99 lines
3.3 KiB
C
99 lines
3.3 KiB
C
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/**
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* lib/minmax.c: windowed min/max tracker
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*
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* Kathleen Nichols' algorithm for tracking the minimum (or maximum)
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* value of a data stream over some fixed time interval. (E.g.,
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* the minimum RTT over the past five minutes.) It uses constant
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* space and constant time per update yet almost always delivers
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* the same minimum as an implementation that has to keep all the
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* data in the window.
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*
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* The algorithm keeps track of the best, 2nd best & 3rd best min
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* values, maintaining an invariant that the measurement time of
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* the n'th best >= n-1'th best. It also makes sure that the three
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* values are widely separated in the time window since that bounds
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* the worse case error when that data is monotonically increasing
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* over the window.
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*
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* Upon getting a new min, we can forget everything earlier because
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* it has no value - the new min is <= everything else in the window
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* by definition and it's the most recent. So we restart fresh on
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* every new min and overwrites 2nd & 3rd choices. The same property
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* holds for 2nd & 3rd best.
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*/
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#include <linux/module.h>
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#include <linux/win_minmax.h>
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/* As time advances, update the 1st, 2nd, and 3rd choices. */
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static u32 minmax_subwin_update(struct minmax *m, u32 win,
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const struct minmax_sample *val)
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{
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u32 dt = val->t - m->s[0].t;
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if (unlikely(dt > win)) {
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/*
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* Passed entire window without a new val so make 2nd
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* choice the new val & 3rd choice the new 2nd choice.
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* we may have to iterate this since our 2nd choice
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* may also be outside the window (we checked on entry
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* that the third choice was in the window).
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*/
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m->s[0] = m->s[1];
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m->s[1] = m->s[2];
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m->s[2] = *val;
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if (unlikely(val->t - m->s[0].t > win)) {
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m->s[0] = m->s[1];
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m->s[1] = m->s[2];
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m->s[2] = *val;
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}
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} else if (unlikely(m->s[1].t == m->s[0].t) && dt > win/4) {
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/*
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* We've passed a quarter of the window without a new val
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* so take a 2nd choice from the 2nd quarter of the window.
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*/
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m->s[2] = m->s[1] = *val;
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} else if (unlikely(m->s[2].t == m->s[1].t) && dt > win/2) {
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/*
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* We've passed half the window without finding a new val
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* so take a 3rd choice from the last half of the window
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*/
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m->s[2] = *val;
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}
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return m->s[0].v;
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}
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/* Check if new measurement updates the 1st, 2nd or 3rd choice max. */
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u32 minmax_running_max(struct minmax *m, u32 win, u32 t, u32 meas)
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{
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struct minmax_sample val = { .t = t, .v = meas };
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if (unlikely(val.v >= m->s[0].v) || /* found new max? */
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unlikely(val.t - m->s[2].t > win)) /* nothing left in window? */
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return minmax_reset(m, t, meas); /* forget earlier samples */
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if (unlikely(val.v >= m->s[1].v))
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m->s[2] = m->s[1] = val;
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else if (unlikely(val.v >= m->s[2].v))
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m->s[2] = val;
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return minmax_subwin_update(m, win, &val);
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}
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EXPORT_SYMBOL(minmax_running_max);
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/* Check if new measurement updates the 1st, 2nd or 3rd choice min. */
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u32 minmax_running_min(struct minmax *m, u32 win, u32 t, u32 meas)
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{
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struct minmax_sample val = { .t = t, .v = meas };
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if (unlikely(val.v <= m->s[0].v) || /* found new min? */
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unlikely(val.t - m->s[2].t > win)) /* nothing left in window? */
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return minmax_reset(m, t, meas); /* forget earlier samples */
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if (unlikely(val.v <= m->s[1].v))
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m->s[2] = m->s[1] = val;
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else if (unlikely(val.v <= m->s[2].v))
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m->s[2] = val;
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return minmax_subwin_update(m, win, &val);
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}
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