2c66d47786
Currently, the HPA considers unready pods the same as ready pods when looking at their CPU and custom metric usage. However, pods frequently use extra CPU during initialization, so we want to consider them separately. This commit causes the HPA to consider unready pods as having 0 CPU usage when scaling up, and ignores them when scaling down. If, when scaling up, factoring the unready pods as having 0 CPU would cause a downscale instead, we simply choose not to scale. Otherwise, we simply scale up at the reduced amount caculated by factoring the pods in at zero CPU usage. The effect is that unready pods cause the autoscaler to be a bit more conservative -- large increases in CPU usage can still cause scales, even with unready pods in the mix, but will not cause the scale factors to be as large, in anticipation of the new pods later becoming ready and handling load. Similarly, if there are pods for which no metrics have been retrieved, these pods are treated as having 100% of the requested metric when scaling down, and 0% when scaling up. As above, this cannot change the direction of the scale. This commit also changes the HPA to ignore superfluous metrics -- as long as metrics for all ready pods are present, the HPA we make scaling decisions. Currently, this only works for CPU. For custom metrics, we cannot identify which metrics go to which pods if we get superfluous metrics, so we abort the scale.
55 lines
1.9 KiB
Go
55 lines
1.9 KiB
Go
/*
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Copyright 2015 The Kubernetes Authors.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License.
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*/
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package metrics
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// GetResourceUtilizationRatio takes in a set of metrics, a set of matching requests,
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// and a target utilization percentage, and calcuates the the ratio of
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// desired to actual utilization (returning that and the actual utilization)
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func GetResourceUtilizationRatio(metrics PodResourceInfo, requests map[string]int64, targetUtilization int32) (float64, int32, error) {
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metricsTotal := int64(0)
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requestsTotal := int64(0)
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for podName, metricValue := range metrics {
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request, hasRequest := requests[podName]
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if !hasRequest {
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// we check for missing requests elsewhere, so assuming missing requests == extraneous metrics
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continue
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}
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metricsTotal += metricValue
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requestsTotal += request
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}
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currentUtilization := int32((metricsTotal * 100) / requestsTotal)
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return float64(currentUtilization) / float64(targetUtilization), currentUtilization, nil
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}
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// GetMetricUtilizationRatio takes in a set of metrics and a target utilization value,
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// and calcuates the ratio of desired to actual utilization
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// (returning that and the actual utilization)
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func GetMetricUtilizationRatio(metrics PodMetricsInfo, targetUtilization float64) (float64, float64) {
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metricsTotal := float64(0)
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for _, metricValue := range metrics {
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metricsTotal += metricValue
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}
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currentUtilization := metricsTotal / float64(len(metrics))
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return currentUtilization / targetUtilization, currentUtilization
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}
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