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HPA E2E Behavior Test: Scale up/down controls

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This commit is contained in:
Piotr Nosek 2022-08-16 16:53:16 +00:00
parent eb317ecd40
commit a4bc164989
1 changed files with 109 additions and 1 deletions
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110
test/e2e/autoscaling/horizontal_pod_autoscaling_behavior.go
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@ -19,6 +19,7 @@ package autoscaling
import (
"time"
autoscalingv2 "k8s.io/api/autoscaling/v2"
"k8s.io/kubernetes/test/e2e/framework"
e2eautoscaling "k8s.io/kubernetes/test/e2e/framework/autoscaling"
admissionapi "k8s.io/pod-security-admission/api"
@ -26,7 +27,7 @@ import (
"github.com/onsi/ginkgo/v2"
)
var _ = SIGDescribe("[Feature:HPA] [Serial] [Slow] Horizontal pod autoscaling (non-default behavior)", func() {
var _ = SIGDescribe("[Feature:HPA] [Serial] [Slow] [Feature:BHPA] Horizontal pod autoscaling (non-default behavior)", func() {
f := framework.NewDefaultFramework("horizontal-pod-autoscaling")
f.NamespacePodSecurityEnforceLevel = admissionapi.LevelPrivileged
@ -378,4 +379,111 @@ var _ = SIGDescribe("[Feature:HPA] [Serial] [Slow] Horizontal pod autoscaling (n
framework.ExpectEqual(timeWaitedFor2 < deadline, true, "waited %s, wanted less than %s", timeWaitedFor2, deadline)
})
})
ginkgo.Describe("with both scale up and down controls configured", func() {
ginkgo.It("should keep recommendation within the range over two stabilization windows", func() {
ginkgo.By("setting up resource consumer and HPA")
initPods := 2
initCPUUsageTotal := initPods * usageForSingleReplica
upScaleStabilization := 3 * time.Minute
downScaleStabilization := 3 * time.Minute
rc := e2eautoscaling.NewDynamicResourceConsumer(
hpaName, f.Namespace.Name, e2eautoscaling.KindDeployment, initPods,
initCPUUsageTotal, 0, 0, int64(podCPURequest), 200,
f.ClientSet, f.ScalesGetter, e2eautoscaling.Disable, e2eautoscaling.Idle,
)
defer rc.CleanUp()
hpa := e2eautoscaling.CreateCPUHorizontalPodAutoscalerWithBehavior(
rc, int32(targetCPUUtilizationPercent), 2, 10,
e2eautoscaling.HPABehaviorWithStabilizationWindows(upScaleStabilization, downScaleStabilization),
)
defer e2eautoscaling.DeleteHPAWithBehavior(rc, hpa.Name)
ginkgo.By("triggering scale up by increasing consumption")
rc.ConsumeCPU(5 * usageForSingleReplica)
waitDeadline := upScaleStabilization
ginkgo.By("verifying number of replicas stay in desired range within stabilisation window")
rc.EnsureDesiredReplicasInRange(2, 2, waitDeadline, hpa.Name)
ginkgo.By("waiting for replicas to scale up after stabilisation window passed")
waitStart := time.Now()
waitDeadline = maxHPAReactionTime + maxResourceConsumerDelay + waitBuffer
rc.WaitForReplicas(4, waitDeadline)
timeWaited := time.Now().Sub(waitStart)
framework.Logf("time waited for scale up: %s", timeWaited)
framework.ExpectEqual(timeWaited < waitDeadline, true, "waited %s, wanted less than %s", timeWaited, waitDeadline)
ginkgo.By("triggering scale down by lowering consumption")
rc.ConsumeCPU(2 * usageForSingleReplica)
waitDeadline = downScaleStabilization
ginkgo.By("verifying number of replicas stay in desired range within stabilisation window")
rc.EnsureDesiredReplicasInRange(4, 4, waitDeadline, hpa.Name)
ginkgo.By("waiting for replicas to scale down after stabilisation window passed")
waitStart = time.Now()
waitDeadline = maxHPAReactionTime + maxResourceConsumerDelay + waitBuffer
rc.WaitForReplicas(2, waitDeadline)
timeWaited = time.Now().Sub(waitStart)
framework.Logf("time waited for scale down: %s", timeWaited)
framework.ExpectEqual(timeWaited < waitDeadline, true, "waited %s, wanted less than %s", timeWaited, waitDeadline)
})
ginkgo.It("should keep recommendation within the range with stabilization window and pod limit rate", func() {
ginkgo.By("setting up resource consumer and HPA")
initPods := 2
initCPUUsageTotal := initPods * usageForSingleReplica
downScaleStabilization := 3 * time.Minute
limitWindowLength := 2 * time.Minute
podsLimitPerMinute := 1
rc := e2eautoscaling.NewDynamicResourceConsumer(
hpaName, f.Namespace.Name, e2eautoscaling.KindDeployment, initPods,
initCPUUsageTotal, 0, 0, int64(podCPURequest), 200,
f.ClientSet, f.ScalesGetter, e2eautoscaling.Disable, e2eautoscaling.Idle,
)
defer rc.CleanUp()
scaleUpRule := e2eautoscaling.HPAScalingRuleWithScalingPolicy(autoscalingv2.PodsScalingPolicy, int32(podsLimitPerMinute), int32(limitWindowLength.Seconds()))
scaleDownRule := e2eautoscaling.HPAScalingRuleWithStabilizationWindow(int32(downScaleStabilization.Seconds()))
hpa := e2eautoscaling.CreateCPUHorizontalPodAutoscalerWithBehavior(
rc, int32(targetCPUUtilizationPercent), 2, 10,
e2eautoscaling.HPABehaviorWithScaleUpAndDownRules(scaleUpRule, scaleDownRule),
)
defer e2eautoscaling.DeleteHPAWithBehavior(rc, hpa.Name)
ginkgo.By("triggering scale up by increasing consumption")
rc.ConsumeCPU(4 * usageForSingleReplica)
waitDeadline := limitWindowLength
ginkgo.By("verifying number of replicas stay in desired range with pod limit rate")
rc.EnsureDesiredReplicasInRange(2, 3, waitDeadline, hpa.Name)
ginkgo.By("waiting for replicas to scale up")
waitStart := time.Now()
waitDeadline = limitWindowLength + maxHPAReactionTime + maxResourceConsumerDelay + waitBuffer
rc.WaitForReplicas(4, waitDeadline)
timeWaited := time.Now().Sub(waitStart)
framework.Logf("time waited for scale up: %s", timeWaited)
framework.ExpectEqual(timeWaited < waitDeadline, true, "waited %s, wanted less than %s", timeWaited, waitDeadline)
ginkgo.By("triggering scale down by lowering consumption")
rc.ConsumeCPU(2 * usageForSingleReplica)
ginkgo.By("verifying number of replicas stay in desired range within stabilisation window")
waitDeadline = downScaleStabilization
rc.EnsureDesiredReplicasInRange(4, 4, waitDeadline, hpa.Name)
ginkgo.By("waiting for replicas to scale down after stabilisation window passed")
waitStart = time.Now()
waitDeadline = maxHPAReactionTime + maxResourceConsumerDelay + waitBuffer
rc.WaitForReplicas(2, waitDeadline)
timeWaited = time.Now().Sub(waitStart)
framework.Logf("time waited for scale down: %s", timeWaited)
framework.ExpectEqual(timeWaited < waitDeadline, true, "waited %s, wanted less than %s", timeWaited, waitDeadline)
})
})
})