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If $e^{-\theta N_{t\wedge {\sigma }_K}}$ is a nonnegative supermartingale, with ${\sigma }_K$ the first time $N_t\le K$, the optional stopping theorem yields ${\mathbb{P}}_N({\sigma }_K<\infty )\le e^{-\theta (N-K)}$. Such a bound can prove escape of a backlog independently of heuristic fluid models.

A nonnegative fluid model drains in finite time if all its coordinates approach zero at a finite terminal time. A ratio-based ordinary differential equation can become undefined at that endpoint; an absorbing extension is an additional modeling convention.

When backlog $N$ and attempt-control scale $S$ are large with $N/S\approx \kappa$, idle and success probabilities approach $e^{-\kappa }$ and $\kappa e^{-\kappa }$. Expected update increments then motivate a fluid model described by ordinary differential equations.

A fluid limit is a scaling limit of a stochastic process obtained by scaling space and time so that random fluctuations vanish and a deterministic trajectory remains. It can justify a fluid model when the required convergence conditions hold.