[WIP] implement autoscaling #242
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Code2Life
reviewed
Jun 21, 2025
internal/autoscaler/workerstate.go
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| return true | ||
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| func (w *WorkerState) AddVramSample(workload *WorkloadState, metrics *WorkerMetrics) bool { |
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This could be calculated by select max in Greptime flow to offload memory based calculation
Code2Life
reviewed
Jun 21, 2025
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| func (f *FakeMetricsProvider) GetHistoryMetrics() []*WorkerMetrics { | ||
| metrics := []*WorkerMetrics{} | ||
| startTime := time.Now().Add(-7 * 24 * time.Hour) |
internal/autoscaler/recommender.go
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| ) | ||
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| var ( | ||
| safetyMarginFraction = flag.Float64("recommendation-margin-fraction", 0.15, `Fraction of usage added as the safety margin to the recommended request`) |
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should be in pool and scheduling config, not flags
internal/autoscaler/estimator.go
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| // of 1 sample per minute, this metric is equal to N. | ||
| // This implementation is a very simple heuristic which looks at the total count | ||
| // of samples and the time between the first and the last sample. | ||
| func getConfidence(s *WorkloadState, confidenceInterval time.Duration) float64 { |
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This seems not a normalized confidence factor
internal/autoscaler/workloadstate.go
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| Resources tfv1.Resources | ||
| AutoScalingConfig tfv1.AutoScalingConfig | ||
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| TflopsHistogram vpa.Histogram |
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coupled with vpa algorithm, can not extend with others like XGBoost / LightGBM / Prophet / MLP
…e support value all
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