For values that continuously change, moving average or sum are good ways
to provide fast updates while handling temporal and errorneous variability
of the value.  For example, the access rate counter (nr_accesses) is
calculated as a sum of the number of positive sampled access check results
that collected during a discrete time window (aggregation interval), and
hence it handles temporal and errorneous access check results, but
provides the update only for every aggregation interval.  Using a moving
sum method for that could allow providing the value for every sampling
interval.  That could be useful for getting monitoring results snapshot or
running DAMOS in fine-grained timing.

However, supporting the moving sum for cases that number of samples in the
time window is arbirary could impose high overhead, since the number of
past values that it needs to keep could be too high.  The nr_accesses
would also be one of the cases.  To mitigate the overhead, implement a
pseudo-moving sum function that only provides an estimated pseudo-moving
sum.  It assumes there was no error in last discrete time window and
subtract constant portion of last discrete time window sum.

Note that the function is not strictly implementing the moving sum, but it
keeps a property of moving sum, which makes the value same to the
dsicrete-window based sum for each time window-aligned timing.  Hence,
people collecting the value in the old timings would show no difference.

Link: https://lkml.kernel.org/r/20230915025251.72816-4-sj@kernel.orgSigned-off-by: SeongJae Park <sj@kernel.org>
Cc: Brendan Higgins <brendanhiggins@google.com>
Signed-off-by: Andrew Morton <akpm@linux-foundation.org>