runAnalyzer.m

function runtime = runAnalyzer(self, options)
% RUNTIME = RUN()
% Run the solver
 
T0=tic;
iter = NaN;
if nargin<2
    options = self.getOptions;
end
sn = getStruct(self); % this gets modified later on so pass by copy
 
% Convert non-Markovian distributions to PH
sn = sn_nonmarkov_toph(sn, options);
 
orig_method = options.method;
self.runAnalyzerChecks(options);
Solver.resetRandomGeneratorSeed(options.seed);
 
% Show library attribution if verbose and not yet shown
if options.verbose ~= VerboseLevel.SILENT && ~GlobalConstants.isLibraryAttributionShown()
    libs = SolverFLD.getLibrariesUsed(sn, options);
    if ~isempty(libs)
        line_printf('The solver will leverage %s.\n', strjoin(libs, ', '));
        GlobalConstants.setLibraryAttributionShown(true);
    end
end
 
%options.lang = 'java';
 
hasOpenClasses = sn_has_open_classes(sn);
switch options.lang
    case {'java'}
        line_debug(options, 'FLD: using lang=java, delegating to JLINE');
        jmodel = LINE2JLINE(self.model);
        %M = jmodel.getNumberOfStatefulNodes;
        M = jmodel.getNumberOfStations;
        R = jmodel.getNumberOfClasses;
        switch options.method
            case {'default', 'closing', 'matrix', 'rmf'}
                jsolver = JLINE.SolverFluid(jmodel, options);
            otherwise
                line_warning(mfilename,'This solver does not support the specified method. Setting to default.\n');
                options.method = 'default';
                jsolver = JLINE.SolverFluid(jmodel, options);
        end
        [QN,UN,RN,WN,AN,TN] = JLINE.arrayListToResults(jsolver.getAvgTable);
        runtime = toc(T0);
        CN = [];
        XN = [];
        QN = reshape(QN',R,M)';
        UN = reshape(UN',R,M)';
        RN = reshape(RN',R,M)';
        WN = reshape(WN',R,M)';
        AN = reshape(AN',R,M)';
        TN = reshape(TN',R,M)';
        lG = NaN;
        lastiter = NaN;
        self.setAvgResults(QN,UN,RN,TN,AN,WN,CN,XN,runtime,options.method,lastiter);
        self.result.Prob.logNormConstAggr = lG;
        self.result.solverSpecific.sn = JLINE.from_jline_struct(jmodel);
        self.result.solverSpecific.odeStateVec = JLINE.from_jline_matrix(jsolver.result.odeStateVec);
        return
    case 'matlab'
        line_debug(options, 'FLD: using lang=matlab');
        switch options.method
            case {'matrix','pnorm'}
                line_debug(options, 'FLD method: matrix/pnorm requested');
                % Matrix method now supports mixed/open models per Ruuskanen et al., PEVA 151 (2021)
                if sn_has_dps(sn)
                    line_error(mfilename,'The matrix solver does not support DPS scheduling. Use options.method=''closing'' instead.');
                end
            case {'closing'}
                options.method = 'closing';
                line_debug(options, 'FLD method: closing');
            case {'default'}
                % Use rmf for cache models, closing for DPS, matrix otherwise
                if any(sn.nodetype == NodeType.Cache)
                    options.method = 'rmf';
                elseif sn_has_dps(sn)
                    options.method = 'closing';
                else
                    options.method = 'matrix';
                end
                line_debug(options, 'FLD default method resolved to: %s', options.method);
            case {'statedep','softmin'}
                line_debug(options, 'FLD method: %s', options.method);
                % do nothing
            case {'diffusion','fluid.diffusion'}
                line_debug(options, 'FLD method: diffusion approximation');
                % Diffusion approximation - validated in solver_fluid_diffusion
                % do nothing here, validation happens in the solver itself
            case {'mfq','fluid.mfq','butools'}
                line_debug(options, 'FLD method: Markovian fluid queue (BUTools)');
                % Markovian fluid queue method using BUTools for single-queue analysis
                % Validation and fallback happens in solver_fluid_analyzer
            case {'rmf','fluid.rmf'}
                line_debug(options, 'FLD method: refined mean field (cache analysis)');
                % Refined mean field method for multi-list cache analysis
                % Uses DDPP framework with 1/N correction
            otherwise
                line_error(mfilename,sprintf('The ''%s'' method is unsupported by this solver.',options.method));
        end
end
 
if isinf(options.timespan(1))
    if options.verbose  == 2
        line_warning(mfilename,'%s requires options.timespan(1) to be finite. Setting it to 0.\n',mfilename);
    end
    options.timespan(1) = 0;
end
 
if options.timespan(1) == options.timespan(2)
    line_warning(mfilename,'%s: timespan is a single point, unsupported. Setting options.timespace(1) to 0.\n',mfilename);
    options.timespan(1) = 0;
end
 
if self.enableChecks && ~self.supports(self.model)
    line_error(mfilename,'This model contains features not supported by the solver.');
end
 
self.setOptions(options);
 
M = sn.nstations;
K = sn.nclasses;
 
%%
lastSol= [];
Q = zeros(M,K); R = zeros(M,K); T = zeros(M,K);
U = zeros(M,K); C = zeros(1,K); X = zeros(1,K);
Qt=[];
s0 = sn.space;
s0prior = sn.stateprior;
s0_sz = cellfun(@(x) size(x,1), s0)';
s0_id = pprod(s0_sz-1);
cur_state = sn.state;
while s0_id>=0 % for all possible initial states
    s0prior_val = 1;
    for ind=1:sn.nnodes
        if sn.isstateful(ind)
            isf = sn.nodeToStateful(ind);
            s0prior_val = s0prior_val * s0prior{isf}(1+s0_id(isf)); % update prior
            %sn.state{isf} = s0{isf}(1+s0_id(isf),:); % assign initial state to network
            self.model.nodes{ind}.setState(s0{isf}(1+s0_id(isf),:));
        end
    end
    sn = self.model.getStruct;
    sn = sn_nonmarkov_toph(sn, options);  % Re-apply conversion after fresh struct
    if s0prior_val > 0
        %useJLine = false;
        %if useJLine
        %    [Qfull, Ufull, Rfull, Tfull, Cfull, Xfull, t, Qfull_t, Ufull_t, Tfull_t, lastSol] = JLINE.runFluidAnalyzer(self.model, options);
        %else
        [Qfull, Ufull, Rfull, Tfull, Cfull, Xfull, t, Qfull_t, Ufull_t, Tfull_t, lastSol, iter, aoiResults] = solver_fluid_analyzer(sn, options);
        %end
 
        [t,uniqueIdx] = unique(t);
        if isempty(lastSol) % if solution fails
            Q = NaN*ones(M,K); R = NaN*ones(M,K);
            T = NaN*ones(M,K); U = NaN*ones(M,K);
            C = NaN*ones(1,K); X = NaN*ones(1,K);
            Qt = cell(M,K); Ut = cell(M,K); Tt = cell(M,K);
            for ist=1:M
                for r=1:K
                    Qt{ist,r} = [NaN,NaN];
                    Ut{ist,r} = [NaN,NaN];
                    Tt{ist,r} = [NaN,NaN];
                end
            end
        else
            if isempty(self.result) && max(size(Qt))==0 %~exist('Qt','var')
                Q = Qfull*s0prior_val;
                R = Rfull*s0prior_val;
                T = Tfull*s0prior_val;
                U = Ufull*s0prior_val;
                C = Cfull*s0prior_val;
                X = Xfull*s0prior_val;
                Qt = cell(M,K);
                Ut = cell(M,K);
                Tt = cell(M,K);
                for ist=1:M
                    for r=1:K
                        if ~isempty(Qfull_t{ist,r}) && length(Qfull_t{ist,r}) >= max(uniqueIdx)
                            Qfull_t{ist,r} = Qfull_t{ist,r}(uniqueIdx);
                            Ufull_t{ist,r} = Ufull_t{ist,r}(uniqueIdx);
                            Tfull_t{ist,r} = Tfull_t{ist,r}(uniqueIdx);
                            Qt{ist,r} = [Qfull_t{ist,r} * s0prior_val,t];
                            Ut{ist,r} = [Ufull_t{ist,r} * s0prior_val,t];
                            Tt{ist,r} = [Tfull_t{ist,r} * s0prior_val,t];
                        else
                            Qt{ist,r} = [NaN,NaN];
                            Ut{ist,r} = [NaN,NaN];
                            Tt{ist,r} = [NaN,NaN];
                        end
                    end
                end
            else
                Q = Q + Qfull*s0prior_val;
                R = R + Rfull*s0prior_val;
                T = T + Tfull*s0prior_val;
                U = U + Ufull*s0prior_val;
                C = C + Cfull*s0prior_val;
                X = X + Xfull*s0prior_val;
                for ist=1:M
                    for r=1:K
                        [t,uniqueIdx] = unique(t);
                        Qfull_t{ist,r} = Qfull_t{ist,r}(uniqueIdx);
                        Ufull_t{ist,r} = Ufull_t{ist,r}(uniqueIdx);
                        %                                  Tfull_t{i,r} = Tfull_t{i,r}(uniqueIdx);
 
                        tunion = union(Qt{ist,r}(:,2), t);
                        dataOld = interp1(Qt{ist,r}(:,2),Qt{ist,r}(:,1),tunion);
                        dataNew = interp1(t,Qfull_t{ist,r},tunion);
                        Qt{ist,r} = [dataOld + s0prior_val * dataNew, tunion];
 
                        dataOld = interp1(Ut{ist,r}(:,2),Ut{ist,r}(:,1),tunion);
                        dataNew = interp1(t,Ufull_t{ist,r},tunion);
                        Ut{ist,r} = [dataOld + s0prior_val * dataNew, tunion];
 
                        %                                 dataOld = interp1(Tt{i,r}(:,2),Tt{i,r}(:,1),tunion);
                        %                                 dataNew = interp1(t,Tfull_t{i,r},tunion);
                        %                                 Tt{i,r} = [dataOld + s0prior_val * dataNew, tunion];
                    end
                end
            end
        end
    end
    s0_id=pprod(s0_id,s0_sz-1); % update initial state
end
% Now we restore the original state 
for ind=1:sn.nnodes
    if sn.isstateful(ind)
        isf = sn.nodeToStateful(ind);
        self.model.nodes{ind}.setState(cur_state{isf});
    end
end
runtime = toc(T0);
self.result.solverSpecific = lastSol;
% Store AoI results if available
if exist('aoiResults', 'var') && ~isempty(aoiResults)
    self.result.solverSpecific.aoiResults = aoiResults;
end
QN = Q; UN=U; RN=R; TN=T; CN=C; XN=X;
 
% For cache models solved with rmf, update hit/miss probs in the model
if any(strcmp(options.method, {'rmf','fluid.rmf'})) && any(sn.nodetype == NodeType.Cache)
    caches = find(sn.nodetype == NodeType.Cache);
    % Retrieve hitprob/missprob from the cacheqn result stored in lastSol
    if isfield(lastSol, 'cacheHitProb')
        for cIdx = 1:length(caches)
            ind = caches(cIdx);
            self.model.nodes{ind}.setResultHitProb(lastSol.cacheHitProb(cIdx,:));
            self.model.nodes{ind}.setResultMissProb(lastSol.cacheMissProb(cIdx,:));
        end
        self.model.refreshStruct(true);
        sn = self.model.getStruct(true);
    end
end
 
% Compute average arrival rate at steady-state
AN = sn_get_arvr_from_tput(sn, TN, self.getAvgTputHandles());
if strcmp(orig_method,'default') && ~strcmp(options.method,'default')
    self.setAvgResults(QN,UN,RN,TN,AN,[],CN,XN,runtime,['default/',options.method],iter);
else
    self.setAvgResults(QN,UN,RN,TN,AN,[],CN,XN,runtime,options.method,iter);
end
Rt={}; Xt={}; Ct={};
self.setTranAvgResults(Qt,Ut,Rt,Tt,Ct,Xt,runtime);
end