on the power of (even a little) centralization in distributed processing
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 steady-state delay, 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 in steady state scales as log 1/1- p 1/1-? when the traffic intensity ? goes to 1. This is exponentially smaller than the usual M/M/ 1-queue delay scaling of 1/1-?, obtained when all resources are fully allocated to local stations ( p =0). This indicates a strong qualitative impact of even a small degree of centralization. We prove convergence to a fluid limit, and characterize both the transient and steady-state behavior of the finite system, in the limit as the number of stations N goes to infinity. We show that the queue-length process converges to a unique fluid trajectory (over any finite time interval, as N ? ?), and that this fluid trajectory converges to a unique invariant state v I , for which a simple closed-form expression is obtained. We also show that the steady-state distribution of the N -server system concentrates on v I as N goes to infinity.
phase transition dynamic resource allocation partial centralization