Agent Resource Allocator

// Advanced adaptive resource allocation systemclass 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    };  }}

// ML-powered predictive scaling systemclass 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)    };  }}

// Advanced circuit breaker with adaptive thresholdsclass 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()    }));  }}

// Comprehensive performance profiling systemclass 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;  }}

// Comprehensive MCP resource managementconst 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)    };  }};

// Resource allocation performance metricsconst 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()  }};