Interesting paper Dave, I've got a few thoughts:
That said, the performance metrics are derived from the embedded Markov chain of
the queuing system. This means the metrics are averages over *all of
time*, and thus there can be shorter periods (seconds, minutes, hours)
of much worse than average performance. Therefore the conclusions of the
paper should be taken with a grain of salt in my opinion.
Hi Dave,
IMHO the problem w.r.t the applicability of most models from
queueing theory is that they only work for load < 1, whereas
we are using the network with load values ~1 (i.e., around one) due to
congestion control feedback loops that drive the bottleneck link
to saturation (unless you consider application limited traffic sources).
To be fair there are queuing theory models that include packet loss (which is the case for the paper Dave is asking about here), and these can work perfectly well for load > 1. Agree about the CC feedback loops affecting the results though. Even if the distributions are general in the paper, they still assume samples are IID which is not true for real networks. Feedback loops make real traffic self-correlated, which makes the short periods of worse than average performance worse and more frequent than IID models might suggest.
Regards,
Bjørn Ivar
Regards,
Roland
On 27.07.22 at 17:34 Dave Taht via Starlink wrote:
> Occasionally I pass along a recent paper that I don't understand in
> the hope that someone can enlighten me.
> This is one of those occasions, where I am trying to leverage what I
> understand of existing FQ-codel behaviors against real traffic.
>
> https://www.hindawi.com/journals/mpe/2022/4539940/
>
> Compared to the previous study on finite-buffer M/M/1 priority queues
> with time and space priority, where service times are identical and
> exponentially distributed for both types of traffic, in our model we
> assume that service times are different and are generally distributed
> for different types of traffic. As a result, our model is more
> suitable for the performance analysis of communication systems
> accommodating multiple types of traffic with different service-time
> distributions. For the proposed queueing model, we derive the
> queue-length distributions, loss probabilities, and mean waiting times
> of both types of traffic, as well as the push-out probability of
> delay-sensitive traffic.
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