kubernetes/pkg/kubelet/cm/topologymanager/policy.go

/*
Copyright 2019 The Kubernetes Authors.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/

package topologymanager

import (
	"k8s.io/klog/v2"
	"k8s.io/kubernetes/pkg/kubelet/cm/topologymanager/bitmask"
)

// Policy interface for Topology Manager Pod Admit Result
type Policy interface {
	// Returns Policy Name
	Name() string
	// Returns a merged TopologyHint based on input from hint providers
	// and a Pod Admit Handler Response based on hints and policy type
	Merge(providersHints []map[string][]TopologyHint) (TopologyHint, bool)
}

// IsAlignmentGuaranteed return true if the given policy guarantees that either
// the compute resources will be allocated within a NUMA boundary, or the allocation will fail at all.
func IsAlignmentGuaranteed(p Policy) bool {
	// We are abusing the name, but atm this matches almost 1:1 the policy name
	// so we are not adding new fields for now.
	return p.Name() == PolicySingleNumaNode
}

// Merge a TopologyHints permutation to a single hint by performing a bitwise-AND
// of their affinity masks. The hint shall be preferred if all hits in the permutation
// are preferred.
func mergePermutation(defaultAffinity bitmask.BitMask, permutation []TopologyHint) TopologyHint {
	// Get the NUMANodeAffinity from each hint in the permutation and see if any
	// of them encode unpreferred allocations.
	preferred := true
	var numaAffinities []bitmask.BitMask
	for _, hint := range permutation {
		// Only consider hints that have an actual NUMANodeAffinity set.
		if hint.NUMANodeAffinity != nil {
			numaAffinities = append(numaAffinities, hint.NUMANodeAffinity)
			// Only mark preferred if all affinities are equal.
			if !hint.NUMANodeAffinity.IsEqual(numaAffinities[0]) {
				preferred = false
			}
		}
		// Only mark preferred if all affinities are preferred.
		if !hint.Preferred {
			preferred = false
		}
	}

	// Merge the affinities using a bitwise-and operation.
	mergedAffinity := bitmask.And(defaultAffinity, numaAffinities...)
	// Build a mergedHint from the merged affinity mask, setting preferred as
	// appropriate based on the logic above.
	return TopologyHint{mergedAffinity, preferred}
}

func filterProvidersHints(providersHints []map[string][]TopologyHint) [][]TopologyHint {
	// Loop through all hint providers and save an accumulated list of the
	// hints returned by each hint provider. If no hints are provided, assume
	// that provider has no preference for topology-aware allocation.
	var allProviderHints [][]TopologyHint
	for _, hints := range providersHints {
		// If hints is nil, insert a single, preferred any-numa hint into allProviderHints.
		if len(hints) == 0 {
			klog.InfoS("Hint Provider has no preference for NUMA affinity with any resource")
			allProviderHints = append(allProviderHints, []TopologyHint{{nil, true}})
			continue
		}

		// Otherwise, accumulate the hints for each resource type into allProviderHints.
		for resource := range hints {
			if hints[resource] == nil {
				klog.InfoS("Hint Provider has no preference for NUMA affinity with resource", "resource", resource)
				allProviderHints = append(allProviderHints, []TopologyHint{{nil, true}})
				continue
			}

			if len(hints[resource]) == 0 {
				klog.InfoS("Hint Provider has no possible NUMA affinities for resource", "resource", resource)
				allProviderHints = append(allProviderHints, []TopologyHint{{nil, false}})
				continue
			}

			allProviderHints = append(allProviderHints, hints[resource])
		}
	}
	return allProviderHints
}

func narrowestHint(hints []TopologyHint) *TopologyHint {
	if len(hints) == 0 {
		return nil
	}
	var narrowestHint *TopologyHint
	for i := range hints {
		if hints[i].NUMANodeAffinity == nil {
			continue
		}
		if narrowestHint == nil {
			narrowestHint = &hints[i]
		}
		if hints[i].NUMANodeAffinity.IsNarrowerThan(narrowestHint.NUMANodeAffinity) {
			narrowestHint = &hints[i]
		}
	}
	return narrowestHint
}

func maxOfMinAffinityCounts(filteredHints [][]TopologyHint) int {
	maxOfMinCount := 0
	for _, resourceHints := range filteredHints {
		narrowestHint := narrowestHint(resourceHints)
		if narrowestHint == nil {
			continue
		}
		if narrowestHint.NUMANodeAffinity.Count() > maxOfMinCount {
			maxOfMinCount = narrowestHint.NUMANodeAffinity.Count()
		}
	}
	return maxOfMinCount
}

type HintMerger struct {
	NUMAInfo *NUMAInfo
	Hints    [][]TopologyHint
	// Set bestNonPreferredAffinityCount to help decide which affinity mask is
	// preferred amongst all non-preferred hints. We calculate this value as
	// the maximum of the minimum affinity counts supplied for any given hint
	// provider. In other words, prefer a hint that has an affinity mask that
	// includes all of the NUMA nodes from the provider that requires the most
	// NUMA nodes to satisfy its allocation.
	BestNonPreferredAffinityCount int
	CompareNUMAAffinityMasks      func(candidate *TopologyHint, current *TopologyHint) (best *TopologyHint)
}

func NewHintMerger(numaInfo *NUMAInfo, hints [][]TopologyHint, policyName string, opts PolicyOptions) HintMerger {
	compareNumaAffinityMasks := func(current, candidate *TopologyHint) *TopologyHint {
		// If current and candidate bitmasks are the same, prefer current hint.
		if candidate.NUMANodeAffinity.IsEqual(current.NUMANodeAffinity) {
			return current
		}

		// Otherwise compare the hints, based on the policy options provided
		var best bitmask.BitMask
		if (policyName != PolicySingleNumaNode) && opts.PreferClosestNUMA {
			best = numaInfo.Closest(current.NUMANodeAffinity, candidate.NUMANodeAffinity)
		} else {
			best = numaInfo.Narrowest(current.NUMANodeAffinity, candidate.NUMANodeAffinity)
		}
		if best.IsEqual(current.NUMANodeAffinity) {
			return current
		}
		return candidate
	}

	merger := HintMerger{
		NUMAInfo:                      numaInfo,
		Hints:                         hints,
		BestNonPreferredAffinityCount: maxOfMinAffinityCounts(hints),
		CompareNUMAAffinityMasks:      compareNumaAffinityMasks,
	}

	return merger
}

func (m HintMerger) compare(current *TopologyHint, candidate *TopologyHint) *TopologyHint {
	// Only consider candidates that result in a NUMANodeAffinity > 0 to
	// replace the current bestHint.
	if candidate.NUMANodeAffinity.Count() == 0 {
		return current
	}

	// If no current bestHint is set, return the candidate as the bestHint.
	if current == nil {
		return candidate
	}

	// If the current bestHint is non-preferred and the candidate hint is
	// preferred, always choose the preferred hint over the non-preferred one.
	if !current.Preferred && candidate.Preferred {
		return candidate
	}

	// If the current bestHint is preferred and the candidate hint is
	// non-preferred, never update the bestHint, regardless of how
	// the candidate hint's affinity mask compares to the current
	// hint's affinity mask.
	if current.Preferred && !candidate.Preferred {
		return current
	}

	// If the current bestHint and the candidate hint are both preferred,
	// then only consider fitter NUMANodeAffinity
	if current.Preferred && candidate.Preferred {
		return m.CompareNUMAAffinityMasks(current, candidate)

	}

	// The only case left is if the current best bestHint and the candidate
	// hint are both non-preferred. In this case, 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. current.NUMANodeAffinity.Count() >  bestNonPreferredAffinityCount
	//   2. current.NUMANodeAffinity.Count() == bestNonPreferredAffinityCount
	//   3. current.NUMANodeAffinity.Count() <  bestNonPreferredAffinityCount
	//
	// For case (1), the current bestHint is larger than the
	// bestNonPreferredAffinityCount, so updating to fitter 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 candidate hint is also equal to
	// bestNonPreferredAffinityCount and it is fitter.
	//
	// 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. candidate.NUMANodeAffinity.Count() >  bestNonPreferredAffinityCount
	//   3b. candidate.NUMANodeAffinity.Count() == bestNonPreferredAffinityCount
	//   3c. candidate.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
	// candidate hint, making it now equal to bestNonPreferredAffinityCount.
	//
	// For case (3c), we know that *both* the current bestHint and the
	// candidate hint 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. candidate.NUMANodeAffinity.Count() >  current.NUMANodeAffinity.Count()
	//   3cb. candidate.NUMANodeAffinity.Count() <  current.NUMANodeAffinity.Count()
	//   3cc. candidate.NUMANodeAffinity.Count() == current.NUMANodeAffinity.Count()
	//
	// For case (3ca), we want to immediately update bestHint to the
	// candidate hint 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 candidate hint would strictly move us further away from
	// the bestNonPreferredAffinityCount.
	//
	// Finally, for case (3cc), we know that the current bestHint and the
	// candidate hint are equal, so we simply choose the fitter of the 2.

	// Case 1
	if current.NUMANodeAffinity.Count() > m.BestNonPreferredAffinityCount {
		return m.CompareNUMAAffinityMasks(current, candidate)
	}
	// Case 2
	if current.NUMANodeAffinity.Count() == m.BestNonPreferredAffinityCount {
		if candidate.NUMANodeAffinity.Count() != m.BestNonPreferredAffinityCount {
			return current
		}
		return m.CompareNUMAAffinityMasks(current, candidate)
	}
	// Case 3a
	if candidate.NUMANodeAffinity.Count() > m.BestNonPreferredAffinityCount {
		return current
	}
	// Case 3b
	if candidate.NUMANodeAffinity.Count() == m.BestNonPreferredAffinityCount {
		return candidate
	}

	// Case 3ca
	if candidate.NUMANodeAffinity.Count() > current.NUMANodeAffinity.Count() {
		return candidate
	}
	// Case 3cb
	if candidate.NUMANodeAffinity.Count() < current.NUMANodeAffinity.Count() {
		return current
	}

	// Case 3cc
	return m.CompareNUMAAffinityMasks(current, candidate)

}

func (m HintMerger) Merge() TopologyHint {
	defaultAffinity := m.NUMAInfo.DefaultAffinityMask()

	var bestHint *TopologyHint
	iterateAllProviderTopologyHints(m.Hints, func(permutation []TopologyHint) {
		// Get the NUMANodeAffinity from each hint in the permutation and see if any
		// of them encode unpreferred allocations.
		mergedHint := mergePermutation(defaultAffinity, permutation)

		// Compare the current bestHint with the candidate mergedHint and
		// update bestHint if appropriate.
		bestHint = m.compare(bestHint, &mergedHint)
	})

	if bestHint == nil {
		bestHint = &TopologyHint{defaultAffinity, false}
	}

	return *bestHint
}

// Iterate over all permutations of hints in 'allProviderHints [][]TopologyHint'.
//
// This procedure is implemented as a recursive function over the set of hints
// in 'allproviderHints[i]'. It applies the function 'callback' to each
// permutation as it is found. It is the equivalent of:
//
// for i := 0; i < len(providerHints[0]); i++
//
//	for j := 0; j < len(providerHints[1]); j++
//	    for k := 0; k < len(providerHints[2]); k++
//	        ...
//	        for z := 0; z < len(providerHints[-1]); z++
//	            permutation := []TopologyHint{
//	                providerHints[0][i],
//	                providerHints[1][j],
//	                providerHints[2][k],
//	                ...
//	                providerHints[-1][z]
//	            }
//	            callback(permutation)
func iterateAllProviderTopologyHints(allProviderHints [][]TopologyHint, callback func([]TopologyHint)) {
	// Internal helper function to accumulate the permutation before calling the callback.
	var iterate func(i int, accum []TopologyHint)
	iterate = func(i int, accum []TopologyHint) {
		// Base case: we have looped through all providers and have a full permutation.
		if i == len(allProviderHints) {
			callback(accum)
			return
		}

		// Loop through all hints for provider 'i', and recurse to build the
		// permutation of this hint with all hints from providers 'i++'.
		for j := range allProviderHints[i] {
			iterate(i+1, append(accum, allProviderHints[i][j]))
		}
	}
	iterate(0, []TopologyHint{})
}