Revert "[hpa] Parameterize tolerance, downscale, and upscale into HPAController, and add corresponding unit test for backsolved tolerance."
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@@ -20,7 +20,6 @@ import (
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"encoding/json"
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"fmt"
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"io"
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"math"
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"testing"
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"time"
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@@ -33,14 +32,9 @@ import (
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"k8s.io/kubernetes/pkg/controller/podautoscaler/metrics"
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"k8s.io/kubernetes/pkg/runtime"
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glog "github.com/golang/glog"
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"github.com/stretchr/testify/assert"
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heapster "k8s.io/heapster/api/v1/types"
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)
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// unit tests need tolerance awareness to calibrate.
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const (
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tolerance = .1
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"github.com/stretchr/testify/assert"
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)
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func (w fakeResponseWrapper) DoRaw() ([]byte, error) {
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@@ -212,7 +206,7 @@ func (tc *testCase) verifyResults(t *testing.T) {
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func (tc *testCase) runTest(t *testing.T) {
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testClient := tc.prepareTestClient(t)
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metricsClient := metrics.NewHeapsterMetricsClient(testClient, metrics.DefaultHeapsterNamespace, metrics.DefaultHeapsterScheme, metrics.DefaultHeapsterService, metrics.DefaultHeapsterPort)
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hpaController := NewHorizontalController(testClient, metricsClient, tolerance, time.Second, time.Second)
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hpaController := NewHorizontalController(testClient, metricsClient)
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err := hpaController.reconcileAutoscalers()
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assert.Equal(t, nil, err)
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if tc.verifyEvents {
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@@ -366,64 +360,4 @@ func TestEventNotCreated(t *testing.T) {
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tc.runTest(t)
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}
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// TestComputedToleranceAlgImplementation is a regression test which
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// back-calculates a minimal percentage for downscaling based on a small percentage
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// increase in pod utilization which is calibrated against the tolerance value.
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func TestComputedToleranceAlgImplementation(t *testing.T) {
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startPods := 10
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// 150 mCPU per pod.
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totalUsedCPUOfAllPods := uint64(startPods * 150)
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// Each pod starts out asking for 2X what is really needed.
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// This means we will have a 50% ratio of used/requested
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totalRequestedCPUOfAllPods := 2 * totalUsedCPUOfAllPods
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requestedToUsed := float64(totalRequestedCPUOfAllPods / totalUsedCPUOfAllPods)
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// Spread the amount we ask over 10 pods. We can add some jitter later in reportedLevels.
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perPodRequested := int(totalRequestedCPUOfAllPods) / startPods
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// Force a minimal scaling event by satisfying (tolerance < 1 - resourcesUsedRatio).
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target := math.Abs(1/(requestedToUsed*(1-tolerance))) + .01
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finalCpuPercentTarget := int(target * 100)
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resourcesUsedRatio := float64(totalUsedCPUOfAllPods) / float64(float64(totalRequestedCPUOfAllPods)*target)
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// the autoscaler will compare this vs. tolearnce. Lets calculate the usageRatio, which will be
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// compared w tolerance.
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usageRatioToleranceValue := float64(1 - resourcesUsedRatio)
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// i.e. .60 * 20 -> scaled down expectation.
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finalPods := math.Ceil(resourcesUsedRatio * float64(startPods))
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glog.Infof("To breach tolerance %f we will create a utilization ratio difference of %f", tolerance, usageRatioToleranceValue)
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tc := testCase{
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minReplicas: 0,
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maxReplicas: 1000,
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initialReplicas: startPods,
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desiredReplicas: int(finalPods),
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CPUTarget: finalCpuPercentTarget,
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reportedLevels: []uint64{
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totalUsedCPUOfAllPods / 10,
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totalUsedCPUOfAllPods / 10,
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totalUsedCPUOfAllPods / 10,
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totalUsedCPUOfAllPods / 10,
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totalUsedCPUOfAllPods / 10,
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totalUsedCPUOfAllPods / 10,
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totalUsedCPUOfAllPods / 10,
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totalUsedCPUOfAllPods / 10,
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totalUsedCPUOfAllPods / 10,
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totalUsedCPUOfAllPods / 10,
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},
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reportedCPURequests: []resource.Quantity{
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resource.MustParse(fmt.Sprint(perPodRequested+100) + "m"),
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resource.MustParse(fmt.Sprint(perPodRequested-100) + "m"),
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resource.MustParse(fmt.Sprint(perPodRequested+10) + "m"),
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resource.MustParse(fmt.Sprint(perPodRequested-10) + "m"),
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resource.MustParse(fmt.Sprint(perPodRequested+2) + "m"),
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resource.MustParse(fmt.Sprint(perPodRequested-2) + "m"),
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resource.MustParse(fmt.Sprint(perPodRequested+1) + "m"),
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resource.MustParse(fmt.Sprint(perPodRequested-1) + "m"),
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resource.MustParse(fmt.Sprint(perPodRequested) + "m"),
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resource.MustParse(fmt.Sprint(perPodRequested) + "m"),
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},
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}
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tc.runTest(t)
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}
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// TODO: add more tests, e.g., enforcement of upscal/downscale window.
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// TODO: add more tests
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