For the 'single-numa' and 'restricted' TopologyManager policies, pods are only
admitted if all of their containers have perfect alignment across the set of
resources they are requesting. The best-effort policy, on the other hand, will
prefer allocations that have perfect alignment, but fall back to a non-preferred
alignment if perfect alignment can't be achieved.
The existing algorithm of how to choose the best hint from the set of
"non-preferred" hints is fairly naive and often results in choosing a
sub-optimal hint. It works fine in cases where all resources would end up
coming from a single NUMA node (even if its not the same NUMA nodes), but
breaks down as soon as multiple NUMA nodes are required for the "best"
alignment. We will never be able to achieve perfect alignment with these
non-preferred hints, but we should try and do something more intelligent than
simply choosing the hint with the narrowest mask.
In an ideal world, we would have the TopologyManager return a set of
"resources-relative" hints (as opposed to a common hint for all resources as is
done today). Each resource-relative hint would indicate how many other
resources could be aligned to it on a given NUMA node, and a hint provider
would use this information to allocate its resources in the most aligned way
possible. There are likely some edge cases to consider here, but such an
algorithm would allow us to do partial-perfect-alignment of "some" resources,
even if all resources could not be perfectly aligned.
Unfortunately, supporting something like this would require a major redesign to
how the TopologyManager interacts with its hint providers (as well as how those
hint providers make decisions based on the hints they get back).
That said, we can still do better than the naive algorithm we have today, and
this patch provides a mechanism to do so.
We start by looking at the set of hints passed into the TopologyManager for
each resource and generate a list of the minimum number of NUMA nodes required
to satisfy an allocation for a given resource. Each entry in this list then
contains the 'minNUMAAffinity.Count()' for a given resources. Once we have this
list, we find the *maximum* 'minNUMAAffinity.Count()' from the list and mark
that as the 'bestNonPreferredAffinityCount' that we would like to have
associated with whatever "bestHint" we ultimately generate. The intuition being
that we would like to (at the very least) get alignment for those resources
that *require* multiple NUMA nodes to satisfy their allocation. If we can't
quite get there, then we should try to come as close to it as possible.
Once we have this 'bestNonPreferredAffinityCount', the algorithm proceeds as
follows:
If the mergedHint and bestHint are both non-preferred, then try and find a hint
whose affinity count is as close to (but not higher than) the
bestNonPreferredAffinityCount as possible. To do this we need to consider the
following cases and react accordingly:
1. bestHint.NUMANodeAffinity.Count() > bestNonPreferredAffinityCount
2. bestHint.NUMANodeAffinity.Count() == bestNonPreferredAffinityCount
3. bestHint.NUMANodeAffinity.Count() < bestNonPreferredAffinityCount
For case (1), the current bestHint is larger than the
bestNonPreferredAffinityCount, so updating to any narrower mergeHint is
preferred over staying where we are.
For case (2), the current bestHint is equal to the
bestNonPreferredAffinityCount, so we would like to stick with what we have
*unless* the current mergedHint is also equal to bestNonPreferredAffinityCount
and it is narrower.
For case (3), the current bestHint is less than bestNonPreferredAffinityCount,
so we would like to creep back up to bestNonPreferredAffinityCount as close as
we can. There are three cases to consider here:
3a. mergedHint.NUMANodeAffinity.Count() > bestNonPreferredAffinityCount
3b. mergedHint.NUMANodeAffinity.Count() == bestNonPreferredAffinityCount
3c. mergedHint.NUMANodeAffinity.Count() < bestNonPreferredAffinityCount
For case (3a), we just want to stick with the current bestHint because choosing
a new hint that is greater than bestNonPreferredAffinityCount would be
counter-productive.
For case (3b), we want to immediately update bestHint to the current
mergedHint, making it now equal to bestNonPreferredAffinityCount.
For case (3c), we know that *both* the current bestHint and the current
mergedHint are less than bestNonPreferredAffinityCount, so we want to choose
one that brings us back up as close to bestNonPreferredAffinityCount as
possible. There are three cases to consider here:
3ca. mergedHint.NUMANodeAffinity.Count() > bestHint.NUMANodeAffinity.Count()
3cb. mergedHint.NUMANodeAffinity.Count() < bestHint.NUMANodeAffinity.Count()
3cc. mergedHint.NUMANodeAffinity.Count() == bestHint.NUMANodeAffinity.Count()
For case (3ca), we want to immediately update bestHint to mergedHint because
that will bring us closer to the (higher) value of
bestNonPreferredAffinityCount.
For case (3cb), we want to stick with the current bestHint because choosing the
current mergedHint would strictly move us further away from the
bestNonPreferredAffinityCount.
Finally, for case (3cc), we know that the current bestHint and the current
mergedHint are equal, so we simply choose the narrower of the 2.
This patch implements this algorithm for the case where we must choose from a
set of non-preferred hints and provides a set of unit-tests to verify its
correctness.
Signed-off-by: Kevin Klues <kklues@nvidia.com>
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