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|
package stats
import (
"math"
"time"
)
// PatternAnalysis represents the analysis of I/O patterns
type PatternAnalysis struct {
// Burst detection
BurstCount int
BurstDuration time.Duration
BurstThreshold int64
// Periodicity detection
IsPeriodic bool
PeriodDuration time.Duration
// Trend analysis
TrendSlope float64
TrendDirection string // "increasing", "decreasing", "stable"
}
// AnalyzePatterns analyzes the I/O patterns from the statistics
func (s *IOStats) AnalyzePatterns() *PatternAnalysis {
s.mu.RLock()
defer s.mu.RUnlock()
analysis := &PatternAnalysis{
BurstThreshold: 1000, // Configurable threshold
}
// Analyze bursts
analysis.analyzeBursts(s.bytesHistory, s.timestamps)
// Analyze periodicity
analysis.analyzePeriodicity(s.bytesHistory, s.timestamps)
// Analyze trends
analysis.analyzeTrend(s.bytesHistory, s.timestamps)
return analysis
}
func (a *PatternAnalysis) analyzeBursts(bytes []int64, timestamps []time.Time) {
if len(bytes) < 2 {
return
}
var currentBurstSize int64
var burstStart time.Time
inBurst := false
for i := 0; i < len(bytes); i++ {
if bytes[i] > a.BurstThreshold {
if !inBurst {
burstStart = timestamps[i]
inBurst = true
}
currentBurstSize += bytes[i]
} else if inBurst {
a.BurstCount++
a.BurstDuration += timestamps[i].Sub(burstStart)
inBurst = false
currentBurstSize = 0
}
}
}
func (a *PatternAnalysis) analyzePeriodicity(bytes []int64, timestamps []time.Time) {
if len(bytes) < 4 {
return
}
// Simple periodicity detection using autocorrelation
mean := 0.0
for _, b := range bytes {
mean += float64(b)
}
mean /= float64(len(bytes))
var maxCorrelation float64
var bestPeriod int
// Check for periods up to half the data length
for period := 1; period < len(bytes)/2; period++ {
correlation := 0.0
count := 0
for i := 0; i < len(bytes)-period; i++ {
correlation += (float64(bytes[i]) - mean) * (float64(bytes[i+period]) - mean)
count++
}
if count > 0 {
correlation /= float64(count)
if correlation > maxCorrelation {
maxCorrelation = correlation
bestPeriod = period
}
}
}
// If we found a strong correlation, consider it periodic
if maxCorrelation > 0.7 { // Threshold for periodicity
a.IsPeriodic = true
if bestPeriod > 0 && bestPeriod < len(timestamps)-1 {
a.PeriodDuration = timestamps[bestPeriod].Sub(timestamps[0])
}
}
}
func (a *PatternAnalysis) analyzeTrend(bytes []int64, timestamps []time.Time) {
if len(bytes) < 2 {
return
}
// Simple linear regression
var sumX, sumY, sumXY, sumX2 float64
n := float64(len(bytes))
for i := 0; i < len(bytes); i++ {
x := float64(timestamps[i].UnixNano())
y := float64(bytes[i])
sumX += x
sumY += y
sumXY += x * y
sumX2 += x * x
}
// Calculate slope
a.TrendSlope = (n*sumXY - sumX*sumY) / (n*sumX2 - sumX*sumX)
// Determine trend direction
if math.Abs(a.TrendSlope) < 0.1 {
a.TrendDirection = "stable"
} else if a.TrendSlope > 0 {
a.TrendDirection = "increasing"
} else {
a.TrendDirection = "decreasing"
}
}
|
