fs.Float32Var(&o.NodeEvictionRate, "node-eviction-rate", 0.1, "Number of nodes per second on which pods are deleted in case of node failure when a zone is healthy (see --unhealthy-zone-threshold for definition of healthy/unhealthy). Zone refers to entire cluster in non-multizone clusters.")

	Versions []string        // versions present in a counterconfig
	Counters []CounterConfig `json:",omitempty"`
	Stacks   []CounterConfig `json:",omitempty"`
}

type CounterConfig struct {
	Name  string
	Rate  float64 // If X <= Rate, report this counter
	Depth int     `json:",omitempty"` // for stack counters
}

// A Report is what's uploaded (or saved locally)
type Report struct {

				metrics.RecordRequestAbort(req, nil)
			} else {
				metrics.RecordRequestAbort(req, info)
			}
			// This call can have different handlers, but the default chain rate limits. Call it after the metrics are updated
			// in case the rate limit delays it.  If you outrun the rate for this one timed out requests, something has gone
			// seriously wrong with your server, but generally having a logging signal for timeouts is useful.

import java.math.BigDecimal;

public interface PerformanceFlakinessDataProvider {
    /**
     * For the scenario whose flakiness rate is higher than 5%, we see it as "flaky scenario"
     */
    double FLAKY_THRESHOLD = 0.05;

    /**
     * Flakiness rate of a scenario is the number of times the scenario had a regression of an improvement with more than 99.9%

*/

package waitgroup

import (
	"context"
	"strings"
	"sync"
	"testing"
	"time"

	"golang.org/x/time/rate"
	"k8s.io/apimachinery/pkg/util/wait"
)

func TestRateLimitedSafeWaitGroup(t *testing.T) {
	// we want to keep track of how many times rate limiter Wait method is
	// being invoked, both before and after the wait group is in waiting mode.
	limiter := &limiterWrapper{}

	defaultCacheSize = 4096
)

// limitEnforcer enforces a single type of event rate limit, such as server, namespace, or source+object
type limitEnforcer struct {
	// type of this limit
	limitType eventratelimitapi.LimitType
	// cache for holding the rate limiters
	cache cache
	// a keyFunc which is responsible for computing a single key based on input

    ]),
  ], panelHeight=10, startY=1)
  + g.util.grid.makeGrid([
    row.new('Push Information')
    + row.withPanels([
      panels.timeSeries.xdsPushes(
        'XDS Pushes', queries.xdsPushes, |||
          Rate of XDS push operations, by type. This is incremented on a per-proxy basis.
        |||
      ),
      panels.timeSeries.base(
        'Events', queries.pilotEvents, |||
          Size of each xDS push.
        |||

//
// A sponge builds a pseudo-random function from a public pseudo-random
// permutation, by applying the permutation to a state of "rate + capacity"
// bytes, but hiding "capacity" of the bytes.
//
// A sponge starts out with a zero state. To hash an input using a sponge, up
// to "rate" bytes of the input are XORed into the sponge's state. The sponge
// is then "full" and the permutation is applied to "empty" it. This process is

//

func generateExponentialSamples(nsamples int, rate float64, seed uint64) []float64 {
	r := New(NewPCG(seed, seed))
	samples := make([]float64, nsamples)
	for i := range samples {
		samples[i] = r.ExpFloat64() / rate
	}
	return samples
}

func testExponentialDistribution(t *testing.T, nsamples int, rate float64, seed uint64) {
	//fmt.Printf("testing nsamples=%v rate=%v seed=%v\n", nsamples, rate, seed);

	mockProm := mockPromAPI{
		cannedResponse: map[string]prometheus_model.Value{
			"sum(rate(istio_requests_total{destination_workload=~\"details.*\", destination_workload_namespace=~\".*\",reporter=\"destination\"}[1m0s]))": prometheus_model.Vector{ // nolint: lll
				&prometheus_model.Sample{Value: 0.04},
			},