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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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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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+
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+ return minmax_subwin_update(m, win, &val);
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+}
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