简介
scheduler 负责给一个pod 找一个node，经过预选和优选两个阶段，优选阶段会为 通过预选的node 打一个分，然后择优录取。k8s 很多调度策略 比如nodeAffinity 本质上就是 打分实现的。
打分组件主要包括scheduler 内嵌的 ScorePlugin 以及 扩展的Scheduler extender 组件。难点
- 给定一个pod 和node，如何计算分数
- ScorePlugin 与 Scheduler extender 均有多个，多个score 如何算出一个总分？ 权重。
整体设计
对每一个node 可以返回score 1到10 。每个score plugin 为node 算一个分（有权重），scheduler extender为node 算一个分（有权重），按权重为一个node累计一个总分。
// k8s.io/kubernetes/pkg/scheduler/core/generic_scheduler.go
func (g *genericScheduler) prioritizeNodes(
ctx context.Context,
prof *profile.Profile,
state *framework.CycleState,
pod *v1.Pod,
nodes []*v1.Node,
) (framework.NodeScoreList, error) {
// 如果既没有扩展插件， 也没有内嵌score插件，每个node score=1
if len(g.extenders) == 0 && !prof.HasScorePlugins() {
result := make(framework.NodeScoreList, 0, len(nodes))
for i := range nodes {
result = append(result, framework.NodeScore{
Name: nodes[i].Name,
Score: 1,
})
}
return result, nil
}
// Run the Score plugins. 每个插件 针对每个node 算一个评分
scoresMap, scoreStatus := prof.RunScorePlugins(ctx, state, pod, nodes)
// Summarize all scores. 对评分求和
result := make(framework.NodeScoreList, 0, len(nodes))
for i := range nodes {
result = append(result, framework.NodeScore{Name: nodes[i].Name, Score: 0})
for j := range scoresMap {
result[i].Score += scoresMap[j][i].Score
}
}
if len(g.extenders) != 0 && nodes != nil {
combinedScores := make(map[string]int64, len(nodes))
for i := range g.extenders {
go func(extIndex int) {
prioritizedList, weight, err := g.extenders[extIndex].Prioritize(pod, nodes)
for i := range *prioritizedList {
host, score := (*prioritizedList)[i].Host, (*prioritizedList)[i].Score
// 每个extender 插件再算一个分，并基于权重累计
combinedScores[host] += score * weight
}
}(i)
}
// result 内嵌评分（最大100），combinedScores extenders 评分（最大10），最大值不一致， 所以把combinedScores 扩大10倍 加入到 result 上
for i := range result {
// MaxExtenderPriority may diverge from the max priority used in the scheduler and defined by MaxNodeScore,
// therefore we need to scale the score returned by extenders to the score range used by the scheduler.
result[i].Score += combinedScores[result[i].Name] * (framework.MaxNodeScore / extenderv1.MaxExtenderPriority)
}
}
return result, nil
}
每个node 计算出一个score之后，哪个node 的score 最高，pod 就调度到哪个node 上。
// k8s.io/kubernetes/pkg/scheduler/core/generic_scheduler.go
func (g *genericScheduler) Schedule(ctx context.Context, prof *profile.Profile, state *framework.CycleState, pod *v1.Pod) (result ScheduleResult, err error) {
...
priorityList, err := g.prioritizeNodes(ctx, prof, state, pod, filteredNodes)
host, err := g.selectHost(priorityList)
return ScheduleResult{
SuggestedHost: host,
EvaluatedNodes: len(filteredNodes) + len(filteredNodesStatuses),
FeasibleNodes: len(filteredNodes),
}, err
}
func (g *genericScheduler) selectHost(nodeScoreList framework.NodeScoreList) (string, error) {
if len(nodeScoreList) == 0 {
return "", fmt.Errorf("empty priorityList")
}
maxScore := nodeScoreList[0].Score
selected := nodeScoreList[0].Name
cntOfMaxScore := 1
for _, ns := range nodeScoreList[1:] {
if ns.Score > maxScore {
maxScore = ns.Score
selected = ns.Name
cntOfMaxScore = 1
} else if ns.Score == maxScore {
cntOfMaxScore++
if rand.Intn(cntOfMaxScore) == 0 {
// Replace the candidate with probability of 1/cntOfMaxScore
selected = ns.Name
}
}
}
return selected, nil
}
各个score 插件的默认权重
// k8s.io/kubernetes/pkg/scheduler/algorithmprovider/registry.go
func getDefaultConfig() *schedulerapi.Plugins {
return &schedulerapi.Plugins{
...
Score: &schedulerapi.PluginSet{
Enabled: []schedulerapi.Plugin{
{Name: noderesources.BalancedAllocationName, Weight: 1},
{Name: imagelocality.Name, Weight: 1},
{Name: interpodaffinity.Name, Weight: 1},
{Name: noderesources.LeastAllocatedName, Weight: 1},
{Name: nodeaffinity.Name, Weight: 1},
{Name: nodepreferavoidpods.Name, Weight: 10000},
{Name: defaultpodtopologyspread.Name, Weight: 1},
{Name: tainttoleration.Name, Weight: 1},
},
},
}
}
每个ScorePlugin 计算的score 限定在[1,100]，可以看到大部分插件默认为1，个别插件nodepreferavoidpods 权重为10000（基本就是一票否决了）。
调用各个ScorePlugin，过滤非法score，加入权重计算。
// k8s.io/kubernetes/pkg/scheduler/framework/v1alpha1/framework.go
func (f *framework) RunScorePlugins(ctx context.Context, state *CycleState, pod *v1.Pod, nodes []*v1.Node) (ps PluginToNodeScores, status *Status) {
pluginToNodeScores := make(PluginToNodeScores, len(f.scorePlugins))
for _, pl := range f.scorePlugins {
pluginToNodeScores[pl.Name()] = make(NodeScoreList, len(nodes))
}
// Run Score method for each node in parallel.
workqueue.ParallelizeUntil(ctx, 16, len(nodes), func(index int) {
for _, pl := range f.scorePlugins {
nodeName := nodes[index].Name
s, status := f.runScorePlugin(ctx, pl, state, pod, nodeName)
pluginToNodeScores[pl.Name()][index] = NodeScore{
Name: nodeName,
Score: int64(s),
}
}
})
// Run NormalizeScore method for each ScorePlugin in parallel.
workqueue.ParallelizeUntil(ctx, 16, len(f.scorePlugins), func(index int) {
pl := f.scorePlugins[index]
nodeScoreList := pluginToNodeScores[pl.Name()]
status := f.runScoreExtension(ctx, pl, state, pod, nodeScoreList)
})
// Apply score defaultWeights for each ScorePlugin in parallel.
workqueue.ParallelizeUntil(ctx, 16, len(f.scorePlugins), func(index int) {
pl := f.scorePlugins[index]
weight := f.pluginNameToWeightMap[pl.Name()]
nodeScoreList := pluginToNodeScores[pl.Name()]
for i, nodeScore := range nodeScoreList {
// return error if score plugin returns invalid score.
if nodeScore.Score > int64(MaxNodeScore) || nodeScore.Score < int64(MinNodeScore) {
return
}
nodeScoreList[i].Score = nodeScore.Score * int64(weight)
}
})
return pluginToNodeScores, nil
}
ScorePlugin 实现
主要是 k8s.io/kubernetes/pkg/scheduler/framework/plugins/noderesources noderesources
noderesources.BalancedAllocationName 和 noderesources.LeastAllocatedName 都作为score plugin 被使用了。具体实现上，BalancedAllocation 和 LeastAllocated 负责最外层接口实现和 具体score 逻辑，resourceAllocationScorer 负责通用pod node 数据获取 及通用score 逻辑。执行链路为：子类.Score ==> resourceAllocationScorer.score ==> 子类.核心score 逻辑
type resourceAllocationScorer struct {
Name string
scorer func(requested, allocable resourceToValueMap, includeVolumes bool, requestedVolumes int, allocatableVolumes int) int64
resourceToWeightMap resourceToWeightMap
}
type BalancedAllocation struct {
handle framework.FrameworkHandle
resourceAllocationScorer
}
type LeastAllocated struct {
handle framework.FrameworkHandle
resourceAllocationScorer
}
BalancedAllocationName
// k8s.io/kubernetes/pkg/scheduler/framework/plugins/noderesources/resource_allocation.go
type resourceAllocationScorer struct {
Name string
scorer func(requested, allocable resourceToValueMap, includeVolumes bool, requestedVolumes int, allocatableVolumes int) int64
resourceToWeightMap resourceToWeightMap
}
func (r *resourceAllocationScorer) score(
pod *v1.Pod,
nodeInfo *schedulernodeinfo.NodeInfo) (int64, *framework.Status) {
node := nodeInfo.Node()
requested := make(resourceToValueMap, len(r.resourceToWeightMap))
allocatable := make(resourceToValueMap, len(r.resourceToWeightMap))
for resource := range r.resourceToWeightMap {
allocatable[resource], requested[resource] = calculateResourceAllocatableRequest(nodeInfo, pod, resource)
}
var score int64
// Check if the pod has volumes and this could be added to scorer function for balanced resource allocation.
if len(pod.Spec.Volumes) >= 0 && utilfeature.DefaultFeatureGate.Enabled(features.BalanceAttachedNodeVolumes) && nodeInfo.TransientInfo != nil {
score = r.scorer(requested, allocatable, true, nodeInfo.TransientInfo.TransNodeInfo.RequestedVolumes, nodeInfo.TransientInfo.TransNodeInfo.AllocatableVolumesCount)
} else {
score = r.scorer(requested, allocatable, false, 0, 0)
}
return score, nil
}
// k8s.io/kubernetes/pkg/scheduler/framework/plugins/noderesources/balanced_allocation.go
func balancedResourceScorer(requested, allocable resourceToValueMap, includeVolumes bool, requestedVolumes int, allocatableVolumes int) int64 {
cpuFraction := fractionOfCapacity(requested[v1.ResourceCPU], allocable[v1.ResourceCPU])
memoryFraction := fractionOfCapacity(requested[v1.ResourceMemory], allocable[v1.ResourceMemory])
// This to find a node which has most balanced CPU, memory and volume usage.
if cpuFraction >= 1 || memoryFraction >= 1 {
// if requested >= capacity, the corresponding host should never be preferred.
return 0
}
if includeVolumes && utilfeature.DefaultFeatureGate.Enabled(features.BalanceAttachedNodeVolumes) && allocatableVolumes > 0 {
...
return int64((1 - variance) * float64(framework.MaxNodeScore))
}
// Upper and lower boundary of difference between cpuFraction and memoryFraction are -1 and 1
// respectively. Multiplying the absolute value of the difference by 10 scales the value to
// 0-10 with 0 representing well balanced allocation and 10 poorly balanced. Subtracting it from
// 10 leads to the score which also scales from 0 to 10 while 10 representing well balanced.
diff := math.Abs(cpuFraction - memoryFraction)
return int64((1 - diff) * float64(framework.MaxNodeScore))
}
BalancedAllocationName 首先会计算 cpu 、men 以及可能volume reqeust 占有node allocable 的比例Fraction
diff := math.Abs(cpuFraction - memoryFraction)
return int64((1 - diff) * float64(framework.MaxNodeScore))
按照上市逻辑来说
| cpuFraction | memoryFraction | score | |
|---|---|---|---|
| node1 | 10% | 20% | (1-0.1)*100=90 |
| node2 | 20% | 10% | (1-0.1)*100=90 |
| node2 | 20% | 40% | (1-0.2)*100=80 |
| node2 | 20% | 50% | (1-0.3)*100=70 |
从上面可以看到 BalancedAllocationName 是从单个node 的角度出发的，尽量让一个node 上已经分配的 cpu 和mem 百分比是平衡的。减少出现比如 cpu 很富余但内存已经不够的情况。BalancedAllocation is a score plugin that calculates the difference between the cpu and memory fraction of capacity, and prioritizes the host based on how close the two metrics are to each other.
LeastAllocatedName
cpu 和内存分别算一个分数，加权求和。node剩余资源越富余，score 越高。也就是负载越低的node 分数高。
// k8s.io/kubernetes/pkg/scheduler/framework/plugins/noderesources/least_allocated.go
var defaultRequestedRatioResources = resourceToWeightMap{v1.ResourceMemory: 1, v1.ResourceCPU: 1}
func leastResourceScorer(requested, allocable resourceToValueMap, includeVolumes bool, requestedVolumes int, allocatableVolumes int) int64 {
var nodeScore, weightSum int64
for resource, weight := range defaultRequestedRatioResources {
resourceScore := leastRequestedScore(requested[resource], allocable[resource])
nodeScore += resourceScore * weight
weightSum += weight
}
return nodeScore / weightSum
}
func leastRequestedScore(requested, capacity int64) int64 {
if capacity == 0 {
return 0
}
if requested > capacity {
return 0
}
return ((capacity - requested) * int64(framework.MaxNodeScore)) / capacity
}
如何观察节点计算的score
scheduler 在score结束会记录日志
// k8s.io/kubernetes/pkg/scheduler/core/generic_scheduler.go
func (g *genericScheduler) prioritizeNodes(
ctx context.Context,
prof *profile.Profile,
state *framework.CycleState,
pod *v1.Pod,
nodes []*v1.Node,
) (framework.NodeScoreList, error) {
...
if klog.V(10) {
for i := range result {
klog.Infof("Host %s => Score %d", result[i].Name, result[i].Score)
}
}
}
假设k8s 集群存在 192.168.60.237 和 192.168.60.238 两个slave 节点，在192.168.60.89 运行一个scheduler extender，当把kube-scheduler 启动参数的日志level 调整为10 时（值越小估计级别越高，输出日志越少），apply 一个 nginx example， journalctl -u kube-scheduler | grep -i score可以看到代score 关键字的日志（日志部分去掉了前缀）
nginx-deployment-85ff79dd56-972cg -> 192.168.60.238: BalancedResourceAllocation, map of allocatable resources map[cpu:47800 memory:66054406144], map of requested resources map[cpu:7850 memory:6786383872] ,score 93,
nginx-deployment-85ff79dd56-972cg -> 192.168.60.238: LeastResourceAllocation, map of allocatable resources map[cpu:47800 memory:66054406144], map of requested resources map[cpu:7850 memory:6786383872] ,score 86,
nginx-deployment-85ff79dd56-972cg -> 192.168.60.237: BalancedResourceAllocation, map of allocatable resources map[cpu:47800 memory:66054406144], map of requested resources map[cpu:6950 memory:7627341824] ,score 97,
nginx-deployment-85ff79dd56-972cg -> 192.168.60.237: LeastResourceAllocation, map of allocatable resources map[cpu:47800 memory:66054406144], map of requested resources map[cpu:6950 memory:7627341824] ,score 86,
nginx-deployment-85ff79dd56-972cg -> 192.168.60.238: SelectorSpreadPriority, Score: (100)
nginx-deployment-85ff79dd56-972cg -> 192.168.60.237: SelectorSpreadPriority, Score: (100)
nginx-deployment-85ff79dd56-972cg_default -> 192.168.60.238: http://192.168.60.89:8889/, Score: (2)
nginx-deployment-85ff79dd56-972cg_default -> 192.168.60.237: http://192.168.60.89:8889/, Score: (5)
Host 192.168.60.238 => Score 1000404
Host 192.168.60.237 => Score 1000438
metric ：可以看 http://$scheduler_ip:10251/metrics 的metric。 但没有直接找到相关的 metric ，或者信息不充分。