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add more e2e for WorkloadRebalancer #4961

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May 22, 2024
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add more e2e for WorkloadRebalancer #4961

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121 changes: 119 additions & 2 deletions test/e2e/workloadrebalancer_test.go
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Original file line number Diff line number Diff line change
Expand Up @@ -73,7 +73,7 @@ var _ = framework.SerialDescribe("workload rebalancer testing", func() {
// sort member clusters in increasing order
targetClusters = framework.ClusterNames()[0:2]
sort.Strings(targetClusters)
taint = corev1.Taint{Key: "workload-rebalancer-test", Effect: corev1.TaintEffectNoExecute}
taint = corev1.Taint{Key: "workload-rebalancer-test-" + randomStr, Effect: corev1.TaintEffectNoExecute}

deploy = helper.NewDeployment(namespace, deployName)
notExistDeploy = helper.NewDeployment(namespace, notExistDeployName)
Expand Down Expand Up @@ -176,7 +176,116 @@ var _ = framework.SerialDescribe("workload rebalancer testing", func() {
checkWorkloadRebalancerResult(expectedWorkloads)
})

ginkgo.By("step4: udpate WorkloadRebalancer spec workloads", func() {
ginkgo.By("step4: update WorkloadRebalancer spec workloads", func() {
// update workload list from {deploy, clusterrole, notExistDeployObjRef} to {clusterroleObjRef, newAddedDeployObjRef}
updatedWorkloads := []appsv1alpha1.ObjectReference{clusterroleObjRef, newAddedDeployObjRef}
framework.UpdateWorkloadRebalancer(karmadaClient, rebalancerName, updatedWorkloads)

expectedWorkloads := []appsv1alpha1.ObservedWorkload{
{Workload: deployObjRef, Result: appsv1alpha1.RebalanceSuccessful},
{Workload: newAddedDeployObjRef, Result: appsv1alpha1.RebalanceSuccessful},
{Workload: clusterroleObjRef, Result: appsv1alpha1.RebalanceSuccessful},
}
framework.WaitRebalancerObservedWorkloads(karmadaClient, rebalancerName, expectedWorkloads)
})
})
})

// 2. static weight scheduling
ginkgo.Context("static weight schedule type", func() {
ginkgo.BeforeEach(func() {
policy.Spec.Placement.ReplicaScheduling = helper.NewStaticWeightPolicyStrategy(targetClusters, []int64{2, 1})
policy.Spec.Placement.ClusterTolerations = []corev1.Toleration{{
Key: taint.Key,
Effect: taint.Effect,
Operator: corev1.TolerationOpExists,
TolerationSeconds: pointer.Int64(0),
}}
})

ginkgo.It("reschedule when policy is static weight schedule type", func() {
ginkgo.By("step1: check first schedule result", func() {
// after first schedule, deployment is assigned as 2:1 in target clusters and clusterrole propagated to each cluster.
framework.AssertBindingScheduledClusters(karmadaClient, namespace, deployBindingName, [][]string{targetClusters})
framework.WaitClusterRolePresentOnClustersFitWith(targetClusters, clusterroleName, func(_ *rbacv1.ClusterRole) bool { return true })
})

ginkgo.By("step2: add taints to cluster to mock cluster failure", func() {
err := taintCluster(controlPlaneClient, targetClusters[0], taint)
gomega.Expect(err).ShouldNot(gomega.HaveOccurred())

framework.AssertBindingScheduledClusters(karmadaClient, namespace, deployBindingName, [][]string{targetClusters[1:]})
framework.WaitGracefulEvictionTasksDone(karmadaClient, namespace, deployBindingName)

err = recoverTaintedCluster(controlPlaneClient, targetClusters[0], taint)
gomega.Expect(err).ShouldNot(gomega.HaveOccurred())
})

ginkgo.By("step3: trigger a reschedule by WorkloadRebalancer", func() {
framework.CreateWorkloadRebalancer(karmadaClient, rebalancer)
ginkgo.DeferCleanup(func() {
framework.RemoveWorkloadRebalancer(karmadaClient, rebalancerName)
})

// actual replicas propagation of deployment should reschedule back to `targetClusters`,
// which represents rebalancer changed deployment replicas propagation.
framework.AssertBindingScheduledClusters(karmadaClient, namespace, deployBindingName, [][]string{targetClusters})

expectedWorkloads := []appsv1alpha1.ObservedWorkload{
{Workload: deployObjRef, Result: appsv1alpha1.RebalanceSuccessful},
{Workload: notExistDeployObjRef, Result: appsv1alpha1.RebalanceFailed, Reason: appsv1alpha1.RebalanceObjectNotFound},
{Workload: clusterroleObjRef, Result: appsv1alpha1.RebalanceSuccessful},
}
checkWorkloadRebalancerResult(expectedWorkloads)
})

ginkgo.By("step4: update WorkloadRebalancer spec workloads", func() {
// update workload list from {deploy, clusterrole, notExistDeployObjRef} to {clusterroleObjRef, newAddedDeployObjRef}
updatedWorkloads := []appsv1alpha1.ObjectReference{clusterroleObjRef, newAddedDeployObjRef}
framework.UpdateWorkloadRebalancer(karmadaClient, rebalancerName, updatedWorkloads)

expectedWorkloads := []appsv1alpha1.ObservedWorkload{
{Workload: deployObjRef, Result: appsv1alpha1.RebalanceSuccessful},
{Workload: newAddedDeployObjRef, Result: appsv1alpha1.RebalanceSuccessful},
{Workload: clusterroleObjRef, Result: appsv1alpha1.RebalanceSuccessful},
}
framework.WaitRebalancerObservedWorkloads(karmadaClient, rebalancerName, expectedWorkloads)
})
})
})

// 3. aggregated scheduling
ginkgo.Context("aggregated schedule type", func() {
ginkgo.BeforeEach(func() {
policy.Spec.Placement.ReplicaScheduling = &policyv1alpha1.ReplicaSchedulingStrategy{
ReplicaSchedulingType: policyv1alpha1.ReplicaSchedulingTypeDivided,
ReplicaDivisionPreference: policyv1alpha1.ReplicaDivisionPreferenceAggregated,
}
})

ginkgo.It("reschedule when policy is aggregated schedule type", func() {
ginkgo.By("step1: check first schedule result", func() {
// after first schedule, deployment is assigned to exactly one of the target clusters while clusterrole propagated to each cluster.
possibleScheduledClusters := getPossibleClustersInAggregatedScheduling(targetClusters)
framework.AssertBindingScheduledClusters(karmadaClient, namespace, deployBindingName, possibleScheduledClusters)
framework.WaitClusterRolePresentOnClustersFitWith(targetClusters, clusterroleName, func(_ *rbacv1.ClusterRole) bool { return true })
})

ginkgo.By("step2: trigger a reschedule by WorkloadRebalancer", func() {
framework.CreateWorkloadRebalancer(karmadaClient, rebalancer)
ginkgo.DeferCleanup(func() {
framework.RemoveWorkloadRebalancer(karmadaClient, rebalancerName)
})

expectedWorkloads := []appsv1alpha1.ObservedWorkload{
{Workload: deployObjRef, Result: appsv1alpha1.RebalanceSuccessful},
{Workload: notExistDeployObjRef, Result: appsv1alpha1.RebalanceFailed, Reason: appsv1alpha1.RebalanceObjectNotFound},
{Workload: clusterroleObjRef, Result: appsv1alpha1.RebalanceSuccessful},
}
checkWorkloadRebalancerResult(expectedWorkloads)
})

ginkgo.By("step3: update WorkloadRebalancer spec workloads", func() {
// update workload list from {deploy, clusterrole, notExistDeployObjRef} to {clusterroleObjRef, newAddedDeployObjRef}
updatedWorkloads := []appsv1alpha1.ObjectReference{clusterroleObjRef, newAddedDeployObjRef}
framework.UpdateWorkloadRebalancer(karmadaClient, rebalancerName, updatedWorkloads)
Expand All @@ -200,3 +309,11 @@ func bindingHasRescheduled(spec workv1alpha2.ResourceBindingSpec, status workv1a
}
return true
}

func getPossibleClustersInAggregatedScheduling(targetClusters []string) [][]string {
possibleScheduledClusters := make([][]string, 0)
for _, cluster := range targetClusters {
possibleScheduledClusters = append(possibleScheduledClusters, []string{cluster})
}
return possibleScheduledClusters
}