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Replica location planner for Federated ReplicaSet Controller
This commit is contained in:
Marcin Wielgus
parent
3d162d0400
commit
ed92579d2e
@ -0,0 +1,148 @@
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/*
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Copyright 2016 The Kubernetes Authors.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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*/
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package planer
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import (
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"sort"
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fed_api "k8s.io/kubernetes/federation/apis/federation"
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)
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// Planner decides how many out of the given replicas should be placed in each of the
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// federated clusters.
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type Planner struct {
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preferences *fed_api.FederatedReplicaSetPreferences
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}
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type namedClusterReplicaSetPreferences struct {
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clusterName string
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fed_api.ClusterReplicaSetPreferences
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}
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type byWeight []*namedClusterReplicaSetPreferences
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func (a byWeight) Len() int { return len(a) }
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func (a byWeight) Swap(i, j int) { a[i], a[j] = a[j], a[i] }
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// Preferences are sorted according by decreasing weight and increasing clusterName.
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func (a byWeight) Less(i, j int) bool {
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return (a[i].Weight > a[j].Weight) || (a[i].Weight == a[j].Weight && a[i].clusterName < a[j].clusterName)
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}
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func NewPlanner(preferences *fed_api.FederatedReplicaSetPreferences) *Planner {
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return &Planner{
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preferences: preferences,
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}
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}
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// Distribute the desired number of replicas among the given cluster according to the planner preferences.
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// The function tries its best to assign each cluster the prefered number of replicas, however if
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// sum of MinReplicas for all cluster is bigger thant replicasToDistribute then some cluster will not
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// have all of the replicas assigned. In such case a cluster with higher weight has priority over
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// cluster with lower weight (or with lexicographically smaller name in case of draw).
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func (p *Planner) Plan(replicasToDistribute int64, availableClusters []string) map[string]int64 {
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preferences := make([]*namedClusterReplicaSetPreferences, 0, len(availableClusters))
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plan := make(map[string]int64, len(preferences))
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named := func(name string, pref fed_api.ClusterReplicaSetPreferences) *namedClusterReplicaSetPreferences {
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return &namedClusterReplicaSetPreferences{
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clusterName: name,
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ClusterReplicaSetPreferences: pref,
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}
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}
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for _, cluster := range availableClusters {
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if localRSP, found := p.preferences.Clusters[cluster]; found {
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preferences = append(preferences, named(cluster, localRSP))
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} else {
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if localRSP, found := p.preferences.Clusters["*"]; found {
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preferences = append(preferences, named(cluster, localRSP))
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} else {
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plan[cluster] = int64(0)
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}
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}
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}
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sort.Sort(byWeight(preferences))
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remainingReplicas := replicasToDistribute
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// Assign each cluster the minimum number of replicas it requested.
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for _, preference := range preferences {
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min := minInt64(preference.MinReplicas, remainingReplicas)
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remainingReplicas -= min
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plan[preference.clusterName] = min
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}
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modified := true
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// It is possible single pass of the loop is not enough to distribue all replicas among clusters due
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// to weight, max and rounding corner cases. In such case we iterate until either
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// there is no replicas or no cluster gets any more replicas or the number
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// of attempts is less than available cluster count. Every loop either distributes all remainingReplicas
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// or maxes out at least one cluster.
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// TODO: This algorithm is O(clusterCount^2). When needed use sweep-like algorithm for O(n log n).
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for trial := 0; trial < len(availableClusters) && modified && remainingReplicas > 0; trial++ {
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modified = false
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weightSum := int64(0)
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for _, preference := range preferences {
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weightSum += preference.Weight
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}
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newPreferences := make([]*namedClusterReplicaSetPreferences, 0, len(preferences))
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distributeInThisLoop := remainingReplicas
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for _, preference := range preferences {
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if weightSum > 0 {
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start := plan[preference.clusterName]
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// Distribute the remaining replicas, rounding fractions always up.
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extra := (distributeInThisLoop*preference.Weight + weightSum - 1) / weightSum
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extra = minInt64(extra, remainingReplicas)
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// In total there should be the amount that was there at start plus whatever is due
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// in this iteration
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total := start + extra
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// Check if we don't overflow the cluster, and if yes don't consider this cluster
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// in any of the following iterations.
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if preference.MaxReplicas != nil && total > *preference.MaxReplicas {
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total = *preference.MaxReplicas
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} else {
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newPreferences = append(newPreferences, preference)
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}
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// Only total-start replicas were actually taken.
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remainingReplicas -= (total - start)
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plan[preference.clusterName] = total
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// Something extra got scheduled on this cluster.
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if total > start {
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modified = true
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}
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} else {
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break
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}
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}
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preferences = newPreferences
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}
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return plan
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}
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func minInt64(a int64, b int64) int64 {
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if a < b {
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return a
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}
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return b
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}
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@ -0,0 +1,156 @@
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/*
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Copyright 2016 The Kubernetes Authors.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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*/
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package planer
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import (
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"testing"
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fed_api "k8s.io/kubernetes/federation/apis/federation"
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"github.com/stretchr/testify/assert"
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)
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func doCheck(t *testing.T, pref map[string]fed_api.ClusterReplicaSetPreferences, replicas int64, clusters []string, expected map[string]int64) {
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planer := NewPlanner(&fed_api.FederatedReplicaSetPreferences{
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Clusters: pref,
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})
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plan := planer.Plan(replicas, clusters)
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assert.EqualValues(t, expected, plan)
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}
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func pint(val int64) *int64 {
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return &val
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}
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func TestEqual(t *testing.T) {
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doCheck(t, map[string]fed_api.ClusterReplicaSetPreferences{
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"*": {Weight: 1}},
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50, []string{"A", "B", "C"},
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map[string]int64{"A": 17, "B": 17, "C": 16})
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doCheck(t, map[string]fed_api.ClusterReplicaSetPreferences{
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"*": {Weight: 1}},
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50, []string{"A", "B"},
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map[string]int64{"A": 25, "B": 25})
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doCheck(t, map[string]fed_api.ClusterReplicaSetPreferences{
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"*": {Weight: 1}},
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1, []string{"A", "B"},
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map[string]int64{"A": 1, "B": 0})
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doCheck(t, map[string]fed_api.ClusterReplicaSetPreferences{
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"*": {Weight: 1}},
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1, []string{"A", "B", "C", "D"},
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map[string]int64{"A": 1, "B": 0, "C": 0, "D": 0})
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doCheck(t, map[string]fed_api.ClusterReplicaSetPreferences{
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"*": {Weight: 1}},
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1, []string{"A"},
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map[string]int64{"A": 1})
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doCheck(t, map[string]fed_api.ClusterReplicaSetPreferences{
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"*": {Weight: 1}},
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1, []string{},
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map[string]int64{})
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}
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func TestMin(t *testing.T) {
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doCheck(t, map[string]fed_api.ClusterReplicaSetPreferences{
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"*": {MinReplicas: 2, Weight: 0}},
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50, []string{"A", "B", "C"},
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map[string]int64{"A": 2, "B": 2, "C": 2})
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doCheck(t, map[string]fed_api.ClusterReplicaSetPreferences{
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"*": {MinReplicas: 20, Weight: 0}},
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50, []string{"A", "B", "C"},
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map[string]int64{"A": 20, "B": 20, "C": 10})
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doCheck(t, map[string]fed_api.ClusterReplicaSetPreferences{
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"*": {MinReplicas: 20, Weight: 0},
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"A": {MinReplicas: 100, Weight: 1}},
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50, []string{"A", "B", "C"},
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map[string]int64{"A": 50, "B": 0, "C": 0})
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doCheck(t, map[string]fed_api.ClusterReplicaSetPreferences{
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"*": {MinReplicas: 10, Weight: 1, MaxReplicas: pint(12)}},
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50, []string{"A", "B", "C"},
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map[string]int64{"A": 12, "B": 12, "C": 12})
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}
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func TestMax(t *testing.T) {
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doCheck(t, map[string]fed_api.ClusterReplicaSetPreferences{
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"*": {Weight: 1, MaxReplicas: pint(2)}},
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50, []string{"A", "B", "C"},
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map[string]int64{"A": 2, "B": 2, "C": 2})
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doCheck(t, map[string]fed_api.ClusterReplicaSetPreferences{
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"*": {Weight: 0, MaxReplicas: pint(2)}},
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50, []string{"A", "B", "C"},
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map[string]int64{"A": 0, "B": 0, "C": 0})
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}
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func TestWeight(t *testing.T) {
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doCheck(t, map[string]fed_api.ClusterReplicaSetPreferences{
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"A": {Weight: 1},
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"B": {Weight: 2}},
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60, []string{"A", "B", "C"},
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map[string]int64{"A": 20, "B": 40, "C": 0})
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doCheck(t, map[string]fed_api.ClusterReplicaSetPreferences{
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"A": {Weight: 10000},
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"B": {Weight: 1}},
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50, []string{"A", "B", "C"},
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map[string]int64{"A": 50, "B": 0, "C": 0})
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doCheck(t, map[string]fed_api.ClusterReplicaSetPreferences{
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"A": {Weight: 10000},
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"B": {Weight: 1}},
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50, []string{"B", "C"},
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map[string]int64{"B": 50, "C": 0})
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doCheck(t, map[string]fed_api.ClusterReplicaSetPreferences{
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"A": {Weight: 10000, MaxReplicas: pint(10)},
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"B": {Weight: 1},
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"C": {Weight: 1}},
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50, []string{"A", "B", "C"},
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map[string]int64{"A": 10, "B": 20, "C": 20})
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doCheck(t, map[string]fed_api.ClusterReplicaSetPreferences{
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"A": {Weight: 10000, MaxReplicas: pint(10)},
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"B": {Weight: 1},
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"C": {Weight: 1, MaxReplicas: pint(10)}},
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50, []string{"A", "B", "C"},
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map[string]int64{"A": 10, "B": 30, "C": 10})
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doCheck(t, map[string]fed_api.ClusterReplicaSetPreferences{
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"A": {Weight: 10000, MaxReplicas: pint(10)},
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"B": {Weight: 1},
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"C": {Weight: 1, MaxReplicas: pint(21)},
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"D": {Weight: 1, MaxReplicas: pint(10)}},
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71, []string{"A", "B", "C", "D"},
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map[string]int64{"A": 10, "B": 30, "C": 21, "D": 10})
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doCheck(t, map[string]fed_api.ClusterReplicaSetPreferences{
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"A": {Weight: 10000, MaxReplicas: pint(10)},
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"B": {Weight: 1},
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"C": {Weight: 1, MaxReplicas: pint(21)},
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"D": {Weight: 1, MaxReplicas: pint(10)},
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"E": {Weight: 1}},
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91, []string{"A", "B", "C", "D", "E"},
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map[string]int64{"A": 10, "B": 25, "C": 21, "D": 10, "E": 25})
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}
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