DIFFUSION MODELS AND STEADY-STATE APPROXIMATIONS FOR EXPONENTIALLY ERGODIC MARKOVIAN QUEUES BY ITAI GURVICH Northwestern University

Motivated by queues with many-servers, we study Brownian steady-state approximations for continuous time Markov chains (CTMCs). Our approximations are based on diffusion models (rather than a diffusion limit) whose steady-state, we prove, approximates that of the Markov chain with notable precision. Strong approximations provide such “limitless ” approximations for process dynamics. Our focus here is on steady-state distributions, and the diffusion model that we propose is tractable relative to strong approximations. Within an asymptotic framework, in which a scale parameter n is taken large, a uniform (in the scale parameter) Lyapunov condition imposed on the sequence of diffusion models guarantees that the gap between the steadystate moments of the diffusion and those of the properly centered and scaled CTMCs shrinks at a rate of √ n. Our proofs build on gradient estimates for solutions of the Poisson equations associated with the (sequence of) diffusion models and on elementary Martingale arguments. As a by-product of our analysis, we explore connections between Lyapunov functions for the Fluid Model, the Diffusion Model and the CTMC. 1. Introduction. Flui