state vI, for which a simple closed-form expression is obtained.We also show that the steady-state distribution of the N-server system concentrates on vI as N goes to infinity.