* 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 <algorithm>
#include <cstdlib>
#ifndef MATH
#include <cmath>
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);
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;
{
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;
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)
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.
* 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->sharing_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->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);
- for (i = 0; i < var->cnsts_number; i++) {
- if (var->cnsts[i].consumption_weight == 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->sharing_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);
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);
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);
+ 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);
}
if (XBT_LOG_ISENABLED(surf_lagrange, xbt_log_priority_debug)) {
- lmm_print(sys);
+ sys->print();
}
}
double sigma_i = 0.0;
// Compute sigma_i
- 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;
}
//add mu_i if this flow has a RTT constraint associated
* 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);
* 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->sharing_weight > 0.0, "Don't call me with stupid values!");
* 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->sharing_weight > 0.0, "Don't call me with stupid values!");
res_fpi = RENO2_SCALING * (-3.0 * tmp + sqrt(res_fpi)) / (4.0 * tmp);
return res_fpi;
}
+}
+}
