#include "xbt/mallocator.h"
#include "maxmin_private.h"
#include <stdlib.h>
+#include <math.h>
XBT_LOG_NEW_DEFAULT_SUBCATEGORY(surf_maxmin, surf,
"Logging specific to SURF (maxmin)");
var->weight = weight;
var->bound = bound;
var->value = 0.0;
+ var->df = 0.0;
if(weight) xbt_swag_insert_at_head(var,&(sys->variable_set));
else xbt_swag_insert_at_tail(var,&(sys->variable_set));
XBT_OUT;
}
}
+/** \brief Attribute the value bound to var->bound.
+ *
+ * \param sys the lmm_system_t
+ * \param var the lmm_variable_t
+ * \param bound the new bound to associate with var
+ *
+ * Makes var->bound equal to bound. Whenever this function is called
+ * a change is signed in the system. To
+ * avoid false system changing detection it is a good idea to test
+ * (bound != 0) before calling it.
+ *
+ */
void lmm_update_variable_bound(lmm_system_t sys, lmm_variable_t var,
double bound)
{
var->bound = bound;
}
+/** \brief Add the value delta to var->df (the sum of latencies).
+ *
+ * \param sys the lmm_system_t associated
+ * \param var the lmm_variable_t which need to updated
+ * \param delta the variation of the latency
+ *
+ * Add the value delta to var->df (the sum of latencys associated to the
+ * flow). Whenever this function is called a change is signed in the system. To
+ * avoid false system changing detection it is a good idea to test
+ * (delta != 0) before calling it.
+ *
+ */
+void lmm_update_variable_latency(lmm_system_t sys, lmm_variable_t var,
+ double delta)
+{
+ sys->modified = 1;
+ var->df += delta;
+}
+
void lmm_update_variable_weight(lmm_system_t sys, lmm_variable_t var,
double weight)
{
return xbt_swag_getNext(cnst,(sys->active_constraint_set).offset);
}
+/** \brief Attribute the value bound to var->bound.
+ *
+ * \param func_f default function f associated with the chosen protocol flavor
+ * \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})
+ * \param func_fpip partial differential of the inverse of the partial differential of f (f prime inverse prime, ((f')^{-1})')
+ *
+ * Set default functions to the ones passed as parameters.
+ *
+ */
+void lmm_set_default_protocol_functions(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),
+ double (* func_fpip) (lmm_variable_t var, double x))
+
+{
+ func_f_def = func_f;
+ func_fp_def = func_fp;
+ func_fpi_def = func_fpi;
+ func_fpip_def = func_fpip;
+}
+
+
+/*
+ * NOTE for Reno: all functions consider the network
+ * coeficient (alpha) equal to 1.
+ */
+
+/*
+ * For Reno f: $\alpha_f d_f \log\left(x_f\right)$
+ */
+double func_reno_f(lmm_variable_t var, double x){
+ xbt_assert0(x,"Please report this bug.");
+ return var->df * log(x);
+}
+
+/*
+ * For Reno fp: $\frac{\alpha D_f}{x}$
+ */
+double func_reno_fp(lmm_variable_t var, double x){
+ xbt_assert0(x,"Please report this bug.");
+ return var->df/x;
+}
+
+/*
+ * For Reno fpi: $\frac{\alpha D_f}{x}$
+ */
+double func_reno_fpi(lmm_variable_t var, double x){
+ xbt_assert0(x,"Please report this bug.");
+ return var->df/x;
+}
+
+/*
+ * For Reno fpip: $-\frac{\alpha D_f}{x^2}$
+ */
+double func_reno_fpip(lmm_variable_t var, double x){
+ xbt_assert0(x,"Please report this bug.");
+ return -( var->df/(x*x) ) ;
+}
+
+
+/*
+ * For Vegas f: $\frac{\sqrt{\frac{3}{2}}}{D_f} \arctan\left(\sqrt{\frac{3}{2}}x_f D_f\right)$
+ */
+double func_vegas_f(lmm_variable_t var, double x){
+ xbt_assert0(x,"Please report this bug.");
+ // \sqrt{3/2} = 0.8164965808
+ return (0.8164965808 / var->df) * atan( (0.8164965808 / var->df)*x );
+}
+
+/*
+ * For Vegas fp: $\frac{3{D_f}^2}{3{D_f}^2x^2 + 2}$
+ */
+double func_vegas_fp(lmm_variable_t var, double x){
+ xbt_assert0(x,"Please report this bug.");
+ return (3*var->df*var->df) / (3*var->df*var->df*x*x + 2);
+}
+
+/*
+ * For Vegas fpi: $\sqrt{\frac{1}{x} - \frac{2}{3{D_f}^2}}$
+ */
+double func_vegas_fpi(lmm_variable_t var, double x){
+ double res_fpi;
+ xbt_assert0( (x<0.0) ,"Please report this bug.");
+ xbt_assert0( (var->df<0.0), "Please report this bug.");
+ res_fpi = (1/x) - 2/(3*var->df*var->df);
+ return sqrt(res_fpi);
+}
+
+/*
+ * For Vegas fpip: $-\frac{1}{2x^2\sqrt{\frac{1}{x} - \frac{2}{3{D_f}^2}}}$
+ */
+double func_vegas_fpip(lmm_variable_t var, double x){
+ double res_fpip;
+ xbt_assert0(x,"Please report this bug.");
+ xbt_assert0( (x<0.0), "Please report this bug.");
+ res_fpip = sqrt(1/x - 2/(3*var->df*var->df));
+ return -(1/(2*x*x*res_fpip));
+}
