| /* |
| Copyright 2016 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 ( |
| "math" |
| |
| utilfeature "k8s.io/apiserver/pkg/util/feature" |
| "k8s.io/kubernetes/pkg/features" |
| schedulerapi "k8s.io/kubernetes/pkg/scheduler/api" |
| schedulercache "k8s.io/kubernetes/pkg/scheduler/cache" |
| ) |
| |
| var ( |
| balancedResourcePriority = &ResourceAllocationPriority{"BalancedResourceAllocation", 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 - variance(cpuFraction,memoryFraction,volumeFraction)*10. The algorithm is partly inspired by: |
| // "Wei Huang et al. An Energy Efficient Virtual Machine Placement Algorithm with Balanced |
| // Resource Utilization" |
| BalancedResourceAllocationMap = balancedResourcePriority.PriorityMap |
| ) |
| |
| func balancedResourceScorer(requested, allocable *schedulercache.Resource, includeVolumes bool, requestedVolumes int, allocatableVolumes int) int64 { |
| cpuFraction := fractionOfCapacity(requested.MilliCPU, allocable.MilliCPU) |
| memoryFraction := fractionOfCapacity(requested.Memory, allocable.Memory) |
| // This to find a node which has most balanced CPU, memory and volume usage. |
| if includeVolumes && utilfeature.DefaultFeatureGate.Enabled(features.BalanceAttachedNodeVolumes) && allocatableVolumes > 0 { |
| volumeFraction := float64(requestedVolumes) / float64(allocatableVolumes) |
| if cpuFraction >= 1 || memoryFraction >= 1 || volumeFraction >= 1 { |
| // if requested >= capacity, the corresponding host should never be preferred. |
| return 0 |
| } |
| // Compute variance for all the three fractions. |
| mean := (cpuFraction + memoryFraction + volumeFraction) / float64(3) |
| variance := float64((((cpuFraction - mean) * (cpuFraction - mean)) + ((memoryFraction - mean) * (memoryFraction - mean)) + ((volumeFraction - mean) * (volumeFraction - mean))) / float64(3)) |
| // Since the variance is between positive fractions, it will be positive fraction. 1-variance lets the |
| // score to be higher for node which has least variance and multiplying it with 10 provides the scaling |
| // factor needed. |
| return int64((1 - variance) * float64(schedulerapi.MaxPriority)) |
| } |
| |
| if cpuFraction >= 1 || memoryFraction >= 1 { |
| // if requested >= capacity, the corresponding host should never be preferred. |
| return 0 |
| } |
| // 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(schedulerapi.MaxPriority)) |
| } |
| |
| func fractionOfCapacity(requested, capacity int64) float64 { |
| if capacity == 0 { |
| return 1 |
| } |
| return float64(requested) / float64(capacity) |
| } |