1 /** 2 * lib/minmax.c: windowed min/max tracker 3 * 4 * Kathleen Nichols' algorithm for tracking the minimum (or maximum) 5 * value of a data stream over some fixed time interval. (E.g., 6 * the minimum RTT over the past five minutes.) It uses constant 7 * space and constant time per update yet almost always delivers 8 * the same minimum as an implementation that has to keep all the 9 * data in the window. 10 * 11 * The algorithm keeps track of the best, 2nd best & 3rd best min 12 * values, maintaining an invariant that the measurement time of 13 * the n'th best >= n-1'th best. It also makes sure that the three 14 * values are widely separated in the time window since that bounds 15 * the worse case error when that data is monotonically increasing 16 * over the window. 17 * 18 * Upon getting a new min, we can forget everything earlier because 19 * it has no value - the new min is <= everything else in the window 20 * by definition and it's the most recent. So we restart fresh on 21 * every new min and overwrites 2nd & 3rd choices. The same property 22 * holds for 2nd & 3rd best. 23 */ 24 #include <linux/module.h> 25 #include <linux/win_minmax.h> 26 27 /* As time advances, update the 1st, 2nd, and 3rd choices. */ 28 static u32 minmax_subwin_update(struct minmax *m, u32 win, 29 const struct minmax_sample *val) 30 { 31 u32 dt = val->t - m->s[0].t; 32 33 if (unlikely(dt > win)) { 34 /* 35 * Passed entire window without a new val so make 2nd 36 * choice the new val & 3rd choice the new 2nd choice. 37 * we may have to iterate this since our 2nd choice 38 * may also be outside the window (we checked on entry 39 * that the third choice was in the window). 40 */ 41 m->s[0] = m->s[1]; 42 m->s[1] = m->s[2]; 43 m->s[2] = *val; 44 if (unlikely(val->t - m->s[0].t > win)) { 45 m->s[0] = m->s[1]; 46 m->s[1] = m->s[2]; 47 m->s[2] = *val; 48 } 49 } else if (unlikely(m->s[1].t == m->s[0].t) && dt > win/4) { 50 /* 51 * We've passed a quarter of the window without a new val 52 * so take a 2nd choice from the 2nd quarter of the window. 53 */ 54 m->s[2] = m->s[1] = *val; 55 } else if (unlikely(m->s[2].t == m->s[1].t) && dt > win/2) { 56 /* 57 * We've passed half the window without finding a new val 58 * so take a 3rd choice from the last half of the window 59 */ 60 m->s[2] = *val; 61 } 62 return m->s[0].v; 63 } 64 65 /* Check if new measurement updates the 1st, 2nd or 3rd choice max. */ 66 u32 minmax_running_max(struct minmax *m, u32 win, u32 t, u32 meas) 67 { 68 struct minmax_sample val = { .t = t, .v = meas }; 69 70 if (unlikely(val.v >= m->s[0].v) || /* found new max? */ 71 unlikely(val.t - m->s[2].t > win)) /* nothing left in window? */ 72 return minmax_reset(m, t, meas); /* forget earlier samples */ 73 74 if (unlikely(val.v >= m->s[1].v)) 75 m->s[2] = m->s[1] = val; 76 else if (unlikely(val.v >= m->s[2].v)) 77 m->s[2] = val; 78 79 return minmax_subwin_update(m, win, &val); 80 } 81 EXPORT_SYMBOL(minmax_running_max); 82 83 /* Check if new measurement updates the 1st, 2nd or 3rd choice min. */ 84 u32 minmax_running_min(struct minmax *m, u32 win, u32 t, u32 meas) 85 { 86 struct minmax_sample val = { .t = t, .v = meas }; 87 88 if (unlikely(val.v <= m->s[0].v) || /* found new min? */ 89 unlikely(val.t - m->s[2].t > win)) /* nothing left in window? */ 90 return minmax_reset(m, t, meas); /* forget earlier samples */ 91 92 if (unlikely(val.v <= m->s[1].v)) 93 m->s[2] = m->s[1] = val; 94 else if (unlikely(val.v <= m->s[2].v)) 95 m->s[2] = val; 96 97 return minmax_subwin_update(m, win, &val); 98 } 99