mirror of
https://github.com/optim-enterprises-bv/kubernetes.git
synced 2025-11-04 12:18:16 +00:00
248 lines
9.4 KiB
Go
248 lines
9.4 KiB
Go
/*
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Copyright 2014 The Kubernetes Authors All rights reserved.
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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 priorities
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import (
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"math"
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"github.com/golang/glog"
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"k8s.io/kubernetes/pkg/api"
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"k8s.io/kubernetes/pkg/api/resource"
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"k8s.io/kubernetes/pkg/labels"
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"k8s.io/kubernetes/plugin/pkg/scheduler/algorithm"
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"k8s.io/kubernetes/plugin/pkg/scheduler/algorithm/predicates"
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)
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// the unused capacity is calculated on a scale of 0-10
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// 0 being the lowest priority and 10 being the highest
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func calculateScore(requested int64, capacity int64, node string) int {
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if capacity == 0 {
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return 0
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}
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if requested > capacity {
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glog.Infof("Combined requested resources %d from existing pods exceeds capacity %d on node %s",
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requested, capacity, node)
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return 0
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}
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return int(((capacity - requested) * 10) / capacity)
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}
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// For each of these resources, a pod that doesn't request the resource explicitly
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// will be treated as having requested the amount indicated below, for the purpose
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// of computing priority only. This ensures that when scheduling zero-limit pods, such
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// pods will not all be scheduled to the machine with the smallest in-use limit,
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// and that when scheduling regular pods, such pods will not see zero-limit pods as
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// consuming no resources whatsoever. We chose these values to be similar to the
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// resources that we give to cluster addon pods (#10653). But they are pretty arbitrary.
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const defaultMilliCpuLimit int64 = 100 // 0.1 core
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const defaultMemoryLimit int64 = 200 * 1024 * 1024 // 200 MB
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// TODO: Consider setting default as a fixed fraction of machine capacity (take "capacity api.ResourceList"
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// as an additional argument here) rather than using constants
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func getNonzeroLimits(limits *api.ResourceList) (int64, int64) {
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var out_millicpu, out_memory int64
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// Override if un-set, but not if explicitly set to zero
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if (*limits.Cpu() == resource.Quantity{}) {
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out_millicpu = defaultMilliCpuLimit
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} else {
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out_millicpu = limits.Cpu().MilliValue()
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}
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// Override if un-set, but not if explicitly set to zero
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if (*limits.Memory() == resource.Quantity{}) {
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out_memory = defaultMemoryLimit
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} else {
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out_memory = limits.Memory().Value()
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}
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return out_millicpu, out_memory
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}
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// Calculate the resource occupancy on a node. 'node' has information about the resources on the node.
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// 'pods' is a list of pods currently scheduled on the node.
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func calculateResourceOccupancy(pod *api.Pod, node api.Node, pods []*api.Pod) algorithm.HostPriority {
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totalMilliCPU := int64(0)
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totalMemory := int64(0)
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capacityMilliCPU := node.Status.Capacity.Cpu().MilliValue()
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capacityMemory := node.Status.Capacity.Memory().Value()
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for _, existingPod := range pods {
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for _, container := range existingPod.Spec.Containers {
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cpu, memory := getNonzeroLimits(&container.Resources.Limits)
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totalMilliCPU += cpu
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totalMemory += memory
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}
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}
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// Add the resources requested by the current pod being scheduled.
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// This also helps differentiate between differently sized, but empty, minions.
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for _, container := range pod.Spec.Containers {
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cpu, memory := getNonzeroLimits(&container.Resources.Limits)
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totalMilliCPU += cpu
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totalMemory += memory
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}
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cpuScore := calculateScore(totalMilliCPU, capacityMilliCPU, node.Name)
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memoryScore := calculateScore(totalMemory, capacityMemory, node.Name)
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glog.V(10).Infof(
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"%v -> %v: Least Requested Priority, Absolute/Requested: (%d, %d) / (%d, %d) Score: (%d, %d)",
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pod.Name, node.Name,
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totalMilliCPU, totalMemory,
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capacityMilliCPU, capacityMemory,
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cpuScore, memoryScore,
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)
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return algorithm.HostPriority{
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Host: node.Name,
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Score: int((cpuScore + memoryScore) / 2),
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}
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}
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// LeastRequestedPriority is a priority function that favors nodes with fewer requested resources.
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// It calculates the percentage of memory and CPU requested by pods scheduled on the node, and prioritizes
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// based on the minimum of the average of the fraction of requested to capacity.
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// Details: cpu((capacity - sum(requested)) * 10 / capacity) + memory((capacity - sum(requested)) * 10 / capacity) / 2
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func LeastRequestedPriority(pod *api.Pod, podLister algorithm.PodLister, minionLister algorithm.MinionLister) (algorithm.HostPriorityList, error) {
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nodes, err := minionLister.List()
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if err != nil {
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return algorithm.HostPriorityList{}, err
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}
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podsToMachines, err := predicates.MapPodsToMachines(podLister)
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list := algorithm.HostPriorityList{}
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for _, node := range nodes.Items {
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list = append(list, calculateResourceOccupancy(pod, node, podsToMachines[node.Name]))
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}
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return list, nil
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}
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type NodeLabelPrioritizer struct {
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label string
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presence bool
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}
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func NewNodeLabelPriority(label string, presence bool) algorithm.PriorityFunction {
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labelPrioritizer := &NodeLabelPrioritizer{
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label: label,
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presence: presence,
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}
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return labelPrioritizer.CalculateNodeLabelPriority
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}
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// CalculateNodeLabelPriority checks whether a particular label exists on a minion or not, regardless of its value.
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// If presence is true, prioritizes minions that have the specified label, regardless of value.
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// If presence is false, prioritizes minions that do not have the specified label.
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func (n *NodeLabelPrioritizer) CalculateNodeLabelPriority(pod *api.Pod, podLister algorithm.PodLister, minionLister algorithm.MinionLister) (algorithm.HostPriorityList, error) {
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var score int
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minions, err := minionLister.List()
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if err != nil {
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return nil, err
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}
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labeledMinions := map[string]bool{}
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for _, minion := range minions.Items {
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exists := labels.Set(minion.Labels).Has(n.label)
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labeledMinions[minion.Name] = (exists && n.presence) || (!exists && !n.presence)
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}
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result := []algorithm.HostPriority{}
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//score int - scale of 0-10
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// 0 being the lowest priority and 10 being the highest
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for minionName, success := range labeledMinions {
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if success {
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score = 10
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} else {
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score = 0
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}
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result = append(result, algorithm.HostPriority{Host: minionName, Score: score})
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}
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return result, nil
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}
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// BalancedResourceAllocation favors nodes with balanced resource usage rate.
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// BalancedResourceAllocation should **NOT** be used alone, and **MUST** be used together with LeastRequestedPriority.
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// It calculates the difference between the cpu and memory fracion of capacity, and prioritizes the host based on how
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// close the two metrics are to each other.
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// Detail: score = 10 - abs(cpuFraction-memoryFraction)*10. The algorithm is partly inspired by:
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// "Wei Huang et al. An Energy Efficient Virtual Machine Placement Algorithm with Balanced Resource Utilization"
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func BalancedResourceAllocation(pod *api.Pod, podLister algorithm.PodLister, minionLister algorithm.MinionLister) (algorithm.HostPriorityList, error) {
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nodes, err := minionLister.List()
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if err != nil {
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return algorithm.HostPriorityList{}, err
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}
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podsToMachines, err := predicates.MapPodsToMachines(podLister)
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list := algorithm.HostPriorityList{}
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for _, node := range nodes.Items {
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list = append(list, calculateBalancedResourceAllocation(pod, node, podsToMachines[node.Name]))
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}
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return list, nil
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}
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func calculateBalancedResourceAllocation(pod *api.Pod, node api.Node, pods []*api.Pod) algorithm.HostPriority {
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totalMilliCPU := int64(0)
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totalMemory := int64(0)
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score := int(0)
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for _, existingPod := range pods {
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for _, container := range existingPod.Spec.Containers {
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cpu, memory := getNonzeroLimits(&container.Resources.Limits)
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totalMilliCPU += cpu
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totalMemory += memory
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}
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}
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// Add the resources requested by the current pod being scheduled.
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// This also helps differentiate between differently sized, but empty, minions.
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for _, container := range pod.Spec.Containers {
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cpu, memory := getNonzeroLimits(&container.Resources.Limits)
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totalMilliCPU += cpu
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totalMemory += memory
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}
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capacityMilliCPU := node.Status.Capacity.Cpu().MilliValue()
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capacityMemory := node.Status.Capacity.Memory().Value()
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cpuFraction := fractionOfCapacity(totalMilliCPU, capacityMilliCPU)
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memoryFraction := fractionOfCapacity(totalMemory, capacityMemory)
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if cpuFraction >= 1 || memoryFraction >= 1 {
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// if requested >= capacity, the corresponding host should never be preferrred.
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score = 0
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} else {
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// Upper and lower boundary of difference between cpuFraction and memoryFraction are -1 and 1
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// respectively. Multilying the absolute value of the difference by 10 scales the value to
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// 0-10 with 0 representing well balanced allocation and 10 poorly balanced. Subtracting it from
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// 10 leads to the score which also scales from 0 to 10 while 10 representing well balanced.
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diff := math.Abs(cpuFraction - memoryFraction)
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score = int(10 - diff*10)
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}
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glog.V(10).Infof(
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"%v -> %v: Balanced Resource Allocation, Absolute/Requested: (%d, %d) / (%d, %d) Score: (%d)",
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pod.Name, node.Name,
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totalMilliCPU, totalMemory,
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capacityMilliCPU, capacityMemory,
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score,
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)
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return algorithm.HostPriority{
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Host: node.Name,
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Score: score,
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}
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}
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func fractionOfCapacity(requested, capacity int64) float64 {
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if capacity == 0 {
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return 1
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}
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return float64(requested) / float64(capacity)
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}
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