* 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 "src/kernel/lmm/maxmin.hpp"
#include "xbt/log.h"
#include "xbt/sysdep.h"
#define RENO2_SCALING 1.0
namespace simgrid {
-namespace surf {
+namespace kernel {
+namespace lmm {
-double (*func_f_def)(lmm_variable_t, double);
-double (*func_fp_def)(lmm_variable_t, double);
-double (*func_fpi_def)(lmm_variable_t, double);
+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);
+static double dichotomy(double init, double diff(double, const s_lmm_constraint_t&), const s_lmm_constraint_t& cnst,
+ double min_error);
+// computes the value of the differential of constraint cnst applied to lambda
+static double partial_diff_lambda(double lambda, const s_lmm_constraint_t& cnst);
-static int __check_feasible(xbt_swag_t cnst_list, xbt_swag_t var_list, int warn)
+template <class CnstList>
+static int __check_feasible(const CnstList& cnst_list, xbt_swag_t var_list, int warn)
{
- void* _cnst;
void* _elem;
void* _var;
- xbt_swag_t elem_list = nullptr;
+ const_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 (s_lmm_constraint_t const& cnst : cnst_list) {
double tmp = 0;
- elem_list = &(cnst->enabled_element_set);
+ elem_list = &cnst.enabled_element_set;
xbt_swag_foreach(_elem, elem_list)
{
elem = static_cast<lmm_element_t>(_elem);
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);
+ 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)
return 1;
}
-static double new_value(lmm_variable_t var)
+static double new_value(const s_lmm_variable_t& var)
{
double tmp = 0;
- for (s_lmm_element_t const& elem : var->cnsts) {
+ 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->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 var.func_fpi(var, tmp);
}
-static double new_mu(lmm_variable_t var)
+static double new_mu(const s_lmm_variable_t& var)
{
double mu_i = 0.0;
double sigma_i = 0.0;
- for (s_lmm_element_t const& elem : var->cnsts) {
+ for (s_lmm_element_t const& elem : var.cnsts) {
sigma_i += elem.constraint->lambda;
}
- mu_i = var->func_fp(var, var->bound) - sigma_i;
+ mu_i = var.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)
+template <class CnstList>
+static double dual_objective(xbt_swag_t var_list, const CnstList& cnst_list)
{
- void* _cnst;
void* _var;
- lmm_constraint_t cnst = nullptr;
lmm_variable_t var = nullptr;
double obj = 0.0;
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 = static_cast<lmm_constraint_t>(_cnst);
- obj += cnst->lambda * cnst->bound;
- }
+ for (s_lmm_constraint_t const& cnst : cnst_list)
+ obj += cnst.lambda * cnst.bound;
return obj;
}
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);
+ auto& cnst_list = sys->active_constraint_set;
+ for (s_lmm_constraint_t& cnst : cnst_list) {
+ cnst.lambda = 1.0;
+ cnst.new_lambda = 2.0;
+ XBT_DEBUG("#### cnst(%p)->lambda : %e", &cnst, cnst.lambda);
}
/*
var->mu = 1.0;
var->new_mu = 2.0;
}
- var->value = new_value(var);
+ 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);
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);
+ 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;
}
/* 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;
+ for (s_lmm_constraint_t& cnst : cnst_list) {
+ 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);
XBT_DEBUG("Improvement for Objective (%g -> %g) : %g", obj, new_obj, obj - new_obj);
if (var->sharing_weight <= 0)
var->value = 0.0;
else {
- double tmp = new_value(var);
+ double tmp = new_value(*var);
overall_modification = std::max(overall_modification, fabs(var->value - tmp));
*
* @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, const s_lmm_constraint_t&), const s_lmm_constraint_t& 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)
+static double partial_diff_lambda(double lambda, const s_lmm_constraint_t& 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);
+ const_xbt_swag_t elem_list = &cnst.enabled_element_set;
void* _elem;
xbt_swag_foreach(_elem, elem_list)
{
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 += -var->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;
}
* 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)(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))
{
func_f_def = func_f;
func_fp_def = func_fp;
* Therefore: $fp(x) = \frac{\alpha D_f}{x}$
* Therefore: $fpi(x) = \frac{\alpha D_f}{x}$
*/
-double func_vegas_f(lmm_variable_t var, double x)
+double func_vegas_f(const s_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->sharing_weight * log(x);
+ return VEGAS_SCALING * var.sharing_weight * log(x);
}
-double func_vegas_fp(lmm_variable_t var, double x)
+double func_vegas_fp(const s_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->sharing_weight / x;
+ return VEGAS_SCALING * var.sharing_weight / x;
}
-double func_vegas_fpi(lmm_variable_t var, double x)
+double func_vegas_fpi(const s_lmm_variable_t& 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);
+ return var.sharing_weight / (x / VEGAS_SCALING);
}
/*
* Therefore: $fp(x) = \frac{3}{3 D_f^2 x^2+2}$
* Therefore: $fpi(x) = \sqrt{\frac{1}{{D_f}^2 x} - \frac{2}{3{D_f}^2}}$
*/
-double func_reno_f(lmm_variable_t var, double x)
+double func_reno_f(const s_lmm_variable_t& var, double x)
{
- xbt_assert(var->sharing_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->sharing_weight * atan(sqrt(3.0 / 2.0) * var->sharing_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)
+double func_reno_fp(const s_lmm_variable_t& var, double x)
{
- return RENO_SCALING * 3.0 / (3.0 * var->sharing_weight * var->sharing_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 func_reno_fpi(const s_lmm_variable_t& var, double x)
{
double res_fpi;
- xbt_assert(var->sharing_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->sharing_weight * var->sharing_weight * (x / RENO_SCALING)) -
- 2.0 / (3.0 * var->sharing_weight * var->sharing_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;
return sqrt(res_fpi);
* Therefore: $fp(x) = 2/(Weight*x + 2)
* Therefore: $fpi(x) = (2*Weight)/x - 4
*/
-double func_reno2_f(lmm_variable_t var, double x)
+double func_reno2_f(const s_lmm_variable_t& 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));
+ 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)
+double func_reno2_fp(const s_lmm_variable_t& var, double x)
{
- return RENO2_SCALING * 3.0 / (var->sharing_weight * x * (2.0 * var->sharing_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 func_reno2_fpi(const s_lmm_variable_t& var, double x)
{
xbt_assert(x > 0.0, "Don't call me with stupid values!");
- double tmp = x * var->sharing_weight * var->sharing_weight;
+ double tmp = x * var.sharing_weight * var.sharing_weight;
double res_fpi = tmp * (9.0 * x + 24.0);
if (res_fpi <= 0.0)
}
}
}
+}
