clash/transport/tuic/congestion/windowed_filter.go

133 lines
4.3 KiB
Go

package congestion
// WindowedFilter Use the following to construct a windowed filter object of type T.
// For example, a min filter using QuicTime as the time type:
//
// WindowedFilter<T, MinFilter<T>, QuicTime, QuicTime::Delta> ObjectName;
//
// A max filter using 64-bit integers as the time type:
//
// WindowedFilter<T, MaxFilter<T>, uint64_t, int64_t> ObjectName;
//
// Specifically, this template takes four arguments:
// 1. T -- type of the measurement that is being filtered.
// 2. Compare -- MinFilter<T> or MaxFilter<T>, depending on the type of filter
// desired.
// 3. TimeT -- the type used to represent timestamps.
// 4. TimeDeltaT -- the type used to represent continuous time intervals between
// two timestamps. Has to be the type of (a - b) if both |a| and |b| are
// of type TimeT.
type WindowedFilter struct {
// Time length of window.
windowLength int64
estimates []Sample
comparator func(int64, int64) bool
}
type Sample struct {
sample int64
time int64
}
// Compares two values and returns true if the first is greater than or equal
// to the second.
func MaxFilter(a, b int64) bool {
return a >= b
}
// Compares two values and returns true if the first is less than or equal
// to the second.
func MinFilter(a, b int64) bool {
return a <= b
}
func NewWindowedFilter(windowLength int64, comparator func(int64, int64) bool) *WindowedFilter {
return &WindowedFilter{
windowLength: windowLength,
estimates: make([]Sample, 3),
comparator: comparator,
}
}
// Changes the window length. Does not update any current samples.
func (f *WindowedFilter) SetWindowLength(windowLength int64) {
f.windowLength = windowLength
}
func (f *WindowedFilter) GetBest() int64 {
return f.estimates[0].sample
}
func (f *WindowedFilter) GetSecondBest() int64 {
return f.estimates[1].sample
}
func (f *WindowedFilter) GetThirdBest() int64 {
return f.estimates[2].sample
}
func (f *WindowedFilter) Update(sample int64, time int64) {
if f.estimates[0].time == 0 || f.comparator(sample, f.estimates[0].sample) || (time-f.estimates[2].time) > f.windowLength {
f.Reset(sample, time)
return
}
if f.comparator(sample, f.estimates[1].sample) {
f.estimates[1].sample = sample
f.estimates[1].time = time
f.estimates[2].sample = sample
f.estimates[2].time = time
} else if f.comparator(sample, f.estimates[2].sample) {
f.estimates[2].sample = sample
f.estimates[2].time = time
}
// Expire and update estimates as necessary.
if time-f.estimates[0].time > f.windowLength {
// The best estimate hasn't been updated for an entire window, so promote
// second and third best estimates.
f.estimates[0].sample = f.estimates[1].sample
f.estimates[0].time = f.estimates[1].time
f.estimates[1].sample = f.estimates[2].sample
f.estimates[1].time = f.estimates[2].time
f.estimates[2].sample = sample
f.estimates[2].time = time
// Need to iterate one more time. Check if the new best estimate is
// outside the window as well, since it may also have been recorded a
// long time ago. Don't need to iterate once more since we cover that
// case at the beginning of the method.
if time-f.estimates[0].time > f.windowLength {
f.estimates[0].sample = f.estimates[1].sample
f.estimates[0].time = f.estimates[1].time
f.estimates[1].sample = f.estimates[2].sample
f.estimates[1].time = f.estimates[2].time
}
return
}
if f.estimates[1].sample == f.estimates[0].sample && time-f.estimates[1].time > f.windowLength>>2 {
// A quarter of the window has passed without a better sample, so the
// second-best estimate is taken from the second quarter of the window.
f.estimates[1].sample = sample
f.estimates[1].time = time
f.estimates[2].sample = sample
f.estimates[2].time = time
return
}
if f.estimates[2].sample == f.estimates[1].sample && time-f.estimates[2].time > f.windowLength>>1 {
// We've passed a half of the window without a better estimate, so take
// a third-best estimate from the second half of the window.
f.estimates[2].sample = sample
f.estimates[2].time = time
}
}
func (f *WindowedFilter) Reset(newSample int64, newTime int64) {
f.estimates[0].sample = newSample
f.estimates[0].time = newTime
f.estimates[1].sample = newSample
f.estimates[1].time = newTime
f.estimates[2].sample = newSample
f.estimates[2].time = newTime
}