Hello,
On Wed, Oct 20, 2021 at 04:14:25PM +0530, Pratik Sampat wrote:
As you have elucidated, it doesn't like an easy feat toYeah, it gets tricky and we want to get the basics right from the get go.
define metrics like ballpark numbers as there are many variables
involved.
For the CPU example, cpusets control the resource space whereasSo, for CPU, the important functional number is the number of threads needed
period-quota control resource time. These seem like two vectors on
different axes.
Conveying these restrictions in one metric doesn't seem easy. Some
container runtime convert the period-quota time dimension to X CPUs
worth of runtime space dimension. However, we need to carefully model
what a ballpark metric in this sense would be and provide clearer
constraints as both of these restrictions can be active at a given
point in time and can influence how something is run.
to saturate available resources and that one is pretty easy.
The other
metric would be the maximum available fractions of CPUs available to the
cgroup subtree if the cgroup stays saturating. This number is trickier as it
has to consider how much others are using but would be determined by the
smaller of what would be available through cpu.weight and cpu.max.
IO likely is in a similar boat. We can calculate metrics showing the
rbps/riops/wbps/wiops available to a given cgroup subtree. This would factor
in the limits from io.max and the resulting distribution from io.weight in
iocost's case (iocost will give a % number but we can translate that to
bps/iops numbers).
Thank you,Restrictions for memory are even more complicated to model as you haveYeah, this one is the most challenging.
pointed out as well.
Thanks.