The data was being taken from general network traffic from multiple opcos for a tier 1 global network operator. The task wasn't to fix or improve anything, but to merely establish the baseline quality of the network as-is.
What is different about these metrics is the ability to extract the underlying causality, and to be able to (de)compose complete supply chains in a scientific manner (that would stand up in court). If you can capture timing data of the same packet passing multiple probing points, then you can use preexisting measurement capture systems. What matters if getting multi-point distributions, rather than single-point averages.
The inherent limitation of AQM is its goal: constructing exemplars of "success modes" for differential flow treatment, without considering what the "failure mode" risks are (which are significant and serious). That said, it prolongs the life of the current infrastructure, buying time to address the underlying science and engineering issues (like work conservation, emergent performance outcomes, and loss/delay trading that conflates degrees of freedom).
It doesn't matter what scheduling algorithm you build if it creates arbitrage or denial-of-service attacks that can arm a systemic collapse hazard. The good news is we have a new class of scheduling technology (that works on a different paradigm) that can fully address all of the requirements. We are currently deploying it to enable the world's first commercial quality-assured broadband service.
Martin