agent-performance-optimizer

// Optimize computational resource allocation
class ResourceOptimizer {
  async optimizeAllocation(resources, demands, constraints) {
    // Create resource allocation matrix
    const allocationMatrix = this.buildAllocationMatrix(resources, constraints);

    // Solve optimization problem
    const optimization = await mcp__sublinear-time-solver__solve({
      matrix: allocationMatrix,
      vector: demands,
      method: "neumann",
      epsilon: 1e-8,
      maxIterations: 1000
    });

    return {
      allocation: this.extractAllocation(optimization.solution),
      efficiency: this.calculateEfficiency(optimization),
      utilization: this.calculateUtilization(optimization),
      bottlenecks: this.identifyBottlenecks(optimization)
    };
  }

  async analyzeSystemPerformance(systemMetrics, performanceTargets) {
    // Analyze current system performance
    const analysis = await mcp__sublinear-time-solver__analyzeMatrix({
      matrix: systemMetrics,
      checkDominance: true,
      estimateCondition: true,
      computeGap: true
    });

    return {
      performanceScore: this.calculateScore(analysis),
      recommendations: this.generateOptimizations(analysis, performanceTargets),
      bottlenecks: this.identifyPerformanceBottlenecks(analysis)
    };
  }
}

// Optimize load distribution across compute nodes
async function optimizeLoadBalancing(nodes, workloads, capacities) {
  // Create load balancing matrix
  const loadMatrix = {
    rows: nodes.length,
    cols: workloads.length,
    format: "dense",
    data: createLoadBalancingMatrix(nodes, workloads, capacities)
  };

  // Solve load balancing optimization
  const balancing = await mcp__sublinear-time-solver__solve({
    matrix: loadMatrix,
    vector: workloads,
    method: "random-walk",
    epsilon: 1e-6,
    maxIterations: 500
  });

  return {
    loadDistribution: extractLoadDistribution(balancing.solution),
    balanceScore: calculateBalanceScore(balancing),
    nodeUtilization: calculateNodeUtilization(balancing),
    recommendations: generateLoadBalancingRecommendations(balancing)
  };
}

// Analyze and resolve performance bottlenecks
class BottleneckAnalyzer {
  async analyzeBottlenecks(performanceData, systemTopology) {
    // Estimate critical performance metrics
    const criticalMetrics = await Promise.all(
      performanceData.map(async (metric, index) => {
        return await mcp__sublinear-time-solver__estimateEntry({
          matrix: systemTopology,
          vector: performanceData,
          row: index,
          column: index,
          method: "random-walk",
          epsilon: 1e-6,
          confidence: 0.95
        });
      })
    );

    return {
      bottlenecks: this.identifyBottlenecks(criticalMetrics),
      severity: this.assessSeverity(criticalMetrics),
      solutions: this.generateSolutions(criticalMetrics),
      priority: this.prioritizeOptimizations(criticalMetrics)
    };
  }

  async validateOptimizations(originalMetrics, optimizedMetrics) {
    // Validate performance improvements
    const validation = await mcp__sublinear-time-solver__validateTemporalAdvantage({
      size: originalMetrics.length,
      distanceKm: 1000 // Symbolic distance for comparison
    });

    return {
      improvementFactor: this.calculateImprovement(originalMetrics, optimizedMetrics),
      validationResult: validation,
      confidence: this.calculateConfidence(validation)
    };
  }
}

// Deploy performance optimization in Flow Nexus
const optimizationSandbox = await mcp__flow-nexus__sandbox_create({
  template: "python",
  name: "performance-optimizer",
  env_vars: {
    OPTIMIZATION_MODE: "realtime",
    MONITORING_INTERVAL: "1000",
    RESOURCE_THRESHOLD: "80"
  },
  install_packages: ["numpy", "scipy", "psutil", "prometheus_client"]
});

// Execute performance optimization
const optimizationResult = await mcp__flow-nexus__sandbox_execute({
  sandbox_id: optimizationSandbox.id,
  code: `
    import psutil
    import numpy as np
    from datetime import datetime
    import asyncio

    class RealTimeOptimizer:
        def __init__(self):
            self.metrics_history = []
            self.optimization_interval = 1.0  # seconds

        async def monitor_and_optimize(self):
            while True:
                # Collect system metrics
                metrics = {
                    'cpu_percent': psutil.cpu_percent(interval=1),
                    'memory_percent': psutil.virtual_memory().percent,
                    'disk_io': psutil.disk_io_counters()._asdict(),
                    'network_io': psutil.net_io_counters()._asdict(),
                    'timestamp': datetime.now().isoformat()
                }

                # Add to history
                self.metrics_history.append(metrics)

                # Perform optimization if needed
                if self.needs_optimization(metrics):
                    await self.optimize_system(metrics)

                await asyncio.sleep(self.optimization_interval)

        def needs_optimization(self, metrics):
            threshold = float(os.environ.get('RESOURCE_THRESHOLD', 80))
            return (metrics['cpu_percent'] > threshold or
                    metrics['memory_percent'] > threshold)

        async def optimize_system(self, metrics):
            print(f"Optimizing system - CPU: {metrics['cpu_percent']}%, "
                  f"Memory: {metrics['memory_percent']}%")

            # Implement optimization strategies
            await self.optimize_cpu_usage()
            await self.optimize_memory_usage()
            await self.optimize_io_operations()

        async def optimize_cpu_usage(self):
            # CPU optimization logic
            print("Optimizing CPU usage...")

        async def optimize_memory_usage(self):
            # Memory optimization logic
            print("Optimizing memory usage...")

        async def optimize_io_operations(self):
            # I/O optimization logic
            print("Optimizing I/O operations...")

    # Start real-time optimization
    optimizer = RealTimeOptimizer()
    await optimizer.monitor_and_optimize()
  `,
  language: "python"
});

// Train neural networks for performance prediction
const performanceModel = await mcp__flow-nexus__neural_train({
  config: {
    architecture: {
      type: "lstm",
      layers: [
        { type: "lstm", units: 128, return_sequences: true },
        { type: "dropout", rate: 0.3 },
        { type: "lstm", units: 64, return_sequences: false },
        { type: "dense", units: 32, activation: "relu" },
        { type: "dense", units: 1, activation: "linear" }
      ]
    },
    training: {
      epochs: 50,
      batch_size: 32,
      learning_rate: 0.001,
      optimizer: "adam"
    }
  },
  tier: "medium"
});