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Lets look at the core mechanism that allows BRIGHT to be uncertainty-aware: the Bayesian flow state estimation. For every active flow in the network, we dont just track a single rate estimate; we maintain a full Gaussian posterior distribution. This distribution, represented by the mean mu and the variance sigma squared, captures both our best guess of the flow rate and how confident we are in that guess. When we receive a new counter sample from a switch, we use standard Bayesian inference to update this posterior. The key here is that the new measurement is combined with our prior belief, giving us a refined estimate. If the new measurement is close to our prior mean, the variance shrinks significantly, meaning our confidence goes up. If the measurement is surprising, the mean shifts, but the variance might not shrink as much. This process is lightweight and runs continuously for thousands of flows, giving us a real-time, principled measure of estimation quality. But just knowing the posterior isnt enough; we need to turn that into actionable intelligence, which requires quantifying the uncertainty.
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IEEE/ICACT20260358 Slide.07
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