-/* 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. */
/*
- * Modelling the proportional fairness using the Lagrange Optimization
- * Approach. For a detailed description see:
+ * 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)");
+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);
double (*func_fpi_def) (lmm_variable_t, double);
/*
- * Local prototypes to implement the lagrangian optimization with optimal step, also called dichotomy.
+ * Local prototypes to implement the Lagrangian optimization with optimal step, also called dichotomy.
*/
-//solves the proportional fairness using a lagrange optimizition with dichotomy step
+//solves the proportional fairness using a Lagrangian optimization with dichotomy step
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
+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
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)
+static int __check_feasible(xbt_swag_t cnst_list, xbt_swag_t var_list, int warn)
{
- void *_cnst, *_elem, *_var;
- xbt_swag_t elem_list = NULL;
- lmm_element_t elem = NULL;
- lmm_constraint_t cnst = NULL;
- lmm_variable_t var = NULL;
-
- double tmp;
+ 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;
xbt_swag_foreach(_cnst, cnst_list) {
- cnst = (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 = (lmm_element_t)_elem;
+ elem = static_cast<lmm_element_t>(_elem);
var = elem->variable;
- xbt_assert(var->weight > 0);
+ xbt_assert(var->sharing_weight > 0);
tmp += var->value;
}
if (double_positive(tmp - cnst->bound, sg_maxmin_precision)) {
if (warn)
- XBT_WARN
- ("The link (%p) is over-used. Expected less than %f and got %f",
- cnst, cnst->bound, tmp);
+ XBT_WARN ("The link (%p) is over-used. Expected less than %f and got %f", cnst, cnst->bound, tmp);
return 0;
}
- XBT_DEBUG
- ("Checking feasability for constraint (%p): sat = %f, lambda = %f ",
- cnst, tmp - cnst->bound, cnst->lambda);
+ XBT_DEBUG ("Checking feasability for constraint (%p): sat = %f, lambda = %f ", cnst, tmp - cnst->bound,
+ cnst->lambda);
}
xbt_swag_foreach(_var, var_list) {
- var = (lmm_variable_t)_var;
- if (!var->weight)
+ var = static_cast<lmm_variable_t>(_var);
+ if (not var->sharing_weight)
break;
if (var->bound < 0)
continue;
- XBT_DEBUG("Checking feasability for variable (%p): sat = %f mu = %f", var,
- var->value - var->bound, var->mu);
+ XBT_DEBUG("Checking feasability for variable (%p): sat = %f mu = %f", var, var->value - var->bound, var->mu);
if (double_positive(var->value - var->bound, sg_maxmin_precision)) {
if (warn)
- XBT_WARN
- ("The variable (%p) is too large. Expected less than %f and got %f",
- var, var->bound, var->value);
+ XBT_WARN ("The variable (%p) is too large. Expected less than %f and got %f", var, var->bound, var->value);
return 0;
}
}
static double new_value(lmm_variable_t var)
{
double tmp = 0;
- int i;
- for (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;
- int j;
- for (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)
static double dual_objective(xbt_swag_t var_list, xbt_swag_t cnst_list)
{
- void *_cnst, *_var;
- lmm_constraint_t cnst = NULL;
- lmm_variable_t var = NULL;
+ void *_cnst;
+ void *_var;
+ lmm_constraint_t cnst = nullptr;
+ lmm_variable_t var = nullptr;
double obj = 0.0;
xbt_swag_foreach(_var, var_list) {
- var = (lmm_variable_t)_var;
+ var = static_cast<lmm_variable_t>(_var);
double sigma_i = 0.0;
- int j;
- if (!var->weight)
+ if (not var->sharing_weight)
break;
- for (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;
XBT_DEBUG("var %p : sigma_i = %1.20f", var, sigma_i);
- obj += var->func_f(var, var->func_fpi(var, sigma_i)) -
- sigma_i * var->func_fpi(var, sigma_i);
+ obj += var->func_f(var, var->func_fpi(var, sigma_i)) - sigma_i * var->func_fpi(var, sigma_i);
if (var->bound > 0)
obj += var->mu * var->bound;
}
xbt_swag_foreach(_cnst, cnst_list) {
- cnst = (lmm_constraint_t)_cnst;
- obj += cnst->lambda * cnst->bound;
+ cnst = static_cast<lmm_constraint_t>(_cnst);
+ obj += cnst->lambda * cnst->bound;
}
return obj;
void lagrange_solve(lmm_system_t sys)
{
- /*
- * Lagrange Variables.
- */
+ /* Lagrange Variables. */
int max_iterations = 100;
double epsilon_min_error = 0.00001; /* this is the precision on the objective function so it's none of the configurable values and this value is the legacy one */
double dichotomy_min_error = 1e-14;
double overall_modification = 1;
- /*
- * Variables to manipulate the data structure proposed to model the maxmin
- * fairness. See docummentation for more details.
- */
- xbt_swag_t cnst_list = NULL;
- void *_cnst;
- lmm_constraint_t cnst = NULL;
-
- xbt_swag_t var_list = NULL;
- void *_var;
- lmm_variable_t var = NULL;
-
- /*
- * Auxiliary variables.
- */
- int iteration = 0;
- double tmp = 0;
- int i;
- double obj, 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);
+ 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 (!(sys->modified))
+ if (not sys->modified)
return;
-
- /*
- * Initialize lambda.
- */
- cnst_list = &(sys->active_constraint_set);
+ /* Initialize lambda. */
+ 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 = (lmm_variable_t)_var;
- if (!var->weight)
+ lmm_variable_t 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;
- var->value = new_value(var);
+ XBT_DEBUG("#### NOTE var(%p) is a boundless variable", var);
+ var->mu = -1.0;
} else {
- var->mu = 1.0;
+ var->mu = 1.0;
var->new_mu = 2.0;
- var->value = new_value(var);
}
- XBT_DEBUG("#### var(%p) ->weight : %e", var, var->weight);
+ 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->weight);
+ 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].value == 0.0)
- nb++;
- }
- if (nb == var->cnsts_number)
+ 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);
-
- /*
- * While doesn't reach a minimun error or a number maximum of iterations.
- */
- while (overall_modification > epsilon_min_error
- && iteration < max_iterations) {
-/* int dual_updated=0; */
+ /* Compute dual objective. */
+ 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);
XBT_DEBUG("-------------- Gradient Descent ----------");
- /*
- * Improve the value of mu_i
- */
+ /* Improve the value of mu_i */
xbt_swag_foreach(_var, var_list) {
- var = (lmm_variable_t)_var;
- if (!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);
+ 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);
- 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);
+ 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
- */
+ /* Improve the value of lambda_i */
xbt_swag_foreach(_cnst, cnst_list) {
- cnst = (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->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);
- 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);
+ 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;
}
- /*
- * Now computes the values of each variable (\rho) based on
- * the values of \lambda and \mu.
- */
+ /* Now computes the values of each variable (\rho) based on the values of \lambda and \mu. */
XBT_DEBUG("-------------- Check convergence ----------");
overall_modification = 0;
xbt_swag_foreach(_var, var_list) {
- var = (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("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(!dual_updated) { */
-/* XBT_WARN("Could not improve the convergence at iteration %d. Drop it!",iteration); */
-/* break; */
-/* } */
+ XBT_DEBUG("Iteration %d: overall_modification : %f", iteration, overall_modification);
}
__check_feasible(cnst_list, var_list, 1);
XBT_DEBUG("The method converges in %d iterations.", iteration);
}
if (iteration >= max_iterations) {
- XBT_DEBUG
- ("Method reach %d iterations, which is the maximum number of iterations allowed.",
- iteration);
+ XBT_DEBUG ("Method reach %d iterations, which is the maximum number of iterations allowed.", iteration);
}
-/* XBT_INFO("Method converged after %d iterations", iteration); */
if (XBT_LOG_ISENABLED(surf_lagrange, xbt_log_priority_debug)) {
- lmm_print(sys);
+ sys->print();
}
}
/*
- * Returns a double value corresponding to the result of a dichotomy proccess with
- * respect to a given variable/constraint (\mu in the case of a variable or \lambda in
- * case of a constraint) and a initial value init.
+ * Returns a double value corresponding to the result of a dichotomy process with respect to a given
+ * variable/constraint (\mu in the case of a variable or \lambda in case of a constraint) and a initial value init.
*
* @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 min_erro a minimun error tolerated
+ * @param var_cnst a pointer to a variable or constraint
+ * @param min_erro a minimum error tolerated
*
- * @return a double correponding to the result of the dichotomyal process
+ * @return a double corresponding to the result of the dichotomy process
*/
-static double dichotomy(double init, double diff(double, void *),
- void *var_cnst, double min_error)
+static double dichotomy(double init, double diff(double, void *), void *var_cnst, double min_error)
{
- double min, max;
+ double min =init;
+ double max= init;
double overall_error;
double middle;
- double min_diff, max_diff, middle_diff;
+ double middle_diff;
double diff_0 = 0.0;
- min = max = init;
XBT_IN();
- if (init == 0.0) {
- min = max = 0.5;
+ if (fabs(init) < 1e-20) {
+ min = 0.5;
+ max = 0.5;
}
overall_error = 1;
- if ((diff_0 = diff(1e-16, var_cnst)) >= 0) {
+ diff_0 = diff(1e-16, var_cnst);
+ if (diff_0 >= 0) {
XBT_CDEBUG(surf_lagrange_dichotomy, "returning 0.0 (diff = %e)", diff_0);
XBT_OUT();
return 0.0;
}
- min_diff = diff(min, var_cnst);
- max_diff = diff(max, var_cnst);
+ double min_diff = diff(min, var_cnst);
+ double max_diff = diff(max, var_cnst);
while (overall_error > min_error) {
- XBT_CDEBUG(surf_lagrange_dichotomy,
- "[min, max] = [%1.20f, %1.20f] || diffmin, diffmax = %1.20f, %1.20f",
- min, max, min_diff, max_diff);
+ XBT_CDEBUG(surf_lagrange_dichotomy, "[min, max] = [%1.20f, %1.20f] || diffmin, diffmax = %1.20f, %1.20f",
+ min, max, min_diff, max_diff);
if (min_diff > 0 && max_diff > 0) {
if (min == max) {
}
} else if (min_diff < 0 && max_diff > 0) {
middle = (max + min) / 2.0;
- XBT_CDEBUG(surf_lagrange_dichotomy, "Trying (max+min)/2 : %1.20f",
- middle);
-
- if ((min == middle) || (max == middle)) {
- XBT_CWARN(surf_lagrange_dichotomy,
- "Cannot improve the convergence! min=max=middle=%1.20f, diff = %1.20f."
- " Reaching the 'double' limits. Maybe scaling your function would help ([%1.20f,%1.20f]).",
- min, max - min, min_diff, max_diff);
+ XBT_CDEBUG(surf_lagrange_dichotomy, "Trying (max+min)/2 : %1.20f", middle);
+
+ if ((fabs(min - middle) < 1e-20) || (fabs(max - middle) < 1e-20)){
+ XBT_CWARN(surf_lagrange_dichotomy, "Cannot improve the convergence! min=max=middle=%1.20f, diff = %1.20f."
+ " Reaching the 'double' limits. Maybe scaling your function would help ([%1.20f,%1.20f]).",
+ min, max - min, min_diff, max_diff);
break;
}
middle_diff = diff(middle, var_cnst);
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 (min_diff == 0) {
+ } else if (fabs(min_diff) < 1e-20) {
max = min;
overall_error = 0;
-/* SHOW_EXPR(overall_error); */
- } else if (max_diff == 0) {
+ } 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_CWARN(surf_lagrange_dichotomy, "The impossible happened, partial_diff(min) > 0 && partial_diff(max) < 0");
xbt_abort();
} else {
XBT_CWARN(surf_lagrange_dichotomy,
static double partial_diff_lambda(double lambda, void *param_cnst)
{
-
- int j;
- void *_elem;
- xbt_swag_t elem_list = NULL;
- lmm_element_t elem = NULL;
- lmm_variable_t var = NULL;
- lmm_constraint_t cnst = (lmm_constraint_t) param_cnst;
+ 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 = (lmm_element_t)_elem;
- var = elem->variable;
- xbt_assert(var->weight > 0);
- XBT_CDEBUG(surf_lagrange_dichotomy, "Computing sigma_i for var (%p)",
- var);
+ 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
diff += -var->func_fpi(var, sigma_i);
}
-
diff += cnst->bound;
- XBT_CDEBUG(surf_lagrange_dichotomy,
- "d D/d lambda for cnst (%p) at %1.20f = %1.20f", cnst, lambda,
- diff);
+ XBT_CDEBUG(surf_lagrange_dichotomy, "d D/d lambda for cnst (%p) at %1.20f = %1.20f", cnst, lambda, diff);
XBT_OUT();
return diff;
}
/** \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 polimorfism in C pure, enjoy the roots of programming.
+ *
+ * Set default functions to the ones passed as parameters. This is a polymorphism in C pure, enjoy the roots of
+ * programming.
*
*/
-void lmm_set_default_protocol_function(double (*func_f)
-
-
-
-
-
-
- (lmm_variable_t var, double x),
- double (*func_fp) (lmm_variable_t
- var, double x),
- double (*func_fpi) (lmm_variable_t
- var, double x))
+void lmm_set_default_protocol_function(double (*func_f) (lmm_variable_t var, double x),
+ double (*func_fp) (lmm_variable_t var, double x),
+ double (*func_fpi) (lmm_variable_t var, double x))
{
func_f_def = func_f;
func_fp_def = func_fp;
func_fpi_def = func_fpi;
}
-
/**************** Vegas and Reno functions *************************/
-/*
- * NOTE for Reno: all functions consider the network
- * coeficient (alpha) equal to 1.
- */
+/* NOTE for Reno: all functions consider the network coefficient (alpha) equal to 1. */
/*
* For Vegas: $f(x) = \alpha D_f\ln(x)$
* 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);
}
-
/* Implementing new Reno-2
* For Reno-2: $f(x) = U_f(x_f) = \frac{{2}{D_f}}*ln(2+x*D_f)$
* 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)
{
- double res_fpi;
- double tmp;
-
xbt_assert(x > 0.0, "Don't call me with stupid values!");
- tmp = x * var->weight * var->weight;
- res_fpi = tmp * (9.0 * x + 24.0);
+ double tmp = x * var->sharing_weight * var->sharing_weight;
+ double res_fpi = tmp * (9.0 * x + 24.0);
if (res_fpi <= 0.0)
return 0.0;
res_fpi = RENO2_SCALING * (-3.0 * tmp + sqrt(res_fpi)) / (4.0 * tmp);
return res_fpi;
}
+}
+}
