X-Git-Url: http://info.iut-bm.univ-fcomte.fr/pub/gitweb/simgrid.git/blobdiff_plain/6e99c3f212a73ef360ad80d8af6f760023b3cf1e..d4b2fe80fbab6343e6dce68a12eb522a2e0559b8:/src/kernel/lmm/lagrange.cpp

diff --git a/src/kernel/lmm/lagrange.cpp b/src/kernel/lmm/lagrange.cpp
index 500884e764..f9d999d821 100644
--- a/src/kernel/lmm/lagrange.cpp
+++ b/src/kernel/lmm/lagrange.cpp
@@ -29,9 +29,9 @@ 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);
+double (*func_f_def)(const Variable&, double);
+double (*func_fp_def)(const Variable&, double);
+double (*func_fpi_def)(const Variable&, double);
 
 /*
  * Local prototypes to implement the Lagrangian optimization with optimal step, also called dichotomy.
@@ -39,17 +39,16 @@ double (*func_fpi_def)(const s_lmm_variable_t&, double);
 // 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, const s_lmm_constraint_t&), const s_lmm_constraint_t& cnst,
-                        double min_error);
+static double dichotomy(double init, double diff(double, const Constraint&), const Constraint& 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 double partial_diff_lambda(double lambda, const Constraint& cnst);
 
 template <class CnstList, class VarList>
 static int __check_feasible(const CnstList& cnst_list, const VarList& var_list, int warn)
 {
-  for (s_lmm_constraint_t const& cnst : cnst_list) {
+  for (Constraint const& cnst : cnst_list) {
     double tmp = 0;
-    for (s_lmm_element_t const& elem : cnst.enabled_element_set) {
+    for (Element const& elem : cnst.enabled_element_set) {
       lmm_variable_t var = elem.variable;
       xbt_assert(var->sharing_weight > 0);
       tmp += var->value;
@@ -63,7 +62,7 @@ static int __check_feasible(const CnstList& cnst_list, const VarList& var_list,
     XBT_DEBUG("Checking feasability for constraint (%p): sat = %f, lambda = %f ", &cnst, tmp - cnst.bound, cnst.lambda);
   }
 
-  for (s_lmm_variable_t const& var : var_list) {
+  for (Variable const& var : var_list) {
     if (not var.sharing_weight)
       break;
     if (var.bound < 0)
@@ -79,11 +78,11 @@ static int __check_feasible(const CnstList& cnst_list, const VarList& var_list,
   return 1;
 }
 
-static double new_value(const s_lmm_variable_t& var)
+static double 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)
@@ -93,12 +92,12 @@ static double new_value(const s_lmm_variable_t& var)
   return var.func_fpi(var, tmp);
 }
 
-static double new_mu(const s_lmm_variable_t& var)
+static double 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;
@@ -112,13 +111,13 @@ static double dual_objective(const VarList& var_list, const CnstList& cnst_list)
 {
   double obj = 0.0;
 
-  for (s_lmm_variable_t const& var : var_list) {
+  for (Variable const& var : var_list) {
     double sigma_i = 0.0;
 
     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)
@@ -132,7 +131,7 @@ static double dual_objective(const VarList& var_list, const CnstList& cnst_list)
       obj += var.mu * var.bound;
   }
 
-  for (s_lmm_constraint_t const& cnst : cnst_list)
+  for (Constraint const& cnst : cnst_list)
     obj += cnst.lambda * cnst.bound;
 
   return obj;
@@ -161,18 +160,17 @@ void lagrange_solve(lmm_system_t sys)
 
   /* Initialize lambda. */
   auto& cnst_list = sys->active_constraint_set;
-  for (s_lmm_constraint_t& cnst : cnst_list) {
+  for (Constraint& cnst : cnst_list) {
     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 var_list variable with only the active variables. Initialize mu.
    */
   auto& var_list = sys->variable_set;
-  for (s_lmm_variable_t& var : var_list) {
+  for (Variable& var : var_list) {
     if (not var.sharing_weight)
       var.value = 0.0;
     else {
@@ -188,8 +186,8 @@ void lagrange_solve(lmm_system_t sys)
       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; });
+      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;
     }
@@ -206,7 +204,7 @@ void lagrange_solve(lmm_system_t sys)
     XBT_DEBUG("-------------- Gradient Descent ----------");
 
     /* Improve the value of mu_i */
-    for (s_lmm_variable_t& var : var_list) {
+    for (Variable& var : var_list) {
       if (var.sharing_weight && var.bound >= 0) {
         XBT_DEBUG("Working on var (%p)", &var);
         var.new_mu = new_mu(var);
@@ -221,7 +219,7 @@ void lagrange_solve(lmm_system_t sys)
     }
 
     /* Improve the value of lambda_i */
-    for (s_lmm_constraint_t& cnst : cnst_list) {
+    for (Constraint& 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);
@@ -236,7 +234,7 @@ void lagrange_solve(lmm_system_t sys)
     /* Now computes the values of each variable (\rho) based on the values of \lambda and \mu. */
     XBT_DEBUG("-------------- Check convergence ----------");
     overall_modification = 0;
-    for (s_lmm_variable_t& var : var_list) {
+    for (Variable& var : var_list) {
       if (var.sharing_weight <= 0)
         var.value = 0.0;
       else {
@@ -280,8 +278,7 @@ void lagrange_solve(lmm_system_t sys)
  *
  * @return a double corresponding to the result of the dichotomy process
  */
-static double dichotomy(double init, double diff(double, const s_lmm_constraint_t&), const s_lmm_constraint_t& cnst,
-                        double min_error)
+static double dichotomy(double init, double diff(double, const Constraint&), const Constraint& cnst, double min_error)
 {
   double min = init;
   double max = init;
@@ -381,7 +378,7 @@ static double dichotomy(double init, double diff(double, const s_lmm_constraint_
   return ((min + max) / 2.0);
 }
 
-static double partial_diff_lambda(double lambda, const s_lmm_constraint_t& cnst)
+static double partial_diff_lambda(double lambda, const Constraint& cnst)
 {
   double diff           = 0.0;
 
@@ -389,15 +386,15 @@ static double partial_diff_lambda(double lambda, const s_lmm_constraint_t& cnst)
 
   XBT_CDEBUG(surf_lagrange_dichotomy, "Computing diff of cnst (%p)", &cnst);
 
-  for (s_lmm_element_t const& elem : cnst.enabled_element_set) {
-    s_lmm_variable_t& var = *elem.variable;
+  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& elem2 : var.cnsts)
+    for (Element const& elem2 : var.cnsts)
       sigma_i += elem2.constraint->lambda;
 
     // add mu_i if this flow has a RTT constraint associated
@@ -425,9 +422,9 @@ static double partial_diff_lambda(double lambda, const s_lmm_constraint_t& cnst)
  *  programming.
  *
  */
-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 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;
@@ -442,19 +439,19 @@ void lmm_set_default_protocol_function(double (*func_f)(const s_lmm_variable_t&
  * 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);
@@ -465,19 +462,19 @@ double func_vegas_fpi(const s_lmm_variable_t& var, double 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;
 
@@ -496,19 +493,19 @@ double func_reno_fpi(const s_lmm_variable_t& var, double x)
  * 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;