Resource allocation problems in service systems like data centers and ride-hailing are usually studied using queueing models. Such systems are primarily studied in the steady-state and in asymptotic regimes such as under heavy traffic due to their analytical tractability. However, almost all applications in real life do not operate in asymptotic regimes, and so, there is a clear discrepancy in translating theoretical queuing results to practical applications. In this work, we bridge this gap by presenting nonasymptotic and finite-time bounds for Erlang-C systems, providing a stepping stone towards understanding the transient behavior of more general queuing systems. We bound the Chi-square distance between the finite-time queue length distribution and the stationary distribution, show that it decays exponentially fast, and characterize the rate of decay. We observe that the Erlang-C system exhibits a phase transition, depending on a parameter that measures the load relative to the size of the system. We then use these results to obtain bounds on the mean queue length and tails of the queue lengths in finite time for the nonasymptotic system. We also establish that the rate we obtain is tight up to universal constants in appropriate heavy-traffic asymptotic regimes. We obtain these results using the Lyapunov-Poincaré approach, where we first carefully design a Lyapunov function to obtain a negative drift outside a finite set. Within the finite set, we develop different strategies depending on the properties of the finite set to get a handle on the mixing behavior via a local Poincaré inequality. A key aspect of our methodological contribution is obtaining tight guarantees in these two regions, which when combined, give us tight mixing time bounds. We believe that this approach is of independent interest for studying mixing in reversible countable-state Markov chains more generally.

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