| /** |
| * 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); |
| } |