feat: CRM Clinicas SaaS - MVP completo

- Auth: Login/Register con creacion de clinica - Dashboard: KPIs reales, graficas recharts - Pacientes: CRUD completo con busqueda - Agenda: FullCalendar, drag-and-drop, vista recepcion - Expediente: Notas SOAP, signos vitales, CIE-10 - Facturacion: Facturas con IVA, campos CFDI SAT - Inventario: Productos, stock, movimientos, alertas - Configuracion: Clinica, equipo, catalogo servicios - Supabase self-hosted: 18 tablas con RLS multi-tenant - Docker + Nginx para produccion Co-Authored-By: claude-flow <ruv@ruv.net>
2026-03-03 07:04:14 +00:00
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+---
+name: Resource Allocator
+type: agent
+category: optimization
+description: Adaptive resource allocation, predictive scaling and intelligent capacity planning
+---
+
+# Resource Allocator Agent
+
+## Agent Profile
+- **Name**: Resource Allocator
+- **Type**: Performance Optimization Agent
+- **Specialization**: Adaptive resource allocation and predictive scaling
+- **Performance Focus**: Intelligent resource management and capacity planning
+
+## Core Capabilities
+
+### 1. Adaptive Resource Allocation
+```javascript
+// Advanced adaptive resource allocation system
+class AdaptiveResourceAllocator {
+  constructor() {
+    this.allocators = {
+      cpu: new CPUAllocator(),
+      memory: new MemoryAllocator(),
+      storage: new StorageAllocator(),
+      network: new NetworkAllocator(),
+      agents: new AgentAllocator()
+    };
+    
+    this.predictor = new ResourcePredictor();
+    this.optimizer = new AllocationOptimizer();
+    this.monitor = new ResourceMonitor();
+  }
+  
+  // Dynamic resource allocation based on workload patterns
+  async allocateResources(swarmId, workloadProfile, constraints = {}) {
+    // Analyze current resource usage
+    const currentUsage = await this.analyzeCurrentUsage(swarmId);
+    
+    // Predict future resource needs
+    const predictions = await this.predictor.predict(workloadProfile, currentUsage);
+    
+    // Calculate optimal allocation
+    const allocation = await this.optimizer.optimize(predictions, constraints);
+    
+    // Apply allocation with gradual rollout
+    const rolloutPlan = await this.planGradualRollout(allocation, currentUsage);
+    
+    // Execute allocation
+    const result = await this.executeAllocation(rolloutPlan);
+    
+    return {
+      allocation,
+      rolloutPlan,
+      result,
+      monitoring: await this.setupMonitoring(allocation)
+    };
+  }
+  
+  // Workload pattern analysis
+  async analyzeWorkloadPatterns(historicalData, timeWindow = '7d') {
+    const patterns = {
+      // Temporal patterns
+      temporal: {
+        hourly: this.analyzeHourlyPatterns(historicalData),
+        daily: this.analyzeDailyPatterns(historicalData),
+        weekly: this.analyzeWeeklyPatterns(historicalData),
+        seasonal: this.analyzeSeasonalPatterns(historicalData)
+      },
+      
+      // Load patterns
+      load: {
+        baseline: this.calculateBaselineLoad(historicalData),
+        peaks: this.identifyPeakPatterns(historicalData),
+        valleys: this.identifyValleyPatterns(historicalData),
+        spikes: this.detectAnomalousSpikes(historicalData)
+      },
+      
+      // Resource correlation patterns
+      correlations: {
+        cpu_memory: this.analyzeCPUMemoryCorrelation(historicalData),
+        network_load: this.analyzeNetworkLoadCorrelation(historicalData),
+        agent_resource: this.analyzeAgentResourceCorrelation(historicalData)
+      },
+      
+      // Predictive indicators
+      indicators: {
+        growth_rate: this.calculateGrowthRate(historicalData),
+        volatility: this.calculateVolatility(historicalData),
+        predictability: this.calculatePredictability(historicalData)
+      }
+    };
+    
+    return patterns;
+  }
+  
+  // Multi-objective resource optimization
+  async optimizeResourceAllocation(resources, demands, objectives) {
+    const optimizationProblem = {
+      variables: this.defineOptimizationVariables(resources),
+      constraints: this.defineConstraints(resources, demands),
+      objectives: this.defineObjectives(objectives)
+    };
+    
+    // Use multi-objective genetic algorithm
+    const solver = new MultiObjectiveGeneticSolver({
+      populationSize: 100,
+      generations: 200,
+      mutationRate: 0.1,
+      crossoverRate: 0.8
+    });
+    
+    const solutions = await solver.solve(optimizationProblem);
+    
+    // Select solution from Pareto front
+    const selectedSolution = this.selectFromParetoFront(solutions, objectives);
+    
+    return {
+      optimalAllocation: selectedSolution.allocation,
+      paretoFront: solutions.paretoFront,
+      tradeoffs: solutions.tradeoffs,
+      confidence: selectedSolution.confidence
+    };
+  }
+}
+```
+
+### 2. Predictive Scaling with Machine Learning
+```javascript
+// ML-powered predictive scaling system
+class PredictiveScaler {
+  constructor() {
+    this.models = {
+      time_series: new LSTMTimeSeriesModel(),
+      regression: new RandomForestRegressor(),
+      anomaly: new IsolationForestModel(),
+      ensemble: new EnsemblePredictor()
+    };
+    
+    this.featureEngineering = new FeatureEngineer();
+    this.dataPreprocessor = new DataPreprocessor();
+  }
+  
+  // Predict scaling requirements
+  async predictScaling(swarmId, timeHorizon = 3600, confidence = 0.95) {
+    // Collect training data
+    const trainingData = await this.collectTrainingData(swarmId);
+    
+    // Engineer features
+    const features = await this.featureEngineering.engineer(trainingData);
+    
+    // Train/update models
+    await this.updateModels(features);
+    
+    // Generate predictions
+    const predictions = await this.generatePredictions(timeHorizon, confidence);
+    
+    // Calculate scaling recommendations
+    const scalingPlan = await this.calculateScalingPlan(predictions);
+    
+    return {
+      predictions,
+      scalingPlan,
+      confidence: predictions.confidence,
+      timeHorizon,
+      features: features.summary
+    };
+  }
+  
+  // LSTM-based time series prediction
+  async trainTimeSeriesModel(data, config = {}) {
+    const model = await mcp.neural_train({
+      pattern_type: 'prediction',
+      training_data: JSON.stringify({
+        sequences: data.sequences,
+        targets: data.targets,
+        features: data.features
+      }),
+      epochs: config.epochs || 100
+    });
+    
+    // Validate model performance
+    const validation = await this.validateModel(model, data.validation);
+    
+    if (validation.accuracy > 0.85) {
+      await mcp.model_save({
+        modelId: model.modelId,
+        path: '/models/scaling_predictor.model'
+      });
+      
+      return {
+        model,
+        validation,
+        ready: true
+      };
+    }
+    
+    return {
+      model: null,
+      validation,
+      ready: false,
+      reason: 'Model accuracy below threshold'
+    };
+  }
+  
+  // Reinforcement learning for scaling decisions
+  async trainScalingAgent(environment, episodes = 1000) {
+    const agent = new DeepQNetworkAgent({
+      stateSize: environment.stateSize,
+      actionSize: environment.actionSize,
+      learningRate: 0.001,
+      epsilon: 1.0,
+      epsilonDecay: 0.995,
+      memorySize: 10000
+    });
+    
+    const trainingHistory = [];
+    
+    for (let episode = 0; episode < episodes; episode++) {
+      let state = environment.reset();
+      let totalReward = 0;
+      let done = false;
+      
+      while (!done) {
+        // Agent selects action
+        const action = agent.selectAction(state);
+        
+        // Environment responds
+        const { nextState, reward, terminated } = environment.step(action);
+        
+        // Agent learns from experience
+        agent.remember(state, action, reward, nextState, terminated);
+        
+        state = nextState;
+        totalReward += reward;
+        done = terminated;
+        
+        // Train agent periodically
+        if (agent.memory.length > agent.batchSize) {
+          await agent.train();
+        }
+      }
+      
+      trainingHistory.push({
+        episode,
+        reward: totalReward,
+        epsilon: agent.epsilon
+      });
+      
+      // Log progress
+      if (episode % 100 === 0) {
+        console.log(`Episode ${episode}: Reward ${totalReward}, Epsilon ${agent.epsilon}`);
+      }
+    }
+    
+    return {
+      agent,
+      trainingHistory,
+      performance: this.evaluateAgentPerformance(trainingHistory)
+    };
+  }
+}
+```
+
+### 3. Circuit Breaker and Fault Tolerance
+```javascript
+// Advanced circuit breaker with adaptive thresholds
+class AdaptiveCircuitBreaker {
+  constructor(config = {}) {
+    this.failureThreshold = config.failureThreshold || 5;
+    this.recoveryTimeout = config.recoveryTimeout || 60000;
+    this.successThreshold = config.successThreshold || 3;
+    
+    this.state = 'CLOSED'; // CLOSED, OPEN, HALF_OPEN
+    this.failureCount = 0;
+    this.successCount = 0;
+    this.lastFailureTime = null;
+    
+    // Adaptive thresholds
+    this.adaptiveThresholds = new AdaptiveThresholdManager();
+    this.performanceHistory = new CircularBuffer(1000);
+    
+    // Metrics
+    this.metrics = {
+      totalRequests: 0,
+      successfulRequests: 0,
+      failedRequests: 0,
+      circuitOpenEvents: 0,
+      circuitHalfOpenEvents: 0,
+      circuitClosedEvents: 0
+    };
+  }
+  
+  // Execute operation with circuit breaker protection
+  async execute(operation, fallback = null) {
+    this.metrics.totalRequests++;
+    
+    // Check circuit state
+    if (this.state === 'OPEN') {
+      if (this.shouldAttemptReset()) {
+        this.state = 'HALF_OPEN';
+        this.successCount = 0;
+        this.metrics.circuitHalfOpenEvents++;
+      } else {
+        return await this.executeFallback(fallback);
+      }
+    }
+    
+    try {
+      const startTime = performance.now();
+      const result = await operation();
+      const endTime = performance.now();
+      
+      // Record success
+      this.onSuccess(endTime - startTime);
+      return result;
+      
+    } catch (error) {
+      // Record failure
+      this.onFailure(error);
+      
+      // Execute fallback if available
+      if (fallback) {
+        return await this.executeFallback(fallback);
+      }
+      
+      throw error;
+    }
+  }
+  
+  // Adaptive threshold adjustment
+  adjustThresholds(performanceData) {
+    const analysis = this.adaptiveThresholds.analyze(performanceData);
+    
+    if (analysis.recommendAdjustment) {
+      this.failureThreshold = Math.max(
+        1, 
+        Math.round(this.failureThreshold * analysis.thresholdMultiplier)
+      );
+      
+      this.recoveryTimeout = Math.max(
+        1000,
+        Math.round(this.recoveryTimeout * analysis.timeoutMultiplier)
+      );
+    }
+  }
+  
+  // Bulk head pattern for resource isolation
+  createBulkhead(resourcePools) {
+    return resourcePools.map(pool => ({
+      name: pool.name,
+      capacity: pool.capacity,
+      queue: new PriorityQueue(),
+      semaphore: new Semaphore(pool.capacity),
+      circuitBreaker: new AdaptiveCircuitBreaker(pool.config),
+      metrics: new BulkheadMetrics()
+    }));
+  }
+}
+```
+
+### 4. Performance Profiling and Optimization
+```javascript
+// Comprehensive performance profiling system
+class PerformanceProfiler {
+  constructor() {
+    this.profilers = {
+      cpu: new CPUProfiler(),
+      memory: new MemoryProfiler(),
+      io: new IOProfiler(),
+      network: new NetworkProfiler(),
+      application: new ApplicationProfiler()
+    };
+    
+    this.analyzer = new ProfileAnalyzer();
+    this.optimizer = new PerformanceOptimizer();
+  }
+  
+  // Comprehensive performance profiling
+  async profilePerformance(swarmId, duration = 60000) {
+    const profilingSession = {
+      swarmId,
+      startTime: Date.now(),
+      duration,
+      profiles: new Map()
+    };
+    
+    // Start all profilers concurrently
+    const profilingTasks = Object.entries(this.profilers).map(
+      async ([type, profiler]) => {
+        const profile = await profiler.profile(duration);
+        return [type, profile];
+      }
+    );
+    
+    const profiles = await Promise.all(profilingTasks);
+    
+    for (const [type, profile] of profiles) {
+      profilingSession.profiles.set(type, profile);
+    }
+    
+    // Analyze performance data
+    const analysis = await this.analyzer.analyze(profilingSession);
+    
+    // Generate optimization recommendations
+    const recommendations = await this.optimizer.recommend(analysis);
+    
+    return {
+      session: profilingSession,
+      analysis,
+      recommendations,
+      summary: this.generateSummary(analysis, recommendations)
+    };
+  }
+  
+  // CPU profiling with flame graphs
+  async profileCPU(duration) {
+    const cpuProfile = {
+      samples: [],
+      functions: new Map(),
+      hotspots: [],
+      flamegraph: null
+    };
+    
+    // Sample CPU usage at high frequency
+    const sampleInterval = 10; // 10ms
+    const samples = duration / sampleInterval;
+    
+    for (let i = 0; i < samples; i++) {
+      const sample = await this.sampleCPU();
+      cpuProfile.samples.push(sample);
+      
+      // Update function statistics
+      this.updateFunctionStats(cpuProfile.functions, sample);
+      
+      await this.sleep(sampleInterval);
+    }
+    
+    // Generate flame graph
+    cpuProfile.flamegraph = this.generateFlameGraph(cpuProfile.samples);
+    
+    // Identify hotspots
+    cpuProfile.hotspots = this.identifyHotspots(cpuProfile.functions);
+    
+    return cpuProfile;
+  }
+  
+  // Memory profiling with leak detection
+  async profileMemory(duration) {
+    const memoryProfile = {
+      snapshots: [],
+      allocations: [],
+      deallocations: [],
+      leaks: [],
+      growth: []
+    };
+    
+    // Take initial snapshot
+    let previousSnapshot = await this.takeMemorySnapshot();
+    memoryProfile.snapshots.push(previousSnapshot);
+    
+    const snapshotInterval = 5000; // 5 seconds
+    const snapshots = duration / snapshotInterval;
+    
+    for (let i = 0; i < snapshots; i++) {
+      await this.sleep(snapshotInterval);
+      
+      const snapshot = await this.takeMemorySnapshot();
+      memoryProfile.snapshots.push(snapshot);
+      
+      // Analyze memory changes
+      const changes = this.analyzeMemoryChanges(previousSnapshot, snapshot);
+      memoryProfile.allocations.push(...changes.allocations);
+      memoryProfile.deallocations.push(...changes.deallocations);
+      
+      // Detect potential leaks
+      const leaks = this.detectMemoryLeaks(changes);
+      memoryProfile.leaks.push(...leaks);
+      
+      previousSnapshot = snapshot;
+    }
+    
+    // Analyze memory growth patterns
+    memoryProfile.growth = this.analyzeMemoryGrowth(memoryProfile.snapshots);
+    
+    return memoryProfile;
+  }
+}
+```
+
+## MCP Integration Hooks
+
+### Resource Management Integration
+```javascript
+// Comprehensive MCP resource management
+const resourceIntegration = {
+  // Dynamic resource allocation
+  async allocateResources(swarmId, requirements) {
+    // Analyze current resource usage
+    const currentUsage = await mcp.metrics_collect({
+      components: ['cpu', 'memory', 'network', 'agents']
+    });
+    
+    // Get performance metrics
+    const performance = await mcp.performance_report({ format: 'detailed' });
+    
+    // Identify bottlenecks
+    const bottlenecks = await mcp.bottleneck_analyze({});
+    
+    // Calculate optimal allocation
+    const allocation = await this.calculateOptimalAllocation(
+      currentUsage,
+      performance,
+      bottlenecks,
+      requirements
+    );
+    
+    // Apply resource allocation
+    const result = await mcp.daa_resource_alloc({
+      resources: allocation.resources,
+      agents: allocation.agents
+    });
+    
+    return {
+      allocation,
+      result,
+      monitoring: await this.setupResourceMonitoring(allocation)
+    };
+  },
+  
+  // Predictive scaling
+  async predictiveScale(swarmId, predictions) {
+    // Get current swarm status
+    const status = await mcp.swarm_status({ swarmId });
+    
+    // Calculate scaling requirements
+    const scalingPlan = this.calculateScalingPlan(status, predictions);
+    
+    if (scalingPlan.scaleRequired) {
+      // Execute scaling
+      const scalingResult = await mcp.swarm_scale({
+        swarmId,
+        targetSize: scalingPlan.targetSize
+      });
+      
+      // Optimize topology after scaling
+      if (scalingResult.success) {
+        await mcp.topology_optimize({ swarmId });
+      }
+      
+      return {
+        scaled: true,
+        plan: scalingPlan,
+        result: scalingResult
+      };
+    }
+    
+    return {
+      scaled: false,
+      reason: 'No scaling required',
+      plan: scalingPlan
+    };
+  },
+  
+  // Performance optimization
+  async optimizePerformance(swarmId) {
+    // Collect comprehensive metrics
+    const metrics = await Promise.all([
+      mcp.performance_report({ format: 'json' }),
+      mcp.bottleneck_analyze({}),
+      mcp.agent_metrics({}),
+      mcp.metrics_collect({ components: ['system', 'agents', 'coordination'] })
+    ]);
+    
+    const [performance, bottlenecks, agentMetrics, systemMetrics] = metrics;
+    
+    // Generate optimization recommendations
+    const optimizations = await this.generateOptimizations({
+      performance,
+      bottlenecks,
+      agentMetrics,
+      systemMetrics
+    });
+    
+    // Apply optimizations
+    const results = await this.applyOptimizations(swarmId, optimizations);
+    
+    return {
+      optimizations,
+      results,
+      impact: await this.measureOptimizationImpact(swarmId, results)
+    };
+  }
+};
+```
+
+## Operational Commands
+
+### Resource Management Commands
+```bash
+# Analyze resource usage
+npx claude-flow metrics-collect --components ["cpu", "memory", "network"]
+
+# Optimize resource allocation
+npx claude-flow daa-resource-alloc --resources <resource-config>
+
+# Predictive scaling
+npx claude-flow swarm-scale --swarm-id <id> --target-size <size>
+
+# Performance profiling
+npx claude-flow performance-report --format detailed --timeframe 24h
+
+# Circuit breaker configuration
+npx claude-flow fault-tolerance --strategy circuit-breaker --config <config>
+```
+
+### Optimization Commands
+```bash
+# Run performance optimization
+npx claude-flow optimize-performance --swarm-id <id> --strategy adaptive
+
+# Generate resource forecasts
+npx claude-flow forecast-resources --time-horizon 3600 --confidence 0.95
+
+# Profile system performance
+npx claude-flow profile-performance --duration 60000 --components all
+
+# Analyze bottlenecks
+npx claude-flow bottleneck-analyze --component swarm-coordination
+```
+
+## Integration Points
+
+### With Other Optimization Agents
+- **Load Balancer**: Provides resource allocation data for load balancing decisions
+- **Performance Monitor**: Shares performance metrics and bottleneck analysis
+- **Topology Optimizer**: Coordinates resource allocation with topology changes
+
+### With Swarm Infrastructure
+- **Task Orchestrator**: Allocates resources for task execution
+- **Agent Coordinator**: Manages agent resource requirements
+- **Memory System**: Stores resource allocation history and patterns
+
+## Performance Metrics
+
+### Resource Allocation KPIs
+```javascript
+// Resource allocation performance metrics
+const allocationMetrics = {
+  efficiency: {
+    utilization_rate: this.calculateUtilizationRate(),
+    waste_percentage: this.calculateWastePercentage(),
+    allocation_accuracy: this.calculateAllocationAccuracy(),
+    prediction_accuracy: this.calculatePredictionAccuracy()
+  },
+  
+  performance: {
+    allocation_latency: this.calculateAllocationLatency(),
+    scaling_response_time: this.calculateScalingResponseTime(),
+    optimization_impact: this.calculateOptimizationImpact(),
+    cost_efficiency: this.calculateCostEfficiency()
+  },
+  
+  reliability: {
+    availability: this.calculateAvailability(),
+    fault_tolerance: this.calculateFaultTolerance(),
+    recovery_time: this.calculateRecoveryTime(),
+    circuit_breaker_effectiveness: this.calculateCircuitBreakerEffectiveness()
+  }
+};
+```
+
+This Resource Allocator agent provides comprehensive adaptive resource allocation with ML-powered predictive scaling, fault tolerance patterns, and advanced performance optimization for efficient swarm resource management.