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

// 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

// 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

// 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

// 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

// 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

# 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

# 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

// 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.