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.