blob: c8420d404926d4d7e8a3816a66b06b4257ba9ae8
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 |