3 ms·

I’m not an expert on queue theory but this little “tidbit” has always bothered me. As far as I can tell, it is true IFF your variability causes the average wpos

I’m not an expert on queue theory but this little “tidbit” has always bothered me. As far as I can tell, it is true IFF your variability causes the average wpos to go up, which negates the entire premise of “requests arriving at 1000/s” If you’re averaging 1000/s and you get 1010 req in one sec, yes this leads to an infinite queue - but your average is now above 1000/s! Then if a few seconds later you get 990 requests, the queue catches up (because your avg is back to 1000) IOW - it assumes only positive variability and no negative variability which does not mimic real world systems in my experience. It’s an academic exercise that’s interesting to think about, but has no practical application. Else we would see infinite queue backups every time a system received a burst of traffic beyond their maximum throughput in any given time period. What am I missing?

hansvm 4y ago
Just code up a 5-line simulation and see what happens? You do actually expect the server to eventually recover from all load spikes, but that dead time means cumulatively you'll typically have queued more requests than you've handled at long enough time scales. On average that surplus is infinite, despite it sometimes being as low as 0. It's not just academic, but it's not surprising that doesn't echo your real-world experience either. We don't usually run systems anywhere near capacity, and even when you do you'll still expect the queue to be able to handle the load eventually.