agent-resource-allocator

name	agent-resource-allocator
description	Agent skill for resource-allocator - invoke with $agent-resource-allocator

---

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);
    
    
     predictions =  .(timeHorizon, confidence);
    
    
     scalingPlan =  .(predictions);
    
     {
      predictions,
      scalingPlan,
      : predictions.,
      timeHorizon,
      : features.
    };
  }
  
  
   () {
     model =  mcp.({
      : ,
      : .({
        : data.,
        : data.,
        : data.
      }),
      : config. || 
    });
    
    
     validation =  .(model, data.);
    
     (validation. > ) {
       mcp.({
        : model.,
        : 
      });
      
       {
        model,
        validation,
        : 
      };
    }
    
     {
      : ,
      validation,
      : ,
      : 
    };
  }
  
  
   () {
     agent =  ({
      : environment.,
      : environment.,
      : ,
      : ,
      : ,
      : 
    });
    
     trainingHistory = [];
    
     ( episode = ; episode < episodes; episode++) {
       state = environment.();
       totalReward = ;
       done = ;
      
       (!done) {
        
         action = agent.(state);
        
        
         { nextState, reward, terminated } = environment.(action);
        
        
        agent.(state, action, reward, nextState, terminated);
        
        state = nextState;
        totalReward += reward;
        done = terminated;
        
        
         (agent.. > agent.) {
           agent.();
        }
      }
      
      trainingHistory.({
        episode,
        : totalReward,
        : agent.
      });
      
      
       (episode %  === ) {
        .();
      }
    }
    
     {
      agent,
      trainingHistory,
      : .(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,
      : ,
      : ,
      : 
    };
  }
  
  
   () {
    ..++;
    
    
     (. === ) {
       (.()) {
        . = ;
        . = ;
        ..++;
      }  {
          .(fallback);
      }
    }
    
     {
       startTime = performance.();
       result =  ();
       endTime = performance.();
      
      
      .(endTime - startTime);
       result;
      
    }  (error) {
      
      .(error);
      
      
       (fallback) {
          .(fallback);
      }
      
       error;
    }
  }
  
  
  () {
     analysis = ..(performanceData);
    
     (analysis.) {
      . = .(
        , 
        .(. * analysis.)
      );
      
      . = .(
        ,
        .(. * analysis.)
      );
    }
  }
  
  
  () {
     resourcePools.( ({
      : pool.,
      : pool.,
      :  (),
      :  (pool.),
      :  (pool.),
      :  ()
    }));
  }
}

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(
       ([type, profiler]) => {
         profile =  profiler.(duration);
         [type, profile];
      }
    );
    
     profiles =  .(profilingTasks);
    
     ( [type, profile]  profiles) {
      profilingSession..(type, profile);
    }
    
    
     analysis =  ..(profilingSession);
    
    
     recommendations =  ..(analysis);
    
     {
      : profilingSession,
      analysis,
      recommendations,
      : .(analysis, recommendations)
    };
  }
  
  
   () {
     cpuProfile = {
      : [],
      :  (),
      : [],
      : 
    };
    
    
     sampleInterval = ; 
     samples = duration / sampleInterval;
    
     ( i = ; i < samples; i++) {
       sample =  .();
      cpuProfile..(sample);
      
      
      .(cpuProfile., sample);
      
       .(sampleInterval);
    }
    
    
    cpuProfile. = .(cpuProfile.);
    
    
    cpuProfile. = .(cpuProfile.);
    
     cpuProfile;
  }
  
  
   () {
     memoryProfile = {
      : [],
      : [],
      : [],
      : [],
      : []
    };
    
    
     previousSnapshot =  .();
    memoryProfile..(previousSnapshot);
    
     snapshotInterval = ; 
     snapshots = duration / snapshotInterval;
    
     ( i = ; i < snapshots; i++) {
       .(snapshotInterval);
      
       snapshot =  .();
      memoryProfile..(snapshot);
      
      
       changes = .(previousSnapshot, snapshot);
      memoryProfile..(...changes.);
      memoryProfile..(...changes.);
      
      
       leaks = .(changes);
      memoryProfile..(...leaks);
      
      previousSnapshot = snapshot;
    }
    
    
    memoryProfile. = .(memoryProfile.);
    
     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
   () {
    
     status =  mcp.({ swarmId });
    
    
     scalingPlan = .(status, predictions);
    
     (scalingPlan.) {
      
       scalingResult =  mcp.({
        swarmId,
        : scalingPlan.
      });
      
      
       (scalingResult.) {
         mcp.({ swarmId });
      }
      
       {
        : ,
        : scalingPlan,
        : scalingResult
      };
    }
    
     {
      : ,
      : ,
      : scalingPlan
    };
  },
  
  
   () {
    
     metrics =  .([
      mcp.({ :  }),
      mcp.({}),
      mcp.({}),
      mcp.({ : [, , ] })
    ]);
    
     [performance, bottlenecks, agentMetrics, systemMetrics] = metrics;
    
    
     optimizations =  .({
      performance,
      bottlenecks,
      agentMetrics,
      systemMetrics
    });
    
    
     results =  .(swarmId, optimizations);
    
     {
      optimizations,
      results,
      :  .(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.