Thank you, that is quite interesting! As you can see, even though I have an applied math degree, I still can't just analytically infer a distribution. You need

Thank you, that is quite interesting! As you can see, even though I have an applied math degree, I still can't just analytically infer a distribution. You need empirical results. This reminds me of the Jellyfish network topology for data centers (https://www.researchgate.net/publication/51943691_Jellyfish_Networking_Data_Centers_Randomly https://www.researchgate.net/publication/51943691_Jellyfish_...), where they found completely random route selection to be faster and more scalable than any other existing solution. I suspect, naively, that latency purely from network appliance queueing probably gives a mixture of Poissons in a straight line bus topology. But add in arbitrary routing and the much more complicated schedulers of the servers themselves, and you get no well-behaved distribution at all. It's especially interesting to compare the SLAs given by data center providers that tend to always use 99th or even 99.9th percentile worst case performance, with application metrics using arithmetic averages.