Don't predict memory in effective hpa #148
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kitianFresh
reviewed
Feb 23, 2022
pkg/recommend/advisor/ehpa.go
Outdated
| @@ -96,11 +96,11 @@ func (a *EHPAAdvisor) Advise(proposed *types.ProposedRecommendation) error { | |||
| targetCpuUtilization := int32(50) // todo: configurable | |||
| maxReplicasFactor := 1.2 // todo: configurable | |||
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| maxCpuUtilization := int32(int64(maxCpuUsage) * 1000 * 100 / requestTotal) | |||
| maxCpuUtilization := int32(int64(maxCpuUsage) * 1000 * 100 * int64(len(a.Pods)) / requestTotal) | |||
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why multiply pods num. maxCpuUsage is average max usage of each pod? I see it is computed only by one timeseries tsList[0], not all
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requestTotal contains total request for workload, so we need to add this.
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maxCpuUsage is max of workload‘s(sum of all pods) usage too, so the original code is right, do not need fix
kitianFresh
reviewed
Feb 23, 2022
pkg/recommend/advisor/ehpa.go
Outdated
| proposedMaxRatio := float64(maxCpuUtilization) / float64(targetCpuUtilization) | ||
| maxReplicasProposed := int32(math.Ceil(proposedMaxRatio * float64(a.ReadyPodNumber) * maxReplicasFactor)) | ||
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| minCpuUtilization := int32(int64(minCpuUsage) * 1000 * 100 / requestTotal) | ||
| minCpuUtilization := int32(int64(minCpuUsage) * 1000 * 100 * int64(len(a.Pods)) / requestTotal) |
mfanjie
reviewed
Feb 23, 2022
| @@ -189,7 +189,7 @@ func (c *EffectiveHPAController) GetHPAMetrics(ctx context.Context, ehpa *autosc | |||
| if metric.Type == autoscalingv2.ResourceMetricSourceType { | |||
| name, err := GetPredictionMetricName(metric.Resource.Name) | |||
| if err != nil { | |||
| return nil, err | |||
| continue | |||
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log error, suggest not make it silent
| @@ -131,7 +131,7 @@ func (c *EffectiveHPAController) NewPredictionObject(ehpa *autoscalingapi.Effect | |||
| if metric.Type == autoscalingv2.ResourceMetricSourceType { | |||
| metricName, err := GetPredictionMetricName(metric.Resource.Name) | |||
| if err != nil { | |||
| return nil, err | |||
| continue | |||
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/lgtm |
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