X-Git-Url: http://info.iut-bm.univ-fcomte.fr/pub/gitweb/simgrid.git/blobdiff_plain/def62837cdeac53fb41fc2fcf8e9fb357caa9568..57d65ecd18c591154169428816b1623be700097a:/src/surf/lagrange.c diff --git a/src/surf/lagrange.c b/src/surf/lagrange.c index c70af9e1f9..8530c65d31 100644 --- a/src/surf/lagrange.c +++ b/src/surf/lagrange.c @@ -1,10 +1,7 @@ /* $Id$ */ - /* Copyright (c) 2007 Arnaud Legrand, Pedro Velho. 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: @@ -20,63 +17,110 @@ #include #endif - -XBT_LOG_NEW_DEFAULT_SUBCATEGORY(surf_lagrange, surf, "Logging specific to SURF (lagrange)"); - +XBT_LOG_NEW_DEFAULT_SUBCATEGORY(surf_lagrange, surf, + "Logging specific to SURF (lagrange)"); +XBT_LOG_NEW_SUBCATEGORY(surf_lagrange_dichotomy, surf, + "Logging specific to SURF (lagrange dichotomy)"); /* - * Local prototypes to implement the lagrangian optimization with optimal step, also called dicotomi. + * Local prototypes to implement the lagrangian optimization with optimal step, also called dichotomy. */ -//solves the proportional fairness using a lagrange optimizition with dicotomi step -void lagrange_solve (lmm_system_t sys); -//computes the value of the dicotomi using a initial values, init, with a specific variable or constraint -double dicotomi(double init, double diff(double, void*), void *var_cnst, double min_error); +//solves the proportional fairness using a lagrange optimizition 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 +double dichotomy(double init, double diff(double, void *), void *var_cnst, + double min_error); //computes the value of the differential of variable param_var applied to mu -double partial_diff_mu (double mu, void * param_var); +double partial_diff_mu(double mu, void *param_var); //computes the value of the differential of constraint param_cnst applied to lambda -double partial_diff_lambda (double lambda, void * param_cnst); +double partial_diff_lambda(double lambda, void *param_cnst); +//auxiliar function to compute the partial_diff +double diff_aux(lmm_variable_t var, double x); +static int __check_feasible(xbt_swag_t cnst_list, xbt_swag_t var_list, int warn) +{ + xbt_swag_t elem_list = NULL; + lmm_element_t elem = NULL; + lmm_constraint_t cnst = NULL; + lmm_variable_t var = NULL; + + double tmp; + + xbt_swag_foreach(cnst, cnst_list) { + tmp = 0; + elem_list = &(cnst->element_set); + xbt_swag_foreach(elem, elem_list) { + var = elem->variable; + if (var->weight <= 0) + continue; + tmp += var->value; + } + + if (double_positive(tmp - cnst->bound)) { + if (warn) + WARN3 + ("The link (%p) is over-used. Expected less than %f and got %f", + cnst, cnst->bound, tmp); + return 0; + } + DEBUG3("Checking feasability for constraint (%p): sat = %f, lambda = %f ", + cnst, tmp - cnst->bound, cnst->lambda); + } + + xbt_swag_foreach(var, var_list) { + if (var->bound < 0 || var->weight <= 0) + continue; + DEBUG3("Checking feasability for variable (%p): sat = %f mu = %f", var, + var->value - var->bound, var->mu); + + if (double_positive(var->value - var->bound)) { + if (warn) + WARN3 + ("The variable (%p) is too large. Expected less than %f and got %f", + var, var->bound, var->value); + return 0; + } + } + return 1; +} void lagrange_solve(lmm_system_t sys) { /* * Lagrange Variables. */ - int max_iterations= 10; - double epsilon_min_error = 1e-10; - double overall_error = 1; - + int max_iterations = 100; + double epsilon_min_error = MAXMIN_PRECISION; + double dichotomy_min_error = 1e-18; + double overall_modification = 1; /* * Variables to manipulate the data structure proposed to model the maxmin * fairness. See docummentation for more details. */ - xbt_swag_t elem_list = NULL; - lmm_element_t elem = NULL; - - - xbt_swag_t cnst_list = NULL; + xbt_swag_t cnst_list = NULL; lmm_constraint_t cnst = NULL; - - xbt_swag_t var_list = NULL; - lmm_variable_t var = NULL; + xbt_swag_t var_list = NULL; + lmm_variable_t var = NULL; /* * Auxiliar variables. */ - int iteration=0; - double tmp=0; + int iteration = 0; + double tmp = 0; int i; - - DEBUG0("Iterative method configuration snapshot =====>"); - DEBUG1("#### Maximum number of iterations : %d", max_iterations); - DEBUG1("#### Minimum error tolerated : %e", epsilon_min_error); + DEBUG0("Iterative method configuration snapshot =====>"); + DEBUG1("#### Maximum number of iterations : %d", max_iterations); + DEBUG1("#### Minimum error tolerated : %e", + epsilon_min_error); + DEBUG1("#### Minimum error tolerated (dichotomy) : %e", + dichotomy_min_error); - if ( !(sys->modified)) + if (!(sys->modified)) return; /* @@ -84,137 +128,133 @@ void lagrange_solve(lmm_system_t sys) * Associate an index in the swag variables. Initialize mu. */ var_list = &(sys->variable_set); - i=0; + i = 0; xbt_swag_foreach(var, var_list) { - if((var->bound > 0.0) || (var->weight <= 0.0)){ - DEBUG1("#### NOTE var(%d) is a boundless variable", i); + if ((var->bound < 0.0) || (var->weight <= 0.0)) { + DEBUG1("#### NOTE var(%d) is a boundless (or inactive) variable", i); var->mu = -1.0; - } else{ - var->mu = 1.0; + } else { + var->mu = 1.0; var->new_mu = 2.0; } - DEBUG2("#### var(%d)->mu : %e", i, var->mu); - DEBUG2("#### var(%d)->weight: %e", i, var->weight); + DEBUG3("#### var(%d) %p ->mu : %e", i, var, var->mu); + DEBUG3("#### var(%d) %p ->weight: %e", i, var, var->weight); + DEBUG3("#### var(%d) %p ->bound: %e", i, var, var->bound); i++; } /* * Initialize lambda. */ - cnst_list=&(sys->active_constraint_set); + cnst_list = &(sys->active_constraint_set); xbt_swag_foreach(cnst, cnst_list) { cnst->lambda = 1.0; cnst->new_lambda = 2.0; DEBUG2("#### cnst(%p)->lambda : %e", cnst, cnst->lambda); } - + /* * While doesn't reach a minimun error or a number maximum of iterations. */ - while(overall_error > epsilon_min_error && iteration < max_iterations){ - + while (overall_modification > epsilon_min_error && iteration < max_iterations) { + int dual_updated=0; + iteration++; - + DEBUG1("************** ITERATION %d **************", iteration); + DEBUG0("-------------- Gradient Descent ----------"); /* * Compute the value of mu_i */ - var_list = &(sys->variable_set); //forall mu_i in mu_1, mu_2, ..., mu_n xbt_swag_foreach(var, var_list) { - if((var->bound >= 0) && (var->weight > 0) ){ - var->mu = dicotomi(var->mu, partial_diff_mu, var, epsilon_min_error); - if(var->mu < 0) var->mu = 0; + if ((var->bound >= 0) && (var->weight > 0)) { + DEBUG1("Working on var (%p)", var); + var->new_mu = + dichotomy(var->mu, partial_diff_mu, var, dichotomy_min_error); + dual_updated += (fabs(var->new_mu-var->mu)>dichotomy_min_error); + DEBUG2("dual_updated (%d) : %1.20f",dual_updated,fabs(var->new_mu-var->mu)); + DEBUG3("Updating mu : var->mu (%p) : %1.20f -> %1.20f", var, var->mu, var->new_mu); + var->mu = var->new_mu; } } - /* * Compute the value of lambda_i */ //forall lambda_i in lambda_1, lambda_2, ..., lambda_n xbt_swag_foreach(cnst, cnst_list) { - cnst->lambda = dicotomi(cnst->lambda, partial_diff_lambda, cnst, epsilon_min_error); - if(cnst->lambda < 0) cnst->lambda = 0; + DEBUG1("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); + DEBUG2("dual_updated (%d) : %1.20f",dual_updated,fabs(cnst->new_lambda-cnst->lambda)); + DEBUG3("Updating lambda : cnst->lambda (%p) : %1.20f -> %1.20f", cnst, cnst->lambda, cnst->new_lambda); + cnst->lambda = cnst->new_lambda; } - /* * Now computes the values of each variable (\rho) based on * the values of \lambda and \mu. */ - overall_error=0; - DEBUG1("Iteration %d ", iteration); + DEBUG0("-------------- Check convergence ----------"); + overall_modification = 0; xbt_swag_foreach(var, var_list) { - if(var->weight <=0) + if (var->weight <= 0) var->value = 0.0; else { + //compute sigma_i + mu_i tmp = 0; - for(i=0; icnsts_number; i++){ + for (i = 0; i < var->cnsts_number; i++) { tmp += (var->cnsts[i].constraint)->lambda; - if(var->bound > 0) - tmp+=var->mu; - } - - //computes de overall_error - if(overall_error < fabs(var->value - 1.0/tmp)){ - overall_error = fabs(var->value - 1.0/tmp); } + if (var->bound > 0) + tmp += var->mu; + DEBUG3("\t Working on var (%p). cost = %e; Df = %e", var, tmp, + var->df); - var->value = 1.0 / tmp; - } + //uses the partial differential inverse function + tmp = var->func_fpi(var, tmp); + if (overall_modification < (fabs(var->value - tmp)/tmp)) { + overall_modification = (fabs(var->value - tmp)/tmp); + } - DEBUG2("======> value of var (%p) = %e", var, var->value); + var->value = tmp; + DEBUG3("New value of var (%p) = %e, overall_modification = %e", var, + var->value, overall_modification); + } } - } - - //verify the KKT property for each link - xbt_swag_foreach(cnst, cnst_list){ - tmp = 0; - elem_list = &(cnst->element_set); - xbt_swag_foreach(elem, elem_list) { - var = elem->variable; - if(var->weight<=0) continue; - tmp += var->value; + if (!__check_feasible(cnst_list, var_list, 0)) + overall_modification = 1.0; + DEBUG2("Iteration %d: overall_modification : %f", iteration, overall_modification); + if(!dual_updated) { + WARN1("Could not improve the convergence at iteration %d. Drop it!",iteration); + break; } - - tmp = tmp - cnst->bound; - - - if(tmp != 0 || cnst->lambda != 0){ - WARN4("The link %s(%p) doesn't match the KKT property, value expected (=0) got (lambda=%e) (sum_rho=%e)", (char *)cnst->id, cnst, cnst->lambda, tmp); - } - } - - //verify the KKT property of each flow - xbt_swag_foreach(var, var_list){ - if(var->bound <= 0 || var->weight <= 0) continue; - tmp = 0; - tmp = (var->value - var->bound); - - if(tmp != 0 || var->mu != 0){ - WARN4("The flow %s(%p) doesn't match the KKT property, value expected (=0) got (lambda=%e) (sum_rho=%e)", (char *)var->id, var, var->mu, tmp); - } + __check_feasible(cnst_list, var_list, 1); + if (overall_modification <= epsilon_min_error) { + DEBUG1("The method converges in %d iterations.", iteration); } - - - if(overall_error <= epsilon_min_error){ - DEBUG1("The method converge in %d iterations.", iteration); - }else{ - WARN1("Method reach %d iterations, which is the maxmimun number of iterations allowed.", iteration); + if (iteration >= max_iterations) { + DEBUG1 + ("Method reach %d iterations, which is the maximum number of iterations allowed.", + iteration); } +/* INFO1("Method converged after %d iterations", iteration); */ - - + if (XBT_LOG_ISENABLED(surf_lagrange, xbt_log_priority_debug)) { + lmm_print(sys); + } } /* - * Returns a double value corresponding to the result of a dicotomi proccess with + * 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. * @@ -223,103 +263,247 @@ void lagrange_solve(lmm_system_t sys) * @param var_cnst a pointer to a variable or constraint * @param min_erro a minimun error tolerated * - * @return a double correponding to the result of the dicotomial process + * @return a double correponding to the result of the dichotomyal process */ -double dicotomi(double init, double diff(double, void*), void *var_cnst, double min_error){ +double dichotomy(double init, double diff(double, void *), void *var_cnst, + double min_error) +{ double min, max; double overall_error; double middle; - + double min_diff, max_diff, middle_diff; + double diff_0 = 0.0; min = max = init; + + XBT_IN; + + if (init == 0.0) { + min = max = 0.5; + } + + min_diff = max_diff = middle_diff = 0.0; overall_error = 1; - DEBUG0("STARTING DICOTOMI... Debuggin, format used [min, max], [D(min),D(max)]"); + if ((diff_0 = diff(1e-16, var_cnst)) >= 0) { + CDEBUG1(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); - while(overall_error > min_error){ - DEBUG4("====> [%e, %e] , [%e,%e]", min, max, diff(min, var_cnst), diff(max, var_cnst)); + while (overall_error > min_error) { + CDEBUG4(surf_lagrange_dichotomy, + "[min, max] = [%1.20f, %1.20f] || diffmin, diffmax = %1.20f, %1.20f", min, max, + min_diff,max_diff); - if( diff(min, var_cnst) > 0 && diff(max, var_cnst) > 0 ){ - if(min == max){ + if (min_diff > 0 && max_diff > 0) { + if (min == max) { + CDEBUG0(surf_lagrange_dichotomy, "Decreasing min"); min = min / 2.0; - }else{ + min_diff = diff(min, var_cnst); + } else { + CDEBUG0(surf_lagrange_dichotomy, "Decreasing max"); max = min; + max_diff = min_diff; + } - }else if( diff(min, var_cnst) < 0 && diff(max, var_cnst) < 0 ){ - if(min == max){ + } else if (min_diff < 0 && max_diff < 0) { + if (min == max) { + CDEBUG0(surf_lagrange_dichotomy, "Increasing max"); max = max * 2.0; - }else{ + max_diff = diff(max, var_cnst); + } else { + CDEBUG0(surf_lagrange_dichotomy, "Increasing min"); min = max; + min_diff = max_diff; + } + } else if (min_diff < 0 && max_diff > 0) { + middle = (max + min) / 2.0; + CDEBUG1(surf_lagrange_dichotomy, "Trying (max+min)/2 : %1.20f",middle); + + if((min==middle) || (max==middle)) { + CWARN2(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.", + min, max-min); + break; } - }else if( diff(min, var_cnst) < 0 && diff(max, var_cnst) > 0 ){ - middle = (max + min)/2.0; - - if( diff(middle, var_cnst) < 0 ){ + middle_diff = diff(middle, var_cnst); + + if (middle_diff < 0) { + CDEBUG0(surf_lagrange_dichotomy, "Increasing min"); min = middle; - }else if( diff(middle, var_cnst) > 0 ){ + min_diff = middle_diff; + overall_error = max-middle_diff; + } else if (middle_diff > 0) { + CDEBUG0(surf_lagrange_dichotomy, "Decreasing max"); max = middle; - }else{ - WARN0("Found an optimal solution with 0 error!"); + max_diff = middle_diff; + overall_error = max-middle_diff; + } else { overall_error = 0; } - overall_error = fabs(min - max); - }else{ - WARN0("The impossible happened, partial_diff(min) >0 && partial_diff(max) < 0"); + } else if (min_diff == 0) { + max=min; + overall_error = 0; + } else if (max_diff == 0) { + min=max; + overall_error = 0; + } else if (min_diff > 0 && max_diff < 0) { + CWARN0(surf_lagrange_dichotomy, + "The impossible happened, partial_diff(min) > 0 && partial_diff(max) < 0"); + abort(); + } else { + CWARN2(surf_lagrange_dichotomy, + "diffmin (%1.20f) or diffmax (%1.20f) are something I don't know, taking no action.", + min_diff, max_diff); + abort(); } } - return ((min+max)/2.0); + CDEBUG1(surf_lagrange_dichotomy, "returning %e", (min + max) / 2.0); + XBT_OUT; + return ((min + max) / 2.0); } -double partial_diff_mu(double mu, void *param_var){ - double mu_partial=0.0; - lmm_variable_t var = (lmm_variable_t)param_var; +/* + * + */ +double partial_diff_mu(double mu, void *param_var) +{ + double mu_partial = 0.0; + double sigma_mu = 0.0; + lmm_variable_t var = (lmm_variable_t) param_var; int i; + XBT_IN; + //compute sigma_i + for (i = 0; i < var->cnsts_number; i++) + sigma_mu += (var->cnsts[i].constraint)->lambda; + + //compute sigma_i + mu_i + sigma_mu += mu; + + //use auxiliar function passing (sigma_i + mu_i) + mu_partial = diff_aux(var, sigma_mu); - //for each link with capacity cnsts[i] that uses flow of variable var do - for(i=0; icnsts_number; i++) - mu_partial += (var->cnsts[i].constraint)->lambda + mu; - - mu_partial = (-1.0/mu_partial) + var->bound; + //add the RTT limit + mu_partial += var->bound; + XBT_OUT; return mu_partial; } +/* + * + */ +double partial_diff_lambda(double lambda, void *param_cnst) +{ -double partial_diff_lambda(double lambda, void *param_cnst){ - - double tmp=0.0; int i; 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; - double lambda_partial=0.0; - + lmm_constraint_t cnst = (lmm_constraint_t) param_cnst; + double lambda_partial = 0.0; + double sigma_i = 0.0; + XBT_IN; elem_list = &(cnst->element_set); - + + CDEBUG1(surf_lagrange_dichotomy,"Computting diff of cnst (%p)", cnst); + xbt_swag_foreach(elem, elem_list) { var = elem->variable; - if(var->weight<=0) continue; - - tmp = 0; - for(i=0; icnsts_number; i++){ - tmp += (var->cnsts[i].constraint)->lambda; + if (var->weight <= 0) + continue; + + //initilize de sumation variable + sigma_i = 0.0; + + //compute sigma_i of variable var + for (i = 0; i < var->cnsts_number; i++) { + sigma_i += (var->cnsts[i].constraint)->lambda; } - - if(var->bound > 0) - tmp += var->mu; - - tmp = tmp - cnst->lambda + lambda; - - //avoid a disaster value of lambda - if(tmp==0) lambda_partial = 10e-8; - - lambda_partial += (-1.0 /tmp); + + //add mu_i if this flow has a RTT constraint associated + 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; + + //use the auxiliar function passing (\sigma_i + \mu_i) + lambda_partial += diff_aux(var, sigma_i); } + lambda_partial += cnst->bound; + XBT_OUT; return lambda_partial; } - + + +double diff_aux(lmm_variable_t var, double x) +{ + double tmp_fpi, result; + + XBT_IN2("(var (%p), x (%1.20f))", var, x); + xbt_assert0(var->func_fpi, + "Initialize the protocol functions first create variables before."); + + tmp_fpi = var->func_fpi(var, x); + result = - tmp_fpi; + + XBT_OUT; + return result; +} + +/** \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. + * + */ +void lmm_set_default_protocol_function(double (* func_fpi) (lmm_variable_t var, double x)) +{ + func_fpi_def = func_fpi; +} + + +/**************** Vegas and Reno functions *************************/ +/* + * NOTE for Reno: all functions consider the network + * coeficient (alpha) equal to 1. + */ + +/* + * For Vegas: $f(x) = \alpha D_f\ln(x)$ + * Therefore: $fpi(x) = \frac{\alpha D_f}{x}$ + */ +#define VEGAS_SCALING 1000.0 +double func_vegas_fpi(lmm_variable_t var, double x){ + xbt_assert0(x>0.0,"Don't call me with stupid values!"); + return VEGAS_SCALING*var->df/x; +} + +/* + * For Reno: $f(x) = \frac{\sqrt{3/2}}{D_f} atan(\sqrt{3/2}D_f x)$ + * Therefore: $fpi(x) = \sqrt{\frac{1}{{D_f}^2 x} - \frac{2}{3{D_f}^2}}$ + */ +#define RENO_SCALING 1000.0 +double func_reno_fpi(lmm_variable_t var, double x){ + double res_fpi; + + xbt_assert0(var->df>0.0,"Don't call me with stupid values!"); + xbt_assert0(x>0.0,"Don't call me with stupid values!"); + + res_fpi = 1/(var->df*var->df*x) - 2/(3*var->df*var->df); + if(res_fpi<=0.0) return 0.0; +/* xbt_assert0(res_fpi>0.0,"Don't call me with stupid values!"); */ + return sqrt(RENO_SCALING*res_fpi); +} +