I never heard of "Network calculus and stochastic network calculus". Certainly no such topics were in any of the applied math grad courses I took. For "going

I never heard of "Network calculus and stochastic network calculus". Certainly no such topics were in any of the applied math grad courses I took. For "going after bounds rather than exact answers", for analyzing network performance, there is a hugely simplifying approach: Just look at the bottlenecks and largely f'get about the rest; maybe that is related to what you mentioned. For network queuing, really I was suggesting using that as a paradigm to formulate an analysis; I accept that analytic solutions are unpromising -- more generally, the exact probabilistic calculations out of queuing theory research are complicated even for simple cases. So, for solutions, i.e., actionable information, use Monte Carlo. Once I knew a guy at IBM's Watson lab who was big on such things. There was a claim that at one time his work was useful in designing some of an IBM mainframe I/O subsystem. He had some software that I used once. His software wanted to collect the usual descriptive statistics, but I wanted the sample paths from the Monte Carlo, got those, and did more analysis of those.