235 lines
9.2 KiB
Go
235 lines
9.2 KiB
Go
package core
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import (
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"fmt"
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"k8s.io/apimachinery/pkg/util/sets"
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clusterv1alpha1 "github.com/karmada-io/karmada/pkg/apis/cluster/v1alpha1"
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policyv1alpha1 "github.com/karmada-io/karmada/pkg/apis/policy/v1alpha1"
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workv1alpha2 "github.com/karmada-io/karmada/pkg/apis/work/v1alpha2"
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"github.com/karmada-io/karmada/pkg/util"
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"github.com/karmada-io/karmada/pkg/util/helper"
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)
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// TargetClustersList is a slice of TargetCluster that implements sort.Interface to sort by Value.
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type TargetClustersList []workv1alpha2.TargetCluster
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func (a TargetClustersList) Len() int { return len(a) }
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func (a TargetClustersList) Swap(i, j int) { a[i], a[j] = a[j], a[i] }
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func (a TargetClustersList) Less(i, j int) bool { return a[i].Replicas > a[j].Replicas }
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// divideReplicasByDynamicWeight assigns a total number of replicas to the selected clusters by the dynamic weight list.
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func divideReplicasByDynamicWeight(clusters []*clusterv1alpha1.Cluster, dynamicWeight policyv1alpha1.DynamicWeightFactor, spec *workv1alpha2.ResourceBindingSpec) ([]workv1alpha2.TargetCluster, error) {
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switch dynamicWeight {
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case policyv1alpha1.DynamicWeightByAvailableReplicas:
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return divideReplicasByResource(clusters, spec, policyv1alpha1.ReplicaDivisionPreferenceWeighted)
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default:
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return nil, fmt.Errorf("undefined replica dynamic weight factor: %s", dynamicWeight)
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}
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}
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func divideReplicasByResource(
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clusters []*clusterv1alpha1.Cluster,
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spec *workv1alpha2.ResourceBindingSpec,
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preference policyv1alpha1.ReplicaDivisionPreference,
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) ([]workv1alpha2.TargetCluster, error) {
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// Step 1: Find the ready clusters that have old replicas
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scheduledClusters := findOutScheduledCluster(spec.Clusters, clusters)
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// Step 2: calculate the assigned Replicas in scheduledClusters
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assignedReplicas := util.GetSumOfReplicas(scheduledClusters)
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// Step 3: Check the scale type (up or down).
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if assignedReplicas > spec.Replicas {
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// We need to reduce the replicas in terms of the previous result.
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newTargetClusters, err := scaleDownScheduleByReplicaDivisionPreference(spec, preference)
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if err != nil {
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return nil, fmt.Errorf("failed to scale down: %v", err)
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}
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return newTargetClusters, nil
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} else if assignedReplicas < spec.Replicas {
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// We need to enlarge the replicas in terms of the previous result (if exists).
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// First scheduling is considered as a special kind of scaling up.
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newTargetClusters, err := scaleUpScheduleByReplicaDivisionPreference(clusters, spec, preference, scheduledClusters, assignedReplicas)
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if err != nil {
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return nil, fmt.Errorf("failed to scaleUp: %v", err)
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}
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return newTargetClusters, nil
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} else {
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return scheduledClusters, nil
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}
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}
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// divideReplicasByStaticWeight assigns a total number of replicas to the selected clusters by the weight list.
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func divideReplicasByStaticWeight(clusters []*clusterv1alpha1.Cluster, weightList []policyv1alpha1.StaticClusterWeight,
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replicas int32) ([]workv1alpha2.TargetCluster, error) {
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weightSum := int64(0)
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matchClusters := make(map[string]int64)
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desireReplicaInfos := make(map[string]int64)
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for _, cluster := range clusters {
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for _, staticWeightRule := range weightList {
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if util.ClusterMatches(cluster, staticWeightRule.TargetCluster) {
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weightSum += staticWeightRule.Weight
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matchClusters[cluster.Name] = staticWeightRule.Weight
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break
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}
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}
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}
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if weightSum == 0 {
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for _, cluster := range clusters {
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weightSum++
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matchClusters[cluster.Name] = 1
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}
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}
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allocatedReplicas := int32(0)
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for clusterName, weight := range matchClusters {
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desireReplicaInfos[clusterName] = weight * int64(replicas) / weightSum
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allocatedReplicas += int32(desireReplicaInfos[clusterName])
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}
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clusterWeights := helper.SortClusterByWeight(matchClusters)
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var clusterNames []string
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for _, clusterWeightInfo := range clusterWeights {
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clusterNames = append(clusterNames, clusterWeightInfo.ClusterName)
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}
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divideRemainingReplicas(int(replicas-allocatedReplicas), desireReplicaInfos, clusterNames)
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targetClusters := make([]workv1alpha2.TargetCluster, len(desireReplicaInfos))
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i := 0
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for key, value := range desireReplicaInfos {
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targetClusters[i] = workv1alpha2.TargetCluster{Name: key, Replicas: int32(value)}
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i++
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}
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return targetClusters, nil
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}
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// divideReplicasByPreference assigns a total number of replicas to the selected clusters by preference according to the resource.
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func divideReplicasByPreference(
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clusterAvailableReplicas []workv1alpha2.TargetCluster,
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replicas int32,
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preference policyv1alpha1.ReplicaDivisionPreference,
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scheduledClusterNames sets.String,
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) ([]workv1alpha2.TargetCluster, error) {
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clustersMaxReplicas := util.GetSumOfReplicas(clusterAvailableReplicas)
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if clustersMaxReplicas < replicas {
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return nil, fmt.Errorf("clusters resources are not enough to schedule, max %d replicas are support", clustersMaxReplicas)
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}
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switch preference {
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case policyv1alpha1.ReplicaDivisionPreferenceAggregated:
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return divideReplicasByAggregation(clusterAvailableReplicas, replicas, scheduledClusterNames), nil
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case policyv1alpha1.ReplicaDivisionPreferenceWeighted:
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return divideReplicasByAvailableReplica(clusterAvailableReplicas, replicas, clustersMaxReplicas), nil
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default:
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return nil, fmt.Errorf("undefined replicaSchedulingType： %v", preference)
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}
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}
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func divideReplicasByAggregation(clusterAvailableReplicas []workv1alpha2.TargetCluster,
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replicas int32, scheduledClusterNames sets.String) []workv1alpha2.TargetCluster {
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clusterAvailableReplicas = resortClusterList(clusterAvailableReplicas, scheduledClusterNames)
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clustersNum, clustersMaxReplicas := 0, int32(0)
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for _, clusterInfo := range clusterAvailableReplicas {
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clustersNum++
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clustersMaxReplicas += clusterInfo.Replicas
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if clustersMaxReplicas >= replicas {
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break
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}
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}
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return divideReplicasByAvailableReplica(clusterAvailableReplicas[0:clustersNum], replicas, clustersMaxReplicas)
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}
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func divideReplicasByAvailableReplica(clusterAvailableReplicas []workv1alpha2.TargetCluster, replicas int32,
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clustersMaxReplicas int32) []workv1alpha2.TargetCluster {
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desireReplicaInfos := make(map[string]int64)
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allocatedReplicas := int32(0)
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for _, clusterInfo := range clusterAvailableReplicas {
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desireReplicaInfos[clusterInfo.Name] = int64(clusterInfo.Replicas * replicas / clustersMaxReplicas)
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allocatedReplicas += int32(desireReplicaInfos[clusterInfo.Name])
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}
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var clusterNames []string
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for _, targetCluster := range clusterAvailableReplicas {
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clusterNames = append(clusterNames, targetCluster.Name)
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}
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divideRemainingReplicas(int(replicas-allocatedReplicas), desireReplicaInfos, clusterNames)
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targetClusters := make([]workv1alpha2.TargetCluster, len(desireReplicaInfos))
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i := 0
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for key, value := range desireReplicaInfos {
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targetClusters[i] = workv1alpha2.TargetCluster{Name: key, Replicas: int32(value)}
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i++
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}
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return targetClusters
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}
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// divideRemainingReplicas divide remaining Replicas to clusters and calculate desiredReplicaInfos
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func divideRemainingReplicas(remainingReplicas int, desiredReplicaInfos map[string]int64, clusterNames []string) {
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if remainingReplicas <= 0 {
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return
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}
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clusterSize := len(clusterNames)
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if remainingReplicas < clusterSize {
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for i := 0; i < remainingReplicas; i++ {
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desiredReplicaInfos[clusterNames[i]]++
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}
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} else {
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avg, residue := remainingReplicas/clusterSize, remainingReplicas%clusterSize
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for i := 0; i < clusterSize; i++ {
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if i < residue {
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desiredReplicaInfos[clusterNames[i]] += int64(avg) + 1
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} else {
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desiredReplicaInfos[clusterNames[i]] += int64(avg)
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}
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}
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}
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}
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func scaleDownScheduleByReplicaDivisionPreference(
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spec *workv1alpha2.ResourceBindingSpec,
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preference policyv1alpha1.ReplicaDivisionPreference,
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) ([]workv1alpha2.TargetCluster, error) {
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// The previous scheduling result will be the weight reference of scaling down.
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// In other words, we scale down the replicas proportionally by their scheduled replicas.
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return divideReplicasByPreference(spec.Clusters, spec.Replicas, preference, sets.NewString())
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}
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func scaleUpScheduleByReplicaDivisionPreference(
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clusters []*clusterv1alpha1.Cluster,
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spec *workv1alpha2.ResourceBindingSpec,
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preference policyv1alpha1.ReplicaDivisionPreference,
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scheduledClusters []workv1alpha2.TargetCluster,
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assignedReplicas int32,
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) ([]workv1alpha2.TargetCluster, error) {
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// Step 1: Get how many replicas should be scheduled in this cycle and construct a new object if necessary
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newSpec := spec
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if assignedReplicas > 0 {
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newSpec = spec.DeepCopy()
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newSpec.Replicas = spec.Replicas - assignedReplicas
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}
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// Step 2: Calculate available replicas of all candidates
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clusterAvailableReplicas := calAvailableReplicas(clusters, newSpec)
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// Step 3: Begin dividing.
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// Only the new replicas are considered during this scheduler, the old replicas will not be moved.
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// If not, the old replicas may be recreated which is not expected during scaling up.
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// The parameter `scheduledClusterNames` is used to make sure that we assign new replicas to them preferentially
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// so that all the replicas are aggregated.
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result, err := divideReplicasByPreference(clusterAvailableReplicas, newSpec.Replicas,
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preference, util.ConvertToClusterNames(scheduledClusters))
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if err != nil {
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return result, err
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}
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// Step 4: Merge the result of previous and new results.
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return util.MergeTargetClusters(scheduledClusters, result), nil
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}
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