-/* Copyright (c) 2007-2014. The SimGrid Team.
- * All rights reserved. */
+/* Copyright (c) 2007-2017. The SimGrid Team. All rights reserved. */
/* This program is free software; you can redistribute it and/or modify it
* under the terms of the license (GNU LGPL) which comes with this package. */
* Modeling the proportional fairness using the Lagrangian Optimization Approach. For a detailed description see:
* "ssh://username@scm.gforge.inria.fr/svn/memo/people/pvelho/lagrange/ppf.ps".
*/
+#include "surf/maxmin.hpp"
#include "xbt/log.h"
#include "xbt/sysdep.h"
-#include "maxmin_private.hpp"
-#include <stdlib.h>
+#include <algorithm>
+#include <cstdlib>
#ifndef MATH
-#include <math.h>
+#include <cmath>
#endif
XBT_LOG_NEW_DEFAULT_SUBCATEGORY(surf_lagrange, surf, "Logging specific to SURF (lagrange)");
XBT_LOG_NEW_SUBCATEGORY(surf_lagrange_dichotomy, surf_lagrange, "Logging specific to SURF (lagrange dichotomy)");
#define SHOW_EXPR(expr) XBT_CDEBUG(surf_lagrange,#expr " = %g",expr);
+#define VEGAS_SCALING 1000.0
+#define RENO_SCALING 1.0
+#define RENO2_SCALING 1.0
+
+namespace simgrid {
+namespace surf {
double (*func_f_def) (lmm_variable_t, double);
double (*func_fp_def) (lmm_variable_t, double);
void lagrange_solve(lmm_system_t sys);
//computes the value of the dichotomy using a initial values, init, with a specific variable or constraint
static double dichotomy(double init, double diff(double, void *), void *var_cnst, double min_error);
-//computes the value of the differential of constraint param_cnst applied to lambda
+//computes the value of the differential of constraint param_cnst applied to lambda
static double partial_diff_lambda(double lambda, void *param_cnst);
static int __check_feasible(xbt_swag_t cnst_list, xbt_swag_t var_list, int warn)
{
- void *_cnst, *_elem, *_var;
+ void* _cnst;
+ void* _elem;
+ void* _var;
xbt_swag_t elem_list = nullptr;
lmm_element_t elem = nullptr;
lmm_constraint_t cnst = nullptr;
lmm_variable_t var = nullptr;
- double tmp;
-
xbt_swag_foreach(_cnst, cnst_list) {
- cnst = static_cast<lmm_constraint_t>(_cnst);
- tmp = 0;
+ cnst = static_cast<lmm_constraint_t>(_cnst);
+ double tmp = 0;
elem_list = &(cnst->enabled_element_set);
xbt_swag_foreach(_elem, elem_list) {
elem = static_cast<lmm_element_t>(_elem);
var = elem->variable;
- xbt_assert(var->weight > 0);
+ xbt_assert(var->sharing_weight > 0);
tmp += var->value;
}
xbt_swag_foreach(_var, var_list) {
var = static_cast<lmm_variable_t>(_var);
- if (not var->weight)
+ if (not var->sharing_weight)
break;
if (var->bound < 0)
continue;
{
double tmp = 0;
- for (int i = 0; i < var->cnsts_number; i++) {
- tmp += (var->cnsts[i].constraint)->lambda;
+ for (s_lmm_element_t const& elem : var->cnsts) {
+ tmp += elem.constraint->lambda;
}
if (var->bound > 0)
tmp += var->mu;
- XBT_DEBUG("\t Working on var (%p). cost = %e; Weight = %e", var, tmp, var->weight);
+ XBT_DEBUG("\t Working on var (%p). cost = %e; Weight = %e", var, tmp, var->sharing_weight);
//uses the partial differential inverse function
return var->func_fpi(var, tmp);
}
double mu_i = 0.0;
double sigma_i = 0.0;
- for (int j = 0; j < var->cnsts_number; j++) {
- sigma_i += (var->cnsts[j].constraint)->lambda;
+ for (s_lmm_element_t const& elem : var->cnsts) {
+ sigma_i += elem.constraint->lambda;
}
mu_i = var->func_fp(var, var->bound) - sigma_i;
if (mu_i < 0.0)
var = static_cast<lmm_variable_t>(_var);
double sigma_i = 0.0;
- if (not var->weight)
+ if (not var->sharing_weight)
break;
- for (int j = 0; j < var->cnsts_number; j++)
- sigma_i += (var->cnsts[j].constraint)->lambda;
+ for (s_lmm_element_t const& elem : var->cnsts)
+ sigma_i += elem.constraint->lambda;
if (var->bound > 0)
sigma_i += var->mu;
double dichotomy_min_error = 1e-14;
double overall_modification = 1;
- /* Variables to manipulate the data structure proposed to model the maxmin fairness. See documentation for details. */
- xbt_swag_t cnst_list = nullptr;
- void *_cnst;
- lmm_constraint_t cnst = nullptr;
-
- xbt_swag_t var_list = nullptr;
- void *_var;
- lmm_variable_t var = nullptr;
-
- /* Auxiliary variables. */
- int iteration = 0;
- double tmp = 0;
- int i;
- double obj;
- double new_obj;
-
XBT_DEBUG("Iterative method configuration snapshot =====>");
XBT_DEBUG("#### Maximum number of iterations : %d", max_iterations);
XBT_DEBUG("#### Minimum error tolerated : %e", epsilon_min_error);
XBT_DEBUG("#### Minimum error tolerated (dichotomy) : %e", dichotomy_min_error);
if (XBT_LOG_ISENABLED(surf_lagrange, xbt_log_priority_debug)) {
- lmm_print(sys);
+ sys->print();
}
if (not sys->modified)
return;
/* Initialize lambda. */
- cnst_list = &(sys->active_constraint_set);
+ xbt_swag_t cnst_list = &(sys->active_constraint_set);
+ void* _cnst;
xbt_swag_foreach(_cnst, cnst_list) {
- cnst = (lmm_constraint_t)_cnst;
+ lmm_constraint_t cnst = (lmm_constraint_t)_cnst;
cnst->lambda = 1.0;
cnst->new_lambda = 2.0;
XBT_DEBUG("#### cnst(%p)->lambda : %e", cnst, cnst->lambda);
}
- /*
- * Initialize the var list variable with only the active variables.
+ /*
+ * Initialize the var list variable with only the active variables.
* Associate an index in the swag variables. Initialize mu.
*/
- var_list = &(sys->variable_set);
- i = 0;
+ xbt_swag_t var_list = &(sys->variable_set);
+ void* _var;
xbt_swag_foreach(_var, var_list) {
- var = static_cast<lmm_variable_t>(_var);
- if (not var->weight)
- var->value = 0.0;
- else {
- int nb = 0;
- if (var->bound < 0.0) {
- XBT_DEBUG("#### NOTE var(%d) is a boundless variable", i);
- var->mu = -1.0;
+ lmm_variable_t var = static_cast<lmm_variable_t>(_var);
+ if (not var->sharing_weight)
+ var->value = 0.0;
+ else {
+ if (var->bound < 0.0) {
+ XBT_DEBUG("#### NOTE var(%p) is a boundless variable", var);
+ var->mu = -1.0;
+ } else {
+ var->mu = 1.0;
+ var->new_mu = 2.0;
+ }
var->value = new_value(var);
- } else {
- var->mu = 1.0;
- var->new_mu = 2.0;
- var->value = new_value(var);
- }
- XBT_DEBUG("#### var(%p) ->weight : %e", var, var->weight);
- XBT_DEBUG("#### var(%p) ->mu : %e", var, var->mu);
- XBT_DEBUG("#### var(%p) ->weight: %e", var, var->weight);
- XBT_DEBUG("#### var(%p) ->bound: %e", var, var->bound);
- for (i = 0; i < var->cnsts_number; i++) {
- if (var->cnsts[i].value == 0.0)
- nb++;
- }
- if (nb == var->cnsts_number)
- var->value = 1.0;
+ XBT_DEBUG("#### var(%p) ->weight : %e", var, var->sharing_weight);
+ XBT_DEBUG("#### var(%p) ->mu : %e", var, var->mu);
+ XBT_DEBUG("#### var(%p) ->weight: %e", var, var->sharing_weight);
+ XBT_DEBUG("#### var(%p) ->bound: %e", var, var->bound);
+ auto weighted = std::find_if(begin(var->cnsts), end(var->cnsts),
+ [](s_lmm_element_t const& x) { return x.consumption_weight != 0.0; });
+ if (weighted == end(var->cnsts))
+ var->value = 1.0;
}
}
/* Compute dual objective. */
- obj = dual_objective(var_list, cnst_list);
+ double obj = dual_objective(var_list, cnst_list);
/* While doesn't reach a minimum error or a number maximum of iterations. */
+ int iteration = 0;
while (overall_modification > epsilon_min_error && iteration < max_iterations) {
iteration++;
XBT_DEBUG("************** ITERATION %d **************", iteration);
/* Improve the value of mu_i */
xbt_swag_foreach(_var, var_list) {
- var = static_cast<lmm_variable_t>(_var);
- if (not var->weight)
- break;
- if (var->bound >= 0) {
+ lmm_variable_t var = static_cast<lmm_variable_t>(_var);
+ if (var->sharing_weight && var->bound >= 0) {
XBT_DEBUG("Working on var (%p)", var);
var->new_mu = new_mu(var);
-/* dual_updated += (fabs(var->new_mu-var->mu)>dichotomy_min_error); */
-/* XBT_DEBUG("dual_updated (%d) : %1.20f",dual_updated,fabs(var->new_mu-var->mu)); */
XBT_DEBUG("Updating mu : var->mu (%p) : %1.20f -> %1.20f", var, var->mu, var->new_mu);
var->mu = var->new_mu;
- new_obj = dual_objective(var_list, cnst_list);
+ double new_obj = dual_objective(var_list, cnst_list);
XBT_DEBUG("Improvement for Objective (%g -> %g) : %g", obj, new_obj, obj - new_obj);
xbt_assert(obj - new_obj >= -epsilon_min_error, "Our gradient sucks! (%1.20f)", obj - new_obj);
obj = new_obj;
/* Improve the value of lambda_i */
xbt_swag_foreach(_cnst, cnst_list) {
- cnst = static_cast<lmm_constraint_t>(_cnst);
+ lmm_constraint_t cnst = static_cast<lmm_constraint_t>(_cnst);
XBT_DEBUG("Working on cnst (%p)", cnst);
cnst->new_lambda = dichotomy(cnst->lambda, partial_diff_lambda, cnst, dichotomy_min_error);
-/* dual_updated += (fabs(cnst->new_lambda-cnst->lambda)>dichotomy_min_error); */
-/* XBT_DEBUG("dual_updated (%d) : %1.20f",dual_updated,fabs(cnst->new_lambda-cnst->lambda)); */
XBT_DEBUG("Updating lambda : cnst->lambda (%p) : %1.20f -> %1.20f", cnst, cnst->lambda, cnst->new_lambda);
cnst->lambda = cnst->new_lambda;
- new_obj = dual_objective(var_list, cnst_list);
+ double new_obj = dual_objective(var_list, cnst_list);
XBT_DEBUG("Improvement for Objective (%g -> %g) : %g", obj, new_obj, obj - new_obj);
xbt_assert(obj - new_obj >= -epsilon_min_error, "Our gradient sucks! (%1.20f)", obj - new_obj);
obj = new_obj;
XBT_DEBUG("-------------- Check convergence ----------");
overall_modification = 0;
xbt_swag_foreach(_var, var_list) {
- var = static_cast<lmm_variable_t>(_var);
- if (var->weight <= 0)
+ lmm_variable_t var = static_cast<lmm_variable_t>(_var);
+ if (var->sharing_weight <= 0)
var->value = 0.0;
else {
- tmp = new_value(var);
+ double tmp = new_value(var);
- overall_modification = MAX(overall_modification, fabs(var->value - tmp));
+ overall_modification = std::max(overall_modification, fabs(var->value - tmp));
var->value = tmp;
XBT_DEBUG("New value of var (%p) = %e, overall_modification = %e", var, var->value, overall_modification);
}
XBT_DEBUG("-------------- Check feasability ----------");
- if (!__check_feasible(cnst_list, var_list, 0))
+ if (not __check_feasible(cnst_list, var_list, 0))
overall_modification = 1.0;
XBT_DEBUG("Iteration %d: overall_modification : %f", iteration, overall_modification);
- /* if(not dual_updated) { */
- /* XBT_WARN("Could not improve the convergence at iteration %d. Drop it!",iteration); */
- /* break; */
- /* } */
}
__check_feasible(cnst_list, var_list, 1);
}
if (XBT_LOG_ISENABLED(surf_lagrange, xbt_log_priority_debug)) {
- lmm_print(sys);
+ sys->print();
}
}
*
* @param init initial value for \mu or \lambda
* @param diff a function that computes the differential of with respect a \mu or \lambda
- * @param var_cnst a pointer to a variable or constraint
+ * @param var_cnst a pointer to a variable or constraint
* @param min_erro a minimum error tolerated
*
* @return a double corresponding to the result of the dichotomy process
min = middle;
overall_error = max_diff - middle_diff;
min_diff = middle_diff;
-/* SHOW_EXPR(overall_error); */
} else if (middle_diff > 0) {
XBT_CDEBUG(surf_lagrange_dichotomy, "Decreasing max");
max = middle;
overall_error = max_diff - middle_diff;
max_diff = middle_diff;
-/* SHOW_EXPR(overall_error); */
} else {
overall_error = 0;
-/* SHOW_EXPR(overall_error); */
}
} else if (fabs(min_diff) < 1e-20) {
max = min;
overall_error = 0;
-/* SHOW_EXPR(overall_error); */
} else if (fabs(max_diff) < 1e-20) {
min = max;
overall_error = 0;
-/* SHOW_EXPR(overall_error); */
} else if (min_diff > 0 && max_diff < 0) {
XBT_CWARN(surf_lagrange_dichotomy, "The impossible happened, partial_diff(min) > 0 && partial_diff(max) < 0");
xbt_abort();
static double partial_diff_lambda(double lambda, void *param_cnst)
{
- int j;
- void *_elem;
- xbt_swag_t elem_list = nullptr;
- lmm_element_t elem = nullptr;
- lmm_variable_t var = nullptr;
lmm_constraint_t cnst = static_cast<lmm_constraint_t>(param_cnst);
double diff = 0.0;
- double sigma_i = 0.0;
XBT_IN();
- elem_list = &(cnst->enabled_element_set);
XBT_CDEBUG(surf_lagrange_dichotomy, "Computing diff of cnst (%p)", cnst);
+ xbt_swag_t elem_list = &(cnst->enabled_element_set);
+ void* _elem;
xbt_swag_foreach(_elem, elem_list) {
- elem = static_cast<lmm_element_t>(_elem);
- var = elem->variable;
- xbt_assert(var->weight > 0);
+ lmm_element_t elem = static_cast<lmm_element_t>(_elem);
+ lmm_variable_t var = elem->variable;
+ xbt_assert(var->sharing_weight > 0);
XBT_CDEBUG(surf_lagrange_dichotomy, "Computing sigma_i for var (%p)", var);
// Initialize the summation variable
- sigma_i = 0.0;
+ double sigma_i = 0.0;
- // Compute sigma_i
- for (j = 0; j < var->cnsts_number; j++) {
- sigma_i += (var->cnsts[j].constraint)->lambda;
+ // Compute sigma_i
+ for (s_lmm_element_t const& elem : var->cnsts) {
+ sigma_i += elem.constraint->lambda;
}
//add mu_i if this flow has a RTT constraint associated
}
/** \brief Attribute the value bound to var->bound.
- *
+ *
* \param func_fpi inverse of the partial differential of f (f prime inverse, (f')^{-1})
- *
+ *
* Set default functions to the ones passed as parameters. This is a polymorphism in C pure, enjoy the roots of
* programming.
*
* Therefore: $fp(x) = \frac{\alpha D_f}{x}$
* Therefore: $fpi(x) = \frac{\alpha D_f}{x}$
*/
-#define VEGAS_SCALING 1000.0
-
double func_vegas_f(lmm_variable_t var, double x)
{
xbt_assert(x > 0.0, "Don't call me with stupid values! (%1.20f)", x);
- return VEGAS_SCALING * var->weight * log(x);
+ return VEGAS_SCALING * var->sharing_weight * log(x);
}
double func_vegas_fp(lmm_variable_t var, double x)
{
xbt_assert(x > 0.0, "Don't call me with stupid values! (%1.20f)", x);
- return VEGAS_SCALING * var->weight / x;
+ return VEGAS_SCALING * var->sharing_weight / x;
}
double func_vegas_fpi(lmm_variable_t var, double x)
{
xbt_assert(x > 0.0, "Don't call me with stupid values! (%1.20f)", x);
- return var->weight / (x / VEGAS_SCALING);
+ return var->sharing_weight / (x / VEGAS_SCALING);
}
/*
* Therefore: $fp(x) = \frac{3}{3 D_f^2 x^2+2}$
* Therefore: $fpi(x) = \sqrt{\frac{1}{{D_f}^2 x} - \frac{2}{3{D_f}^2}}$
*/
-#define RENO_SCALING 1.0
double func_reno_f(lmm_variable_t var, double x)
{
- xbt_assert(var->weight > 0.0, "Don't call me with stupid values!");
+ xbt_assert(var->sharing_weight > 0.0, "Don't call me with stupid values!");
- return RENO_SCALING * sqrt(3.0 / 2.0) / var->weight * atan(sqrt(3.0 / 2.0) * var->weight * x);
+ return RENO_SCALING * sqrt(3.0 / 2.0) / var->sharing_weight * atan(sqrt(3.0 / 2.0) * var->sharing_weight * x);
}
double func_reno_fp(lmm_variable_t var, double x)
{
- return RENO_SCALING * 3.0 / (3.0 * var->weight * var->weight * x * x + 2.0);
+ return RENO_SCALING * 3.0 / (3.0 * var->sharing_weight * var->sharing_weight * x * x + 2.0);
}
double func_reno_fpi(lmm_variable_t var, double x)
{
double res_fpi;
- xbt_assert(var->weight > 0.0, "Don't call me with stupid values!");
+ xbt_assert(var->sharing_weight > 0.0, "Don't call me with stupid values!");
xbt_assert(x > 0.0, "Don't call me with stupid values!");
- res_fpi = 1.0 / (var->weight * var->weight * (x / RENO_SCALING)) - 2.0 / (3.0 * var->weight * var->weight);
+ res_fpi = 1.0 / (var->sharing_weight * var->sharing_weight * (x / RENO_SCALING)) -
+ 2.0 / (3.0 * var->sharing_weight * var->sharing_weight);
if (res_fpi <= 0.0)
return 0.0;
-/* xbt_assert(res_fpi>0.0,"Don't call me with stupid values!"); */
return sqrt(res_fpi);
}
* Therefore: $fp(x) = 2/(Weight*x + 2)
* Therefore: $fpi(x) = (2*Weight)/x - 4
*/
-#define RENO2_SCALING 1.0
double func_reno2_f(lmm_variable_t var, double x)
{
- xbt_assert(var->weight > 0.0, "Don't call me with stupid values!");
- return RENO2_SCALING * (1.0 / var->weight) * log((x * var->weight) / (2.0 * x * var->weight + 3.0));
+ xbt_assert(var->sharing_weight > 0.0, "Don't call me with stupid values!");
+ return RENO2_SCALING * (1.0 / var->sharing_weight) *
+ log((x * var->sharing_weight) / (2.0 * x * var->sharing_weight + 3.0));
}
double func_reno2_fp(lmm_variable_t var, double x)
{
- return RENO2_SCALING * 3.0 / (var->weight * x * (2.0 * var->weight * x + 3.0));
+ return RENO2_SCALING * 3.0 / (var->sharing_weight * x * (2.0 * var->sharing_weight * x + 3.0));
}
double func_reno2_fpi(lmm_variable_t var, double x)
{
xbt_assert(x > 0.0, "Don't call me with stupid values!");
- double tmp = x * var->weight * var->weight;
+ double tmp = x * var->sharing_weight * var->sharing_weight;
double res_fpi = tmp * (9.0 * x + 24.0);
if (res_fpi <= 0.0)
res_fpi = RENO2_SCALING * (-3.0 * tmp + sqrt(res_fpi)) / (4.0 * tmp);
return res_fpi;
}
+}
+}
