Some assertions are written as "if (x) assert(0)" to avoid having
the text of a long argument compiled in the binary. Rewrite them
to use a new BRIEF_ASSERT macro to make the condition easier to read in
its non-negated form and make it easier to turn it back to the full-text
assert if needed.
Add a new parameter to limit the negative value of the step state
variable. It's set as a maximum delay in number of updates before the
actual step applied to the quantile estimate starts growing from the
minimum step when the input value is consistently larger or smaller than
the estimate.
This prevents the algorithm from effectively becoming the slower 1U
variant if the quantile estimate is stable most of the time.
Set it to 100 updates for the NTP delay and 1000 updates for the hwclock
delay. An option could be added later to make it configurable.
The algorithm was designed for estimating quantiles in streams of
integer values. When the estimate is equal to the input value, the
step state variable does not change. This causes problems for the
floating-point adaptation used for measurents of delay in chrony.
One problem is numerical instability due to the strict comparison of
the input value and the current estimate.
Another problem is with signals that are so stable that the nanosecond
resolution of the system functions becomes the limitation. There is a
large difference in the value of the step state variable, which
determines how quickly the estimate will adapt to a new distribution,
between signals that are constant in the nanosecond resolution and
signals that can move in two nanoseconds.
Change the estimate update to never consider the input value equal to
the current estimate and don't set the estimate exactly to the input
value. Keep it off by a quarter of the minimum step to force jumping
around the input value if it's constant and decreasing the step variable
to negative values. Also fix the initial adjustment to step at least by
the minimum step (the original algorithm is described with ceil(), not
fabs()).
Add estimation of quantiles using the Frugal-2U streaming algorithm
(https://arxiv.org/pdf/1407.1121v1.pdf). It does not need to save
previous samples and adapts to changes in the distribution.
Allow multiple estimates of the same quantile and select the median for
better stability.
