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Merge pull request #55442 from anfernee/priority_resource

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Automatic merge from submit-queue (batch tested with PRs 57257, 55442). If you want to cherry-pick this change to another branch, please follow the instructions <a href="https://github.com/kubernetes/community/blob/master/contributors/devel/cherry-picks.md">here</a>.

Merge 3 resource allocation priority functions

**What this PR does / why we need it**: those 3 priority functions are closed related, and share a lot of the same logic, put them together.

**Release note**:
```release-note
None
```
This commit is contained in:
Kubernetes Submit Queue 2017-12-20 23:56:49 -08:00 committed by GitHub
commit 754bb1350f
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6 changed files with 143 additions and 179 deletions
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1
plugin/pkg/scheduler/algorithm/priorities/BUILD
View File

@ -19,6 +19,7 @@ go_library(
"node_label.go",
"node_prefer_avoid_pods.go",
"reduce.go",
"resource_allocation.go",
"resource_limits.go",
"selector_spreading.go",
"taint_toleration.go",

95
plugin/pkg/scheduler/algorithm/priorities/balanced_resource_allocation.go
View File

@ -17,76 +17,40 @@ limitations under the License.
package priorities
import (
"fmt"
"math"
"k8s.io/api/core/v1"
priorityutil "k8s.io/kubernetes/plugin/pkg/scheduler/algorithm/priorities/util"
schedulerapi "k8s.io/kubernetes/plugin/pkg/scheduler/api"
"k8s.io/kubernetes/plugin/pkg/scheduler/schedulercache"
"github.com/golang/glog"
)
// This is a reasonable size range of all container images. 90%ile of images on dockerhub drops into this range.
const (
mb int64 = 1024 * 1024
minImgSize int64 = 23 * mb
maxImgSize int64 = 1000 * mb
var (
balanceResourcePriority = &ResourceAllocationPriority{"BalanceResourceAllocation", balancedResourceScorer}
// BalancedResourceAllocationMap favors nodes with balanced resource usage rate.
// BalancedResourceAllocationMap should **NOT** be used alone, and **MUST** be used together
// with LeastRequestedPriority. It 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.
// Detail: score = 10 - abs(cpuFraction-memoryFraction)*10. The algorithm is partly inspired by:
// "Wei Huang et al. An Energy Efficient Virtual Machine Placement Algorithm with Balanced
// Resource Utilization"
BalancedResourceAllocationMap = balanceResourcePriority.PriorityMap
)
// Also used in most/least_requested nad metadata.
// TODO: despaghettify it
func getNonZeroRequests(pod *v1.Pod) *schedulercache.Resource {
result := &schedulercache.Resource{}
for i := range pod.Spec.Containers {
container := &pod.Spec.Containers[i]
cpu, memory := priorityutil.GetNonzeroRequests(&container.Resources.Requests)
result.MilliCPU += cpu
result.Memory += memory
}
return result
}
func balancedResourceScorer(requested, allocable *schedulercache.Resource) int64 {
cpuFraction := fractionOfCapacity(requested.MilliCPU, allocable.MilliCPU)
memoryFraction := fractionOfCapacity(requested.Memory, allocable.Memory)
func calculateBalancedResourceAllocation(pod *v1.Pod, podRequests *schedulercache.Resource, nodeInfo *schedulercache.NodeInfo) (schedulerapi.HostPriority, error) {
node := nodeInfo.Node()
if node == nil {
return schedulerapi.HostPriority{}, fmt.Errorf("node not found")
}
allocatableResources := nodeInfo.AllocatableResource()
totalResources := *podRequests
totalResources.MilliCPU += nodeInfo.NonZeroRequest().MilliCPU
totalResources.Memory += nodeInfo.NonZeroRequest().Memory
cpuFraction := fractionOfCapacity(totalResources.MilliCPU, allocatableResources.MilliCPU)
memoryFraction := fractionOfCapacity(totalResources.Memory, allocatableResources.Memory)
score := int(0)
if cpuFraction >= 1 || memoryFraction >= 1 {
// if requested >= capacity, the corresponding host should never be preferred.
score = 0
} else {
// Upper and lower boundary of difference between cpuFraction and memoryFraction are -1 and 1
// respectively. Multilying 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)
score = int((1 - diff) * float64(schedulerapi.MaxPriority))
}
if glog.V(10) {
glog.Infof(
"%v -> %v: Balanced Resource Allocation, capacity %d millicores %d memory bytes, total request %d millicores %d memory bytes, score %d",
pod.Name, node.Name,
allocatableResources.MilliCPU, allocatableResources.Memory,
totalResources.MilliCPU, totalResources.Memory,
score,
)
return 0
}
return schedulerapi.HostPriority{
Host: node.Name,
Score: score,
}, nil
// Upper and lower boundary of difference between cpuFraction and memoryFraction are -1 and 1
// respectively. Multilying 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(schedulerapi.MaxPriority))
}
func fractionOfCapacity(requested, capacity int64) float64 {
@ -95,20 +59,3 @@ func fractionOfCapacity(requested, capacity int64) float64 {
}
return float64(requested) / float64(capacity)
}
// BalancedResourceAllocationMap favors nodes with balanced resource usage rate.
// BalancedResourceAllocationMap should **NOT** be used alone, and **MUST** be used together with LeastRequestedPriority.
// It 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.
// Detail: score = 10 - abs(cpuFraction-memoryFraction)*10. The algorithm is partly inspired by:
// "Wei Huang et al. An Energy Efficient Virtual Machine Placement Algorithm with Balanced Resource Utilization"
func BalancedResourceAllocationMap(pod *v1.Pod, meta interface{}, nodeInfo *schedulercache.NodeInfo) (schedulerapi.HostPriority, error) {
var nonZeroRequest *schedulercache.Resource
if priorityMeta, ok := meta.(*priorityMetadata); ok {
nonZeroRequest = priorityMeta.nonZeroRequest
} else {
// We couldn't parse metadatat - fallback to computing it.
nonZeroRequest = getNonZeroRequests(pod)
}
return calculateBalancedResourceAllocation(pod, nonZeroRequest, nodeInfo)
}

7
plugin/pkg/scheduler/algorithm/priorities/image_locality.go
View File

@ -24,6 +24,13 @@ import (
"k8s.io/kubernetes/plugin/pkg/scheduler/schedulercache"
)
// This is a reasonable size range of all container images. 90%ile of images on dockerhub drops into this range.
const (
mb int64 = 1024 * 1024
minImgSize int64 = 23 * mb
maxImgSize int64 = 1000 * mb
)
// ImageLocalityPriorityMap is a priority function that favors nodes that already have requested pod container's images.
// It will detect whether the requested images are present on a node, and then calculate a score ranging from 0 to 10
// based on the total size of those images.

70
plugin/pkg/scheduler/algorithm/priorities/least_requested.go
View File

@ -17,73 +17,37 @@ limitations under the License.
package priorities
import (
"fmt"
"k8s.io/api/core/v1"
schedulerapi "k8s.io/kubernetes/plugin/pkg/scheduler/api"
"k8s.io/kubernetes/plugin/pkg/scheduler/schedulercache"
"github.com/golang/glog"
)
// LeastRequestedPriority is a priority function that favors nodes with fewer requested resources.
// It calculates the percentage of memory and CPU requested by pods scheduled on the node, and prioritizes
// based on the minimum of the average of the fraction of requested to capacity.
// Details: cpu((capacity - sum(requested)) * 10 / capacity) + memory((capacity - sum(requested)) * 10 / capacity) / 2
func LeastRequestedPriorityMap(pod *v1.Pod, meta interface{}, nodeInfo *schedulercache.NodeInfo) (schedulerapi.HostPriority, error) {
var nonZeroRequest *schedulercache.Resource
if priorityMeta, ok := meta.(*priorityMetadata); ok {
nonZeroRequest = priorityMeta.nonZeroRequest
} else {
// We couldn't parse metadata - fallback to computing it.
nonZeroRequest = getNonZeroRequests(pod)
}
return calculateUnusedPriority(pod, nonZeroRequest, nodeInfo)
var (
leastResourcePriority = &ResourceAllocationPriority{"LeastResourceAllocation", leastResourceScorer}
// LeastRequestedPriority is a priority function that favors nodes with fewer requested resources.
// It calculates the percentage of memory and CPU requested by pods scheduled on the node, and
// prioritizes based on the minimum of the average of the fraction of requested to capacity.
//
// Details:
// cpu((capacity-sum(requested))*10/capacity) + memory((capacity-sum(requested))*10/capacity)/2
LeastRequestedPriorityMap = leastResourcePriority.PriorityMap
)
func leastResourceScorer(requested, allocable *schedulercache.Resource) int64 {
return (leastRequestedScore(requested.MilliCPU, allocable.MilliCPU) +
leastRequestedScore(requested.Memory, allocable.Memory)) / 2
}
// The unused capacity is calculated on a scale of 0-10
// 0 being the lowest priority and 10 being the highest.
// The more unused resources the higher the score is.
func calculateUnusedScore(requested int64, capacity int64, node string) int64 {
func leastRequestedScore(requested, capacity int64) int64 {
if capacity == 0 {
return 0
}
if requested > capacity {
glog.V(10).Infof("Combined requested resources %d from existing pods exceeds capacity %d on node %s",
requested, capacity, node)
return 0
}
return ((capacity - requested) * int64(schedulerapi.MaxPriority)) / capacity
}
// Calculates host priority based on the amount of unused resources.
// 'node' has information about the resources on the node.
// 'pods' is a list of pods currently scheduled on the node.
func calculateUnusedPriority(pod *v1.Pod, podRequests *schedulercache.Resource, nodeInfo *schedulercache.NodeInfo) (schedulerapi.HostPriority, error) {
node := nodeInfo.Node()
if node == nil {
return schedulerapi.HostPriority{}, fmt.Errorf("node not found")
}
allocatableResources := nodeInfo.AllocatableResource()
totalResources := *podRequests
totalResources.MilliCPU += nodeInfo.NonZeroRequest().MilliCPU
totalResources.Memory += nodeInfo.NonZeroRequest().Memory
cpuScore := calculateUnusedScore(totalResources.MilliCPU, allocatableResources.MilliCPU, node.Name)
memoryScore := calculateUnusedScore(totalResources.Memory, allocatableResources.Memory, node.Name)
if glog.V(10) {
glog.Infof(
"%v -> %v: Least Requested Priority, capacity %d millicores %d memory bytes, total request %d millicores %d memory bytes, score %d CPU %d memory",
pod.Name, node.Name,
allocatableResources.MilliCPU, allocatableResources.Memory,
totalResources.MilliCPU, totalResources.Memory,
cpuScore, memoryScore,
)
}
return schedulerapi.HostPriority{
Host: node.Name,
Score: int((cpuScore + memoryScore) / 2),
}, nil
}

67
plugin/pkg/scheduler/algorithm/priorities/most_requested.go
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@ -17,28 +17,23 @@ limitations under the License.
package priorities
import (
"fmt"
"k8s.io/api/core/v1"
schedulerapi "k8s.io/kubernetes/plugin/pkg/scheduler/api"
"k8s.io/kubernetes/plugin/pkg/scheduler/schedulercache"
"github.com/golang/glog"
)
// MostRequestedPriority is a priority function that favors nodes with most requested resources.
// It calculates the percentage of memory and CPU requested by pods scheduled on the node, and prioritizes
// based on the maximum of the average of the fraction of requested to capacity.
// Details: (cpu(10 * sum(requested) / capacity) + memory(10 * sum(requested) / capacity)) / 2
func MostRequestedPriorityMap(pod *v1.Pod, meta interface{}, nodeInfo *schedulercache.NodeInfo) (schedulerapi.HostPriority, error) {
var nonZeroRequest *schedulercache.Resource
if priorityMeta, ok := meta.(*priorityMetadata); ok {
nonZeroRequest = priorityMeta.nonZeroRequest
} else {
// We couldn't parse metadatat - fallback to computing it.
nonZeroRequest = getNonZeroRequests(pod)
}
return calculateUsedPriority(pod, nonZeroRequest, nodeInfo)
var (
mostResourcePriority = &ResourceAllocationPriority{"MostResourceAllocation", mostResourceScorer}
// MostRequestedPriority is a priority function that favors nodes with most requested resources.
// It calculates the percentage of memory and CPU requested by pods scheduled on the node, and prioritizes
// based on the maximum of the average of the fraction of requested to capacity.
// Details: (cpu(10 * sum(requested) / capacity) + memory(10 * sum(requested) / capacity)) / 2
MostRequestedPriorityMap = mostResourcePriority.PriorityMap
)
func mostResourceScorer(requested, allocable *schedulercache.Resource) int64 {
return (mostRequestedScore(requested.MilliCPU, allocable.MilliCPU) +
mostRequestedScore(requested.Memory, allocable.Memory)) / 2
}
// The used capacity is calculated on a scale of 0-10
@ -48,45 +43,13 @@ func MostRequestedPriorityMap(pod *v1.Pod, meta interface{}, nodeInfo *scheduler
// (10 - calculateUnusedScore). The main difference is in rounding. It was added to
// keep the final formula clean and not to modify the widely used (by users
// in their default scheduling policies) calculateUSedScore.
func calculateUsedScore(requested int64, capacity int64, node string) int64 {
func mostRequestedScore(requested, capacity int64) int64 {
if capacity == 0 {
return 0
}
if requested > capacity {
glog.V(10).Infof("Combined requested resources %d from existing pods exceeds capacity %d on node %s",
requested, capacity, node)
return 0
}
return (requested * schedulerapi.MaxPriority) / capacity
}
// Calculate the resource used on a node. 'node' has information about the resources on the node.
// 'pods' is a list of pods currently scheduled on the node.
func calculateUsedPriority(pod *v1.Pod, podRequests *schedulercache.Resource, nodeInfo *schedulercache.NodeInfo) (schedulerapi.HostPriority, error) {
node := nodeInfo.Node()
if node == nil {
return schedulerapi.HostPriority{}, fmt.Errorf("node not found")
}
allocatableResources := nodeInfo.AllocatableResource()
totalResources := *podRequests
totalResources.MilliCPU += nodeInfo.NonZeroRequest().MilliCPU
totalResources.Memory += nodeInfo.NonZeroRequest().Memory
cpuScore := calculateUsedScore(totalResources.MilliCPU, allocatableResources.MilliCPU, node.Name)
memoryScore := calculateUsedScore(totalResources.Memory, allocatableResources.Memory, node.Name)
if glog.V(10) {
glog.Infof(
"%v -> %v: Most Requested Priority, capacity %d millicores %d memory bytes, total request %d millicores %d memory bytes, score %d CPU %d memory",
pod.Name, node.Name,
allocatableResources.MilliCPU, allocatableResources.Memory,
totalResources.MilliCPU, totalResources.Memory,
cpuScore, memoryScore,
)
}
return schedulerapi.HostPriority{
Host: node.Name,
Score: int((cpuScore + memoryScore) / 2),
}, nil
}

82
plugin/pkg/scheduler/algorithm/priorities/resource_allocation.go Normal file
View File

@ -0,0 +1,82 @@
/*
Copyright 2017 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 priorities
import (
"fmt"
"github.com/golang/glog"
"k8s.io/api/core/v1"
priorityutil "k8s.io/kubernetes/plugin/pkg/scheduler/algorithm/priorities/util"
schedulerapi "k8s.io/kubernetes/plugin/pkg/scheduler/api"
"k8s.io/kubernetes/plugin/pkg/scheduler/schedulercache"
)
type ResourceAllocationPriority struct {
Name string
scorer func(requested, allocable *schedulercache.Resource) int64
}
func (r *ResourceAllocationPriority) PriorityMap(
pod *v1.Pod,
meta interface{},
nodeInfo *schedulercache.NodeInfo) (schedulerapi.HostPriority, error) {
node := nodeInfo.Node()
if node == nil {
return schedulerapi.HostPriority{}, fmt.Errorf("node not found")
}
allocatable := nodeInfo.AllocatableResource()
var requested schedulercache.Resource
if priorityMeta, ok := meta.(*priorityMetadata); ok {
requested = *priorityMeta.nonZeroRequest
} else {
// We couldn't parse metadatat - fallback to computing it.
requested = *getNonZeroRequests(pod)
}
requested.MilliCPU += nodeInfo.NonZeroRequest().MilliCPU
requested.Memory += nodeInfo.NonZeroRequest().Memory
score := r.scorer(&requested, &allocatable)
if glog.V(10) {
glog.Infof(
"%v -> %v: %v, capacity %d millicores %d memory bytes, total request %d millicores %d memory bytes, score %d",
pod.Name, node.Name, r.Name,
allocatable.MilliCPU, allocatable.Memory,
requested.MilliCPU+allocatable.MilliCPU, requested.Memory+allocatable.Memory,
score,
)
}
return schedulerapi.HostPriority{
Host: node.Name,
Score: int(score),
}, nil
}
func getNonZeroRequests(pod *v1.Pod) *schedulercache.Resource {
result := &schedulercache.Resource{}
for i := range pod.Spec.Containers {
container := &pod.Spec.Containers[i]
cpu, memory := priorityutil.GetNonzeroRequests(&container.Resources.Requests)
result.MilliCPU += cpu
result.Memory += memory
}
return result
}