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@msfjarvis @yosh https://web.archive.org/web/20210425095523/https://www.cs.mun.ca/~wlodek/
Is the archived website of our professor back then.
At a quick glance, I think this paper seems to discuss most of it. https://scispace.com/pdf/modeling-and-performance-analysis-of-priority-queuing-3g6mmrdnxh.pdf
Like, this seriously informed my intuition for network applications, which was my career before Rust :).
@yosh Uh, uhm. I took a PhD course on how to model this and arrive roughly at that number (long story on how I was allowed to do this, even though I only hold a Masters). We did this on server-client systems, but what is a train but a request traveling through the internet.
Essentially, you can take timed petri-nets (a form of state machine with multiple states being active at once - think "trains on routes, in stations") and use a statistical function to model how long it takes for an token (train) in that net to go to the next state (every edge has a different function, runlength). You can then actually reason _locally_ about states at every node using markov chains. ("how many trains are at this station at any time with which probability") and then _back_-calculate that into delay probabilities.
Biggest learning: this isn't linear. It's almost linear, until the point it breaks, where delays grow rapidly and catastrophically over the network.
Which is also why 70% system load is about the moment where you should start thinking about upgrading your servers.
Sorry for the brain dump :).