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That is absolutely not what queuing theory says. In equally simplified terms, queuing theory says that for a system that handles 99 events per second if you s
by antman 5y ago
That is absolutely not what queuing theory says. In equally simplified terms, queuing theory says that for a system that handles 99 events per second if you size it for 100 it will probably fail because 99 is just the mean, and when you overshoot (in events per second) you don’t have the capability to cover it during the undershoot (in events per second) period because events have piled up.
For a numerical example of queuing theory 101 and why variance is very important rather than something that “scares people away” [0]. Flow tracking is not enough.
Also this discussion is relevant
[0]:https://www.johndcook.com/blog/2008/10/21/what-happens-when-you-add-a-new-teller/ https://www.johndcook.com/blog/2008/10/21/what-happens-when-...
- vosper 5y agoI don’t know anything about queue theory, but I’m not sure how your point differs from GP? You both said that a system designed for 100 and being run at 99 falls apart at 101? I didn’t understand GPs comment about flows - perhaps that’s where the dispute is. Genuine question, looking forward to learning more :)
- antman 5y agoI am saying that the system should fail in terms of SLA even at 99. In the link I shared there is a simple worked example of 10.3 capacity/hr for 10 events/hr that piles up 28 events. It also shows the problem can be mitigated by analysing it in queuing/probabilistic terms. In real life it is even more complicated obviously!
- roenxi 5y agoIt is my example, I can set the variance to whatever I like. It is a D/D/1 queue in case that isn't abundantly clear. Which, in practice, is a model of queuing systems that often gets most of the value of queue theory and is easy to explain without jargon. Then apply an 80% fudge factor. Unless the situation needs care.