Properly scaling queue consumers is a problem I've spent a lot of time on in the last few years. Working on a messaging platform with highly variable traffic, including close to zero during the night, means that capacity provisioning according to the max will be very costly, and lead to a lot of frustration when you are saturated anyway.

Indeed you need backpressure but the traditional methods (CPU usage or similar metrics) are difficult because many consumers aren't high on those metrics --imagine a messaging plaform, pure IO. Also you'd have to tailor to the consumer itself and that's difficult, which is what you mention on the next-to-last paragraph.

In the end I helped solving it by scaling based on queue size and input/output rates, agnostic to the consumer itself, but with the hypothesis that you can scale consumers linearly (or at least monotonically, some sublinearity is allowed). The queue scaler watches for incoming and outgoing traffic on the queue, plus items on the queue itself, and it can scale from 0 to 11 in seconds for gusts, then shutting everything down.

It's a satisfying problem to work on, but its proper solution demanded quite the investigation. Now every queue we've got in the system is managed by this autoscaler -- except when we can't ensure linearity.