-/* Copyright (c) 2007-2017. The SimGrid Team. All rights reserved. */
+/* Copyright (c) 2007-2019. 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. */
* "ssh://username@scm.gforge.inria.fr/svn/memo/people/pvelho/lagrange/ppf.ps".
*/
#include "src/kernel/lmm/maxmin.hpp"
+#include "src/surf/surf_interface.hpp"
#include "xbt/log.h"
#include "xbt/sysdep.h"
#include <algorithm>
-#include <cstdlib>
-#ifndef MATH
#include <cmath>
-#endif
+#include <cstdlib>
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
+static constexpr double VEGAS_SCALING = 1000.0;
+static constexpr double RENO_SCALING = 1.0;
+static constexpr double RENO2_SCALING = 1.0;
namespace simgrid {
namespace kernel {
namespace lmm {
-double (*func_f_def)(const s_lmm_variable_t&, double);
-double (*func_fp_def)(const s_lmm_variable_t&, double);
-double (*func_fpi_def)(const s_lmm_variable_t&, double);
-
-/*
- * Local prototypes to implement the Lagrangian optimization with optimal step, also called dichotomy.
- */
-// 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 partial_diff_lambda(double lambda, void* param_cnst);
+System* make_new_lagrange_system(bool selective_update)
+{
+ return new Lagrange(selective_update);
+}
-static int __check_feasible(xbt_swag_t cnst_list, xbt_swag_t var_list, int warn)
+bool Lagrange::check_feasible(bool warn)
{
- 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 = static_cast<lmm_constraint_t>(_cnst);
+ for (Constraint const& cnst : active_constraint_set) {
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;
+ for (Element const& elem : cnst.enabled_element_set) {
+ Variable* var = elem.variable;
xbt_assert(var->sharing_weight > 0);
tmp += var->value;
}
- if (double_positive(tmp - cnst->bound, sg_maxmin_precision)) {
+ 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);
- return 0;
+ XBT_WARN("The link (%p) is over-used. Expected less than %f and got %f", &cnst, cnst.bound, tmp);
+ return false;
}
- 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 = static_cast<lmm_variable_t>(_var);
- if (not var->sharing_weight)
+ for (Variable const& var : variable_set) {
+ if (not var.sharing_weight)
break;
- if (var->bound < 0)
+ 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 (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);
- return 0;
+ XBT_WARN("The variable (%p) is too large. Expected less than %f and got %f", &var, var.bound, var.value);
+ return false;
}
}
- return 1;
+ return true;
}
-static double new_value(lmm_variable_t var)
+double Lagrange::new_value(const Variable& var)
{
double tmp = 0;
- for (s_lmm_element_t const& elem : var->cnsts) {
+ for (Element 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->sharing_weight);
+ if (var.bound > 0)
+ tmp += var.mu;
+ 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);
+ return func_fpi(var, tmp);
}
-static double new_mu(lmm_variable_t var)
+double Lagrange::new_mu(const Variable& var)
{
double mu_i = 0.0;
double sigma_i = 0.0;
- for (s_lmm_element_t const& elem : var->cnsts) {
+ for (Element const& elem : var.cnsts) {
sigma_i += elem.constraint->lambda;
}
- mu_i = var->func_fp(*var, var->bound) - sigma_i;
+ mu_i = func_fp(var, var.bound) - sigma_i;
if (mu_i < 0.0)
return 0.0;
return mu_i;
}
-static double dual_objective(xbt_swag_t var_list, xbt_swag_t cnst_list)
+double Lagrange::dual_objective()
{
- 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 = static_cast<lmm_variable_t>(_var);
+ for (Variable const& var : variable_set) {
double sigma_i = 0.0;
- if (not var->sharing_weight)
+ if (not var.sharing_weight)
break;
- for (s_lmm_element_t const& elem : var->cnsts)
+ for (Element const& elem : var.cnsts)
sigma_i += elem.constraint->lambda;
- if (var->bound > 0)
- sigma_i += var->mu;
+ if (var.bound > 0)
+ sigma_i += var.mu;
- XBT_DEBUG("var %p : sigma_i = %1.20f", var, sigma_i);
+ 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 += func_f(var, func_fpi(var, sigma_i)) - sigma_i * func_fpi(var, sigma_i);
- if (var->bound > 0)
- obj += var->mu * var->bound;
+ if (var.bound > 0)
+ obj += var.mu * var.bound;
}
- xbt_swag_foreach(_cnst, cnst_list)
- {
- cnst = static_cast<lmm_constraint_t>(_cnst);
- obj += cnst->lambda * cnst->bound;
- }
+ for (Constraint const& cnst : active_constraint_set)
+ obj += cnst.lambda * cnst.bound;
return obj;
}
-void lagrange_solve(lmm_system_t sys)
+// solves the proportional fairness using a Lagrangian optimization with dichotomy step
+void Lagrange::lagrange_solve()
{
/* Lagrange Variables. */
int max_iterations = 100;
XBT_DEBUG("#### Minimum error tolerated (dichotomy) : %e", dichotomy_min_error);
if (XBT_LOG_ISENABLED(surf_lagrange, xbt_log_priority_debug)) {
- sys->print();
+ print();
}
- if (not sys->modified)
+ if (not modified_)
return;
/* Initialize lambda. */
- xbt_swag_t cnst_list = &(sys->active_constraint_set);
- void* _cnst;
- xbt_swag_foreach(_cnst, cnst_list)
- {
- 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);
+ for (Constraint& cnst : active_constraint_set) {
+ 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.
+ * Initialize the active variables. Initialize mu.
*/
- xbt_swag_t var_list = &(sys->variable_set);
- void* _var;
- xbt_swag_foreach(_var, var_list)
- {
- lmm_variable_t var = static_cast<lmm_variable_t>(_var);
- if (not var->sharing_weight)
- var->value = 0.0;
+ for (Variable& var : variable_set) {
+ 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;
+ 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.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);
- 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;
+ 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);
+ auto weighted =
+ std::find_if(begin(var.cnsts), end(var.cnsts), [](Element const& x) { return x.consumption_weight != 0.0; });
+ if (weighted == end(var.cnsts))
+ var.value = 1.0;
}
}
/* Compute dual objective. */
- double obj = dual_objective(var_list, cnst_list);
+ double obj = dual_objective();
/* While doesn't reach a minimum error or a number maximum of iterations. */
int iteration = 0;
XBT_DEBUG("-------------- Gradient Descent ----------");
/* Improve the value of mu_i */
- xbt_swag_foreach(_var, var_list)
- {
- 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;
-
- double new_obj = dual_objective(var_list, cnst_list);
+ for (Variable& var : variable_set) {
+ 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;
+
+ double new_obj = dual_objective();
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)
- {
- 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;
-
- double new_obj = dual_objective(var_list, cnst_list);
+ for (Constraint& cnst : active_constraint_set) {
+ 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;
+
+ double new_obj = dual_objective();
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)
- {
- lmm_variable_t var = static_cast<lmm_variable_t>(_var);
- if (var->sharing_weight <= 0)
- var->value = 0.0;
+ for (Variable& var : variable_set) {
+ if (var.sharing_weight <= 0)
+ var.value = 0.0;
else {
double tmp = new_value(var);
- overall_modification = std::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);
+ var.value = tmp;
+ XBT_DEBUG("New value of var (%p) = %e, overall_modification = %e", &var, var.value, overall_modification);
}
}
XBT_DEBUG("-------------- Check feasability ----------");
- if (not __check_feasible(cnst_list, var_list, 0))
+ if (not check_feasible(false))
overall_modification = 1.0;
XBT_DEBUG("Iteration %d: overall_modification : %f", iteration, overall_modification);
}
- __check_feasible(cnst_list, var_list, 1);
+ check_feasible(true);
if (overall_modification <= epsilon_min_error) {
XBT_DEBUG("The method converges in %d iterations.", iteration);
}
if (XBT_LOG_ISENABLED(surf_lagrange, xbt_log_priority_debug)) {
- sys->print();
+ print();
}
}
/*
* 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.
+ * 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 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 minimum error tolerated
*
* @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)
+double Lagrange::dichotomy(double init, double diff(double, const Constraint&), const Constraint& cnst,
+ double min_error)
{
double min = init;
double max = init;
overall_error = 1;
- diff_0 = diff(1e-16, var_cnst);
+ diff_0 = diff(1e-16, cnst);
if (diff_0 >= 0) {
XBT_CDEBUG(surf_lagrange_dichotomy, "returning 0.0 (diff = %e)", diff_0);
XBT_OUT();
return 0.0;
}
- double min_diff = diff(min, var_cnst);
- double max_diff = diff(max, var_cnst);
+ double min_diff = diff(min, cnst);
+ double max_diff = diff(max, 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,
if (min == max) {
XBT_CDEBUG(surf_lagrange_dichotomy, "Decreasing min");
min = min / 2.0;
- min_diff = diff(min, var_cnst);
+ min_diff = diff(min, cnst);
} else {
XBT_CDEBUG(surf_lagrange_dichotomy, "Decreasing max");
max = min;
if (min == max) {
XBT_CDEBUG(surf_lagrange_dichotomy, "Increasing max");
max = max * 2.0;
- max_diff = diff(max, var_cnst);
+ max_diff = diff(max, cnst);
} else {
XBT_CDEBUG(surf_lagrange_dichotomy, "Increasing min");
min = max;
min, max - min, min_diff, max_diff);
break;
}
- middle_diff = diff(middle, var_cnst);
+ middle_diff = diff(middle, cnst);
if (middle_diff < 0) {
XBT_CDEBUG(surf_lagrange_dichotomy, "Increasing min");
return ((min + max) / 2.0);
}
-static double partial_diff_lambda(double lambda, void* param_cnst)
+double Lagrange::partial_diff_lambda(double lambda, const Constraint& cnst)
{
- lmm_constraint_t cnst = static_cast<lmm_constraint_t>(param_cnst);
double diff = 0.0;
XBT_IN();
- XBT_CDEBUG(surf_lagrange_dichotomy, "Computing diff of cnst (%p)", cnst);
+ 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)
- {
- 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);
+ for (Element const& elem : cnst.enabled_element_set) {
+ Variable& 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
double sigma_i = 0.0;
// Compute sigma_i
- for (s_lmm_element_t const& elem : var->cnsts) {
- sigma_i += elem.constraint->lambda;
- }
+ for (Element const& elem2 : var.cnsts)
+ sigma_i += elem2.constraint->lambda;
// add mu_i if this flow has a RTT constraint associated
- if (var->bound > 0)
- sigma_i += var->mu;
+ if (var.bound > 0)
+ sigma_i += var.mu;
- // replace value of cnst->lambda by the value of parameter lambda
- sigma_i = (sigma_i - cnst->lambda) + lambda;
+ // replace value of cnst.lambda by the value of parameter lambda
+ sigma_i = (sigma_i - cnst.lambda) + lambda;
- diff += -var->func_fpi(*var, sigma_i);
+ diff += -func_fpi(var, sigma_i);
}
- diff += cnst->bound;
+ 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.
+/** @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.
+ * @param func_f function (f)
+ * @param func_fp partial differential of f (f prime, (f'))
+ * @param func_fpi inverse of the partial differential of f (f prime inverse, (f')^{-1})
*
+ * Set default functions to the ones passed as parameters.
*/
-void lmm_set_default_protocol_function(double (*func_f)(const s_lmm_variable_t& var, double x),
- double (*func_fp)(const s_lmm_variable_t& var, double x),
- double (*func_fpi)(const s_lmm_variable_t& var, double x))
+void Lagrange::set_default_protocol_function(double (*func_f)(const Variable& var, double x),
+ double (*func_fp)(const Variable& var, double x),
+ double (*func_fpi)(const Variable& var, double x))
{
- func_f_def = func_f;
- func_fp_def = func_fp;
- func_fpi_def = func_fpi;
+ Lagrange::func_f = func_f;
+ Lagrange::func_fp = func_fp;
+ Lagrange::func_fpi = func_fpi;
}
+double (*Lagrange::func_f)(const Variable&, double);
+double (*Lagrange::func_fp)(const Variable&, double);
+double (*Lagrange::func_fpi)(const Variable&, double);
+
/**************** Vegas and Reno functions *************************/
/* 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: $fpi(x) = \frac{\alpha D_f}{x}$
+ * For Vegas: $f(x) = @alpha D_f@ln(x)$
+ * Therefore: $fp(x) = @frac{@alpha D_f}{x}$
+ * Therefore: $fpi(x) = @frac{@alpha D_f}{x}$
*/
-double func_vegas_f(const s_lmm_variable_t& var, double x)
+double func_vegas_f(const Variable& var, double x)
{
xbt_assert(x > 0.0, "Don't call me with stupid values! (%1.20f)", x);
return VEGAS_SCALING * var.sharing_weight * log(x);
}
-double func_vegas_fp(const s_lmm_variable_t& var, double x)
+double func_vegas_fp(const Variable& var, double x)
{
xbt_assert(x > 0.0, "Don't call me with stupid values! (%1.20f)", x);
return VEGAS_SCALING * var.sharing_weight / x;
}
-double func_vegas_fpi(const s_lmm_variable_t& var, double x)
+double func_vegas_fpi(const Variable& var, double x)
{
xbt_assert(x > 0.0, "Don't call me with stupid values! (%1.20f)", x);
return var.sharing_weight / (x / VEGAS_SCALING);
}
/*
- * For Reno: $f(x) = \frac{\sqrt{3/2}}{D_f} atan(\sqrt{3/2}D_f x)$
- * Therefore: $fp(x) = \frac{3}{3 D_f^2 x^2+2}$
- * Therefore: $fpi(x) = \sqrt{\frac{1}{{D_f}^2 x} - \frac{2}{3{D_f}^2}}$
+ * For Reno: $f(x) = @frac{@sqrt{3/2}}{D_f} atan(@sqrt{3/2}D_f x)$
+ * Therefore: $fp(x) = @frac{3}{3 D_f^2 x^2+2}$
+ * Therefore: $fpi(x) = @sqrt{@frac{1}{{D_f}^2 x} - @frac{2}{3{D_f}^2}}$
*/
-double func_reno_f(const s_lmm_variable_t& var, double x)
+double func_reno_f(const Variable& var, double x)
{
xbt_assert(var.sharing_weight > 0.0, "Don't call me with stupid values!");
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(const s_lmm_variable_t& var, double x)
+double func_reno_fp(const Variable& var, double x)
{
return RENO_SCALING * 3.0 / (3.0 * var.sharing_weight * var.sharing_weight * x * x + 2.0);
}
-double func_reno_fpi(const s_lmm_variable_t& var, double x)
+double func_reno_fpi(const Variable& var, double x)
{
double res_fpi;
}
/* Implementing new Reno-2
- * For Reno-2: $f(x) = U_f(x_f) = \frac{{2}{D_f}}*ln(2+x*D_f)$
+ * 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
*/
-double func_reno2_f(const s_lmm_variable_t& var, double x)
+double func_reno2_f(const Variable& var, double x)
{
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(const s_lmm_variable_t& var, double x)
+double func_reno2_fp(const Variable& var, double x)
{
return RENO2_SCALING * 3.0 / (var.sharing_weight * x * (2.0 * var.sharing_weight * x + 3.0));
}
-double func_reno2_fpi(const s_lmm_variable_t& var, double x)
+double func_reno2_fpi(const Variable& var, double x)
{
xbt_assert(x > 0.0, "Don't call me with stupid values!");
double tmp = x * var.sharing_weight * var.sharing_weight;
