1 files changed, 98 insertions, 0 deletions
diff --git a/lib/win_minmax.c b/lib/win_minmax.c
new file mode 100644
index 000000000000..c8420d404926
--- /dev/null
+++ b/lib/win_minmax.c
@@ -0,0 +1,98 @@
+/**
+ * lib/minmax.c: windowed min/max tracker
+ *
+ * Kathleen Nichols' algorithm for tracking the minimum (or maximum)
+ * value of a data stream over some fixed time interval.  (E.g.,
+ * the minimum RTT over the past five minutes.) It uses constant
+ * space and constant time per update yet almost always delivers
+ * the same minimum as an implementation that has to keep all the
+ * data in the window.
+ *
+ * The algorithm keeps track of the best, 2nd best & 3rd best min
+ * values, maintaining an invariant that the measurement time of
+ * the n'th best >= n-1'th best. It also makes sure that the three
+ * values are widely separated in the time window since that bounds
+ * the worse case error when that data is monotonically increasing
+ * over the window.
+ *
+ * Upon getting a new min, we can forget everything earlier because
+ * it has no value - the new min is <= everything else in the window
+ * by definition and it's the most recent. So we restart fresh on
+ * every new min and overwrites 2nd & 3rd choices. The same property
+ * holds for 2nd & 3rd best.
+ */
+#include <linux/module.h>
+#include <linux/win_minmax.h>
+
+/* As time advances, update the 1st, 2nd, and 3rd choices. */
+static u32 minmax_subwin_update(struct minmax *m, u32 win,
+				const struct minmax_sample *val)
+{
+	u32 dt = val->t - m->s[0].t;
+
+	if (unlikely(dt > win)) {
+		/*
+		 * Passed entire window without a new val so make 2nd
+		 * choice the new val & 3rd choice the new 2nd choice.
+		 * we may have to iterate this since our 2nd choice
+		 * may also be outside the window (we checked on entry
+		 * that the third choice was in the window).
+		 */
+		m->s[0] = m->s[1];
+		m->s[1] = m->s[2];
+		m->s[2] = *val;
+		if (unlikely(val->t - m->s[0].t > win)) {
+			m->s[0] = m->s[1];
+			m->s[1] = m->s[2];
+			m->s[2] = *val;
+		}
+	} else if (unlikely(m->s[1].t == m->s[0].t) && dt > win/4) {
+		/*
+		 * We've passed a quarter of the window without a new val
+		 * so take a 2nd choice from the 2nd quarter of the window.
+		 */
+		m->s[2] = m->s[1] = *val;
+	} else if (unlikely(m->s[2].t == m->s[1].t) && dt > win/2) {
+		/*
+		 * We've passed half the window without finding a new val
+		 * so take a 3rd choice from the last half of the window
+		 */
+		m->s[2] = *val;
+	}
+	return m->s[0].v;
+}
+
+/* Check if new measurement updates the 1st, 2nd or 3rd choice max. */
+u32 minmax_running_max(struct minmax *m, u32 win, u32 t, u32 meas)
+{
+	struct minmax_sample val = { .t = t, .v = meas };
+
+	if (unlikely(val.v >= m->s[0].v) ||	  /* found new max? */
+	    unlikely(val.t - m->s[2].t > win))	  /* nothing left in window? */
+		return minmax_reset(m, t, meas);  /* forget earlier samples */
+
+	if (unlikely(val.v >= m->s[1].v))
+		m->s[2] = m->s[1] = val;
+	else if (unlikely(val.v >= m->s[2].v))
+		m->s[2] = val;
+
+	return minmax_subwin_update(m, win, &val);
+}
+EXPORT_SYMBOL(minmax_running_max);
+
+/* Check if new measurement updates the 1st, 2nd or 3rd choice min. */
+u32 minmax_running_min(struct minmax *m, u32 win, u32 t, u32 meas)
+{
+	struct minmax_sample val = { .t = t, .v = meas };
+
+	if (unlikely(val.v <= m->s[0].v) ||	  /* found new min? */
+	    unlikely(val.t - m->s[2].t > win))	  /* nothing left in window? */
+		return minmax_reset(m, t, meas);  /* forget earlier samples */
+
+	if (unlikely(val.v <= m->s[1].v))
+		m->s[2] = m->s[1] = val;
+	else if (unlikely(val.v <= m->s[2].v))
+		m->s[2] = val;
+
+	return minmax_subwin_update(m, win, &val);
+}