Does it make sense to calculate the variance of a percentile measurement of a timeseries? Developers often look at a percentile measurement of how long a task takes to complete as a measure of performance (e.g. 95th, 99th percentiles of page render time).
However, in order to measure the variance of how long it takes to complete that task, what's the more meaningful calculation - the variance of all the task lengths or just the variance of the 90th percentile lengths?
 A: I don't think this question has a clear statistical answer. It will come down to your particular requirements.
Think about the question each calculation answers. The first calculation, the variance of all task lengths, answers the question:

How much variation do we observe among all task lengths?

The second calculation answers a different question:

How much variation do we observe among the longest-running tasks?

The former gives a sense of overall spread. The latter gives a sense of spread in the upper tail. That is, it's a rough way of looking at the length of that tail. I say "rough" because it's delineated by an arbitrary cutoff — in this case the 90th percentile — and because there are probably more precise and targeted ways to measure tail length.
You can see how this works with some simulated data (in R):
N <- 10000
x <- rnorm(N)
y <- rt(N, 5)
boxplot(x, y, xaxt = "n")
abline(h = quantile(x, 0.9), col = "red")
abline(h = quantile(y, 0.9), col = "blue")
legend("bottom", paste("90th percentile: ", c("x", "y")), col = c("red", "blue"), lwd = 1)
var_90_pct <- function (x) var(x[x > quantile(x, 0.9)])
axis(1, 1:2, c("X: N(0,1)", "Y: T(5)"))
axis(1, 1:2, sprintf("90 pctile var = %.2f", c(var_90_pct(x), var_90_pct(y))), line = 2, tick = FALSE)
title(main = "Simulated Gaussian and Student t distributions\nwith 90th percentiles highlighted")


The Student t distribution (with few degrees of freedom) is distinguished from the standard Gaussian by its long tail. It is easy see on the graph that the Student t has much greater variance among the top 10% of observations, despite the 90th percentiles themselves being very close together.
This is why I don't think the question has a clear answer. Which one do you actually care about? Maybe if you are comfortable with the running time of your 90th percentile tasks but are concerned about a few extreme cases, the "tail-only variance" might be good to study. Variance is pretty cheap to compute; honestly you should probably just do both. And consider graphing the data while you're at it.
Edit:  I should mention that overall variance will also reveal long-tailed-ness. But it is more likely to be affected by other features of the distribution like multimodality, whereas the 90th percentile variance is probably going to be more focused.
