"type": "typeValue",
            "value": "0",
            "averageValue": "0",
            "averageUtilization": 4
          }
        }
      }
    ],
    "behavior": {
      "scaleUp": {
        "stabilizationWindowSeconds": 3,
        "selectPolicy": "selectPolicyValue",
        "policies": [
          {
            "type": "typeValue",
            "value": 2,

spec:
  behavior:
    scaleDown:
      policies:
      - periodSeconds: 3
        type: typeValue
        value: 2
      selectPolicy: selectPolicyValue
      stabilizationWindowSeconds: 3
    scaleUp:
      policies:
      - periodSeconds: 3
        type: typeValue
        value: 2
      selectPolicy: selectPolicyValue
      stabilizationWindowSeconds: 3
  maxReplicas: 3
  metrics:
  - containerResource:

		value  int64
		max    int64
		result int64
		scale  int32
	}{
		{100, 10, 1, 2},
		{100, 10, 1, 2},
		{100, 100, 1, 1},
		{1, 10, 1, 0},
	} {
		r, s := removeInt64Factors(test.value, test.max)
		if r != test.result {
			t.Errorf("%v: unexpected result: %d", test, r)
		}
		if s != test.scale {
			t.Errorf("%v: unexpected scale: %d", test, s)
		}
	}
}

	// overflow.
	//
	// An alpha-premultiplied color component c has been scaled by alpha (a),
	// so has valid values 0 <= c <= a.
	RGBA() (r, g, b, a uint32)
}

// RGBA represents a traditional 32-bit alpha-premultiplied color, having 8
// bits for each of red, green, blue and alpha.
//
// An alpha-premultiplied color component C has been scaled by alpha (A), so
// has valid values 0 <= C <= A.
type RGBA struct {

tfr.func @tf__my_requantize(%input: !tfr.tensor) -> !tfr.tensor {
  %raw_data = tfr.quant_raw_data(%input) : (!tfr.tensor) -> !tfr.tensor
  %scale, %zp = tfr.quant_qparam(%input) : (!tfr.tensor) -> (!tfr.tensor, !tfr.tensor)
  %result = tfr.call @tf__requantize(%raw_data, %scale, %zp) : (!tfr.tensor, !tfr.tensor, !tfr.tensor) -> !tfr.tensor
  tfr.return %result : !tfr.tensor
}

// CHECK-LABEL: @tf__intermediate

          - --container=metrics-server
          - --poll-period=30000
          - --estimator=exponential
          # Specifies the smallest cluster (defined in number of nodes)
          # resources will be scaled to.
          - --minClusterSize={{ metrics_server_min_cluster_size }}
          # Use kube-apiserver metrics to avoid periodically listing nodes.
          - --use-metrics=true
      volumes:

1. [Standalone Deployment](#standalone-deployment)
2. [Distributed Deployment](#distributed-deployment)
3. [Cloud Scale Deployment](#cloud-scale-deployment)

## 1. Standalone Deployment

To host multiple tenants on a single machine, run one MinIO Server per tenant with a dedicated HTTPS port, configuration, and data directory.

import (
	"math"
	"math/big"
	"testing"
)

func TestScaledValueInternal(t *testing.T) {
	tests := []struct {
		unscaled *big.Int
		scale    int
		newScale int

		want int64
	}{
		// remain scale
		{big.NewInt(1000), 0, 0, 1000},

		// scale down
		{big.NewInt(1000), 0, -3, 1},
		{big.NewInt(1000), 3, 0, 1},
		{big.NewInt(0), 3, 0, 0},

		// always round up
		{big.NewInt(999), 3, 0, 1},

MinIO is a cloud-native application designed to scale in a sustainable manner in multi-tenant environments. Orchestration platforms provide perfect launchpad for MinIO to scale. Below is the list of MinIO deployment documents for various orchestration platforms:

				},
			}
			stabilizationWindow := int32(c.RandUint64())
			maxPolicy := autoscaling.MaxPolicySelect
			minPolicy := autoscaling.MinPolicySelect
			s.Behavior = &autoscaling.HorizontalPodAutoscalerBehavior{
				ScaleUp: &autoscaling.HPAScalingRules{
					StabilizationWindowSeconds: &stabilizationWindow,
					SelectPolicy:               &maxPolicy,
					Policies: []autoscaling.HPAScalingPolicy{
						{