Aka the 'full stack developer' as opposed to the 'full stack developer' that knows both JavaScript, PHP and how to install the base distribution of CentOS.
as for the other kind of "full stack developer", the legendary creature that is equally comfortable doing UI scripting and Linux Kernel hacking...well personally I've never met one. mythical beast in my experience.
Anecdotally, this seems wrong but may actually be sadly right.
I don't know how you can get through a decent Computer Science program without knowing these things. Although, I acknowledge quality in CS programs vary hugely.
We had to write an Operating System (with basic memory and process management), know the OSI model up and down, and be able to deep dive into TCP/IP and UDP packets. In addition to coding and math.
I'm actually pro software "engineer" but perhaps we should draw the line somewhere on a bare minimum of skills required. Probably about the line of an ABET accredited undergraduate BS in CS degree?
This is simply not true as well. CS majors also can begin serious work with just a BS degree. I have no idea what school(s) the parent topic is refering to but remind me not to recommend people go there...
Four years is a LOT of time to get deep on multiple CS topics not just a broad overview. If you finish 4 years of serious study and don't have deep knowledge of a few things you did college wrong.
College is the only time in your adult life where your only job is to learn. It is incredible that many of us get that opportunity and that some people come out of it with just broad knowledge is disappointing.
In my CS program, senior and most of junior year were almost entirely specialized courses where we had a choice to pick a topic that interests us and deep dive. (AI, graphics, networking, bioinformatics, big data, programming language theory, etc). All the general studies were mostly done by the end of sophomore year.
You could have two identical ME grads go on to specialize in completely different things. One guy might spend his entire career working on hypoid gear sets and another might work exclusively on marine diesel rotating assemblies.
Ditto for EE, CE and so on.
At times it has been possible to make some progress on the complexity by regarding each application, virtual machine, server, server cluster, or server farm as a network of queues and apply queuing theory and/or Monte Carlo simulation. In addition some of the old work in optimization under uncertainty likely applies.
Network of queues can become hrrendoulsy difficult to analyze unless one assumes memoryless arrivals etc etc. Network calculus and stochastic network calculus simplifies things a great deal by going after bounds rather than exact answers. You might like it
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.
...and now you have :)
> Certainly no such topics were in any of the applied math grad courses I took.
Yeah they are a newer development. Stochastic network calculus more so. Network calculus is older, it even has a wikipedia page https://en.wikipedia.org/wiki/Network_calculus A key idea is convolution but in the max-plus algebra and martingale large deviations.
> For network queuing, really I was suggesting using that as a paradigm to formulate an analysis;
Indeed. SNCs can be a helpful tool there. Yes they are bounds, but a lot of progress has been made to make them tight https://arxiv.org/pdf/1303.4114.pdf they arent quite there yet numerically but tight enough to give an intuition. NC bounds are a lot looser.