Files
chrony/quantiles.c
Miroslav Lichvar d22c8fbcb2 quantiles: add parameter to limit negative step
Add a new parameter to limit the negative value of the step state
variable. It's set as a maximum delay in number of updates before the
actual step applied to the quantile estimate starts growing from the
minimum step when the input value is consistently larger or smaller than
the estimate.

This prevents the algorithm from effectively becoming the slower 1U
variant if the quantile estimate is stable most of the time.

Set it to 100 updates for the NTP delay and 1000 updates for the hwclock
delay. An option could be added later to make it configurable.
2024-11-21 16:00:23 +01:00

236 lines
6.0 KiB
C

/*
chronyd/chronyc - Programs for keeping computer clocks accurate.
**********************************************************************
* Copyright (C) Miroslav Lichvar 2022
*
* This program is free software; you can redistribute it and/or modify
* it under the terms of version 2 of the GNU General Public License as
* published by the Free Software Foundation.
*
* This program is distributed in the hope that it will be useful, but
* WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
* General Public License for more details.
*
* You should have received a copy of the GNU General Public License along
* with this program; if not, write to the Free Software Foundation, Inc.,
* 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
*
**********************************************************************
=======================================================================
Estimation of quantiles using the Frugal-2U streaming algorithm
(https://arxiv.org/pdf/1407.1121v1.pdf)
*/
#include "config.h"
#include "logging.h"
#include "memory.h"
#include "quantiles.h"
#include "regress.h"
#include "util.h"
/* Maximum number of repeated estimates for stabilisation */
#define MAX_REPEAT 64
struct Quantile {
double est;
double step;
int sign;
};
struct QNT_Instance_Record {
struct Quantile *quants;
int n_quants;
int repeat;
int q;
int min_k;
double min_step;
double neg_step_limit;
int n_set;
};
/* ================================================== */
QNT_Instance
QNT_CreateInstance(int min_k, int max_k, int q, int repeat,
int large_step_delay, double min_step)
{
QNT_Instance inst;
long seed;
if (q < 2 || min_k > max_k || min_k < 1 || max_k >= q ||
repeat < 1 || repeat > MAX_REPEAT || min_step <= 0.0 || large_step_delay < 0)
assert(0);
inst = MallocNew(struct QNT_Instance_Record);
inst->n_quants = (max_k - min_k + 1) * repeat;
inst->quants = MallocArray(struct Quantile, inst->n_quants);
inst->repeat = repeat;
inst->q = q;
inst->min_k = min_k;
inst->min_step = min_step;
inst->neg_step_limit = -large_step_delay * min_step;
QNT_Reset(inst);
/* Seed the random number generator, which will not be isolated from
other instances and other random() users */
UTI_GetRandomBytes(&seed, sizeof (seed));
srandom(seed);
return inst;
}
/* ================================================== */
void
QNT_DestroyInstance(QNT_Instance inst)
{
Free(inst->quants);
Free(inst);
}
/* ================================================== */
void
QNT_Reset(QNT_Instance inst)
{
int i;
inst->n_set = 0;
for (i = 0; i < inst->n_quants; i++) {
inst->quants[i].est = 0.0;
inst->quants[i].step = inst->min_step;
inst->quants[i].sign = 1;
}
}
/* ================================================== */
static void
insert_initial_value(QNT_Instance inst, double value)
{
int i, j, r = inst->repeat;
if (inst->n_set * r >= inst->n_quants)
assert(0);
/* Keep the initial estimates repeated and ordered */
for (i = inst->n_set; i > 0 && inst->quants[(i - 1) * r].est > value; i--) {
for (j = 0; j < r; j++)
inst->quants[i * r + j].est = inst->quants[(i - 1) * r].est;
}
for (j = 0; j < r; j++)
inst->quants[i * r + j].est = value;
inst->n_set++;
/* Duplicate the largest value in unset quantiles */
for (i = inst->n_set * r; i < inst->n_quants; i++)
inst->quants[i].est = inst->quants[i - 1].est;
}
/* ================================================== */
static void
update_estimate(struct Quantile *quantile, double value, double p, double rand,
double min_step, double neg_step_limit)
{
if (value >= quantile->est) {
if (rand < (1.0 - p))
return;
quantile->step += quantile->sign > 0 ? min_step : -min_step;
quantile->est += quantile->step > min_step ? quantile->step : min_step;
if (quantile->est > value) {
quantile->step += value - quantile->est;
quantile->est = value + min_step / 4.0;
}
if (quantile->sign < 0 && quantile->step > min_step)
quantile->step = min_step;
quantile->sign = 1;
} else {
if (rand < p)
return;
quantile->step += quantile->sign < 0 ? min_step : -min_step;
quantile->est -= quantile->step > min_step ? quantile->step : min_step;
if (quantile->est < value) {
quantile->step += quantile->est - value;
quantile->est = value - min_step / 4.0;
}
if (quantile->sign > 0 && quantile->step > min_step)
quantile->step = min_step;
quantile->sign = -1;
}
if (quantile->step < neg_step_limit)
quantile->step = neg_step_limit;
}
/* ================================================== */
void
QNT_Accumulate(QNT_Instance inst, double value)
{
double p, rand;
int i;
/* Initialise the estimates with first received values */
if (inst->n_set * inst->repeat < inst->n_quants) {
insert_initial_value(inst, value);
return;
}
for (i = 0; i < inst->n_quants; i++) {
p = (double)(i / inst->repeat + inst->min_k) / inst->q;
rand = (double)random() / ((1U << 31) - 1);
update_estimate(&inst->quants[i], value, p, rand, inst->min_step, inst->neg_step_limit);
}
}
/* ================================================== */
int
QNT_GetMinK(QNT_Instance inst)
{
return inst->min_k;
}
/* ================================================== */
int
QNT_GetMaxK(QNT_Instance inst)
{
return inst->min_k + (inst->n_quants / inst->repeat) - 1;
}
/* ================================================== */
double
QNT_GetMinStep(QNT_Instance inst)
{
return inst->min_step;
}
/* ================================================== */
double
QNT_GetQuantile(QNT_Instance inst, int k)
{
double estimates[MAX_REPEAT];
int i;
if (k < inst->min_k || (k - inst->min_k) * inst->repeat >= inst->n_quants)
assert(0);
for (i = 0; i < inst->repeat; i++)
estimates[i] = inst->quants[(k - inst->min_k) * inst->repeat + i].est;
return RGR_FindMedian(estimates, inst->repeat);
}