Labels map[string]string

	// value captured in current cycle
	Current float64

	// Used when system provides cumulative (since uptime) values
	// helps in calculating the current value by comparing the new
	// cumulative value with previous one
	Cumulative float64

	Max   float64
	Avg   float64
	Sum   float64
	Count uint64
}

func init() {

		m.expMovingAvg = float64(bytesSinceLastWindow) / duration.Seconds()
		return
	}

	increment := float64(bytesSinceLastWindow) / duration.Seconds()
	m.expMovingAvg = exponentialMovingAverage(betaBucket, m.expMovingAvg, increment)
}

// exponentialMovingAverage calculates the exponential moving average
func exponentialMovingAverage(beta, previousAvg, incrementAvg float64) float64 {

		if globalExpiryState != nil {
			expPendingTasks.Value = float64(globalExpiryState.PendingTasks())
			expMissedTasks.Value = float64(globalExpiryState.stats.MissedTasks())
			expMissedFreeVersions.Value = float64(globalExpiryState.stats.MissedFreeVersTasks())
			expMissedTierJournalTasks.Value = float64(globalExpiryState.stats.MissedTierJournalTasks())
			expNumWorkers.Value = float64(globalExpiryState.stats.NumWorkers())
		}

			Buckets: []float64{.05, .1, .25, .5, 1, 2.5, 5, 10},
		},
		[]string{"api"},
	)
	bucketHTTPRequestsDuration = prometheus.NewHistogramVec(
		prometheus.HistogramOpts{
			Name:    "s3_ttfb_seconds",
			Help:    "Time taken by requests served by current MinIO server instance per bucket",
			Buckets: []float64{.05, .1, .25, .5, 1, 2.5, 5, 10},
		},
		[]string{"api", "bucket"},
	)

		return err
	}

	m.Set(memTotal, float64(memMetrics.Total))
	m.Set(memUsed, float64(memMetrics.Used))
	usedPerc := float64(memMetrics.Used) * 100 / float64(memMetrics.Total)
	m.Set(memUsedPerc, usedPerc)
	m.Set(memFree, float64(memMetrics.Free))
	m.Set(memBuffers, float64(memMetrics.Buffers))
	m.Set(memCache, float64(memMetrics.Cache))
	m.Set(memShared, float64(memMetrics.Shared))

		err = errors.New("entity too large")
		return
	}

	configuredPartSize := minPartSize
	// Use floats for part size for all calculations to avoid
	// overflows during float64 to int64 conversions.
	partSizeFlt := float64(objectSize / maxPartsCount)
	partSizeFlt = math.Ceil(partSizeFlt/float64(configuredPartSize)) * float64(configuredPartSize)

	// Part size.
	partSize = int64(partSizeFlt)
	if partSize == 0 {

				vars[idx] = escaper + "<binary>" + escaper
			}
		case int, int8, int16, int32, int64, uint, uint8, uint16, uint32, uint64:
			vars[idx] = utils.ToString(v)
		case float32:
			vars[idx] = strconv.FormatFloat(float64(v), 'f', -1, 32)
		case float64:
			vars[idx] = strconv.FormatFloat(v, 'f', -1, 64)
		case string:
			vars[idx] = escaper + strings.ReplaceAll(v, escaper, escaper+escaper) + escaper
		default:

	storageInfo := clusterDriveMetrics.storageInfo

	m.Set(healthCapacityRawTotalBytes, float64(GetTotalCapacity(storageInfo.Disks)))
	m.Set(healthCapacityRawFreeBytes, float64(GetTotalCapacityFree(storageInfo.Disks)))
	m.Set(healthCapacityUsableTotalBytes, float64(GetTotalUsableCapacity(storageInfo.Disks, storageInfo)))
	m.Set(healthCapacityUsableFreeBytes, float64(GetTotalUsableCapacityFree(storageInfo.Disks, storageInfo)))

	return nil

					// Since we are benchmarking n parallel servers we need to multiply by n.
					// This will give an estimate of the total ops/s.
					latency := float64(atomic.LoadInt64(&lat)) / float64(time.Millisecond)
					b.ReportMetric(float64(n)*float64(ops)/spent.Seconds(), "vops/s")
					b.ReportMetric(latency/float64(ops), "ms/op")
				}
			})
		}
	})
	b.Run("rpc", func(b *testing.B) {
		for par := 1; par <= 32; par *= 2 {

			r.opts.HeaderSize = 0
			need = int(math.Min(float64(b-hdr), float64(need))) // use remaining tokens towards payload
			tokens = need + hdr

		} else { // part of header can be accommodated
			r.opts.HeaderSize -= b - 1
			need = 1 // to ensure we read at least one byte for every Read
			tokens = b
		}
	} else { // all tokens go towards payload
		need = int(math.Min(float64(b), float64(need)))
		tokens = need
	}