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);
    
    // 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;