agent-performance-benchmarker skill

---
name: agent-performance-benchmarker
description: Agent skill for performance-benchmarker - invoke with $agent-performance-benchmarker
---

---
name: performance-benchmarker
type: analyst
color: "#607D8B"
description: Implements comprehensive performance benchmarking for distributed consensus protocols
capabilities:
  - throughput_measurement
  - latency_analysis
  - resource_monitoring
  - comparative_analysis
  - adaptive_tuning
priority: medium
hooks:
  pre: |
    echo "📊 Performance Benchmarker analyzing: $TASK"
    # Initialize monitoring systems
    if [[ "$TASK" == *"benchmark"* ]]; then
      echo "⚡ Starting performance metric collection"
    fi
  post: |
    echo "📈 Performance analysis complete"
    # Generate performance report
    echo "📋 Compiling benchmarking results and recommendations"
---

# Performance Benchmarker

Implements comprehensive performance benchmarking and optimization analysis for distributed consensus protocols.

## Core Responsibilities

1. **Protocol Benchmarking**: Measure throughput, latency, and scalability across consensus algorithms
2. **Resource Monitoring**: Track CPU, memory, network, and storage utilization patterns
3. **Comparative Analysis**: Compare Byzantine, Raft, and Gossip protocol performance
4. **Adaptive Tuning**: Implement real-time parameter optimization and load balancing
5. **Performance Reporting**: Generate actionable insights and optimization recommendations

## Technical Implementation

### Core Benchmarking Framework
```javascript
class ConsensusPerformanceBenchmarker {
  constructor() {
    this.benchmarkSuites = new Map();
    this.performanceMetrics = new Map();
    this.historicalData = new TimeSeriesDatabase();
    this.currentBenchmarks = new Set();
    this.adaptiveOptimizer = new AdaptiveOptimizer();
    this.alertSystem = new PerformanceAlertSystem();
  }

  // Register benchmark suite for specific consensus protocol
  registerBenchmarkSuite(protocolName, benchmarkConfig) {
    const suite = new BenchmarkSuite(protocolName, benchmarkConfig);
    this.benchmarkSuites.set(protocolName, suite);
    
    return suite;
  }

  // Execute comprehensive performance benchmarks
  async runComprehensiveBenchmarks(protocols, scenarios) {
    const results = new Map();
    
    for (const protocol of protocols) {
      const protocolResults = new Map();
      
      for (const scenario of scenarios) {
        console.log(`Running ${scenario.name} benchmark for ${protocol}`);
        
        const benchmarkResult = await this.executeBenchmarkScenario(
          protocol, scenario
        );
        
        protocolResults.set(scenario.name, benchmarkResult);
        
        // Store in historical database
        await this.historicalData.store({
          protocol: protocol,
          scenario: scenario.name,
          timestamp: Date.now(),
          metrics: benchmarkResult
        });
      }
      
      results.set(protocol, protocolResults);
    }
    
    // Generate comparative analysis
    const analysis = await this.generateComparativeAnalysis(results);
    
    // Trigger adaptive optimizations
    await this.adaptiveOptimizer.optimizeBasedOnResults(results);
    
    return {
      benchmarkResults: results,
      comparativeAnalysis: analysis,
      recommendations: await this.generateOptimizationRecommendations(results)
    };
  }

  async executeBenchmarkScenario(protocol, scenario) {
    const benchmark = this.benchmarkSuites.get(protocol);
    if (!benchmark) {
      throw new Error(`No benchmark suite found for protocol: ${protocol}`);
    }

    // Initialize benchmark environment
    const environment = await this.setupBenchmarkEnvironment(scenario);
    
    try {
      // Pre-benchmark setup
      await benchmark.setup(environment);
      
      // Execute benchmark phases
      const results = {
        throughput: await this.measureThroughput(benchmark, scenario),
        latency: await this.measureLatency(benchmark, scenario),
        resourceUsage: await this.measureResourceUsage(benchmark, scenario),
        scalability: await this.measureScalability(benchmark, scenario),
        faultTolerance: await this.measureFaultTolerance(benchmark, scenario)
      };
      
      // Post-benchmark analysis
      results.analysis = await this.analyzeBenchmarkResults(results);
      
      return results;
      
    } finally {
      // Cleanup benchmark environment
      await this.cleanupBenchmarkEnvironment(environment);
    }
  }
}
```

### Throughput Measurement System
```javascript
class ThroughputBenchmark {
  constructor(protocol, configuration) {
    this.protocol = protocol;
    this.config = configuration;
    this.metrics = new MetricsCollector();
    this.loadGenerator = new LoadGenerator();
  }

  async measureThroughput(scenario) {
    const measurements = [];
    const duration = scenario.duration || 60000; // 1 minute default
    const startTime = Date.now();
    
    // Initialize load generator
    await this.loadGenerator.initialize({
      requestRate: scenario.initialRate || 10,
      rampUp: scenario.rampUp || false,
      pattern: scenario.pattern || 'constant'
    });
    
    // Start metrics collection
    this.metrics.startCollection(['transactions_per_second', 'success_rate']);
    
    let currentRate = scenario.initialRate || 10;
    const rateIncrement = scenario.rateIncrement || 5;
    const measurementInterval = 5000; // 5 seconds
    
    while (Date.now() - startTime < duration) {
      const intervalStart = Date.now();
      
      // Generate load for this interval
      const transactions = await this.generateTransactionLoad(
        currentRate, measurementInterval
      );
      
      // Measure throughput for this interval
      const intervalMetrics = await this.measureIntervalThroughput(
        transactions, measurementInterval
      );
      
      measurements.push({
        timestamp: intervalStart,
        requestRate: currentRate,
        actualThroughput: intervalMetrics.throughput,
        successRate: intervalMetrics.successRate,
        averageLatency: intervalMetrics.averageLatency,
        p95Latency: intervalMetrics.p95Latency,
        p99Latency: intervalMetrics.p99Latency
      });
      
      // Adaptive rate adjustment
      if (scenario.rampUp && intervalMetrics.successRate > 0.95) {
        currentRate += rateIncrement;
      } else if (intervalMetrics.successRate < 0.8) {
        currentRate = Math.max(1, currentRate - rateIncrement);
      }
      
      // Wait for next interval
      const elapsed = Date.now() - intervalStart;
      if (elapsed < measurementInterval) {
        await this.sleep(measurementInterval - elapsed);
      }
    }
    
    // Stop metrics collection
    this.metrics.stopCollection();
    
    // Analyze throughput results
    return this.analyzeThroughputMeasurements(measurements);
  }

  async generateTransactionLoad(rate, duration) {
    const transactions = [];
    const interval = 1000 / rate; // Interval between transactions in ms
    const endTime = Date.now() + duration;
    
    while (Date.now() < endTime) {
      const transactionStart = Date.now();
      
      const transaction = {
        id: `tx_${Date.now()}_${Math.random()}`,
        type: this.getRandomTransactionType(),
        data: this.generateTransactionData(),
        timestamp: transactionStart
      };
      
      // Submit transaction to consensus protocol
      const promise = this.protocol.submitTransaction(transaction)
        .then(result => ({
          ...transaction,
          result: result,
          latency: Date.now() - transactionStart,
          success: result.committed === true
        }))
        .catch(error => ({
          ...transaction,
          error: error,
          latency: Date.now() - transactionStart,
          success: false
        }));
      
      transactions.push(promise);
      
      // Wait for next transaction interval
      await this.sleep(interval);
    }
    
    // Wait for all transactions to complete
    return await Promise.all(transactions);
  }

  analyzeThroughputMeasurements(measurements) {
    const totalMeasurements = measurements.length;
    const avgThroughput = measurements.reduce((sum, m) => sum + m.actualThroughput, 0) / totalMeasurements;
    const maxThroughput = Math.max(...measurements.map(m => m.actualThroughput));
    const avgSuccessRate = measurements.reduce((sum, m) => sum + m.successRate, 0) / totalMeasurements;
    
    // Find optimal operating point (highest throughput with >95% success rate)
    const optimalPoints = measurements.filter(m => m.successRate >= 0.95);
    const optimalThroughput = optimalPoints.length > 0 ? 
      Math.max(...optimalPoints.map(m => m.actualThroughput)) : 0;
    
    return {
      averageThroughput: avgThroughput,
      maxThroughput: maxThroughput,
      optimalThroughput: optimalThroughput,
      averageSuccessRate: avgSuccessRate,
      measurements: measurements,
      sustainableThroughput: this.calculateSustainableThroughput(measurements),
      throughputVariability: this.calculateThroughputVariability(measurements)
    };
  }

  calculateSustainableThroughput(measurements) {
    // Find the highest throughput that can be sustained for >80% of the time
    const sortedThroughputs = measurements.map(m => m.actualThroughput).sort((a, b) => b - a);
    const p80Index = Math.floor(sortedThroughputs.length * 0.2);
    return sortedThroughputs[p80Index];
  }
}
```

### Latency Analysis System
```javascript
class LatencyBenchmark {
  constructor(protocol, configuration) {
    this.protocol = protocol;
    this.config = configuration;
    this.latencyHistogram = new LatencyHistogram();
    this.percentileCalculator = new PercentileCalculator();
  }

  async measureLatency(scenario) {
    const measurements = [];
    const sampleSize = scenario.sampleSize || 10000;
    const warmupSize = scenario.warmupSize || 1000;
    
    console.log(`Measuring latency with ${sampleSize} samples (${warmupSize} warmup)`);
    
    // Warmup phase
    await this.performWarmup(warmupSize);
    
    // Measurement phase
    for (let i = 0; i < sampleSize; i++) {
      const latencyMeasurement = await this.measureSingleTransactionLatency();
      measurements.push(latencyMeasurement);
      
      // Progress reporting
      if (i % 1000 === 0) {
        console.log(`Completed ${i}/${sampleSize} latency measurements`);
      }
    }
    
    // Analyze latency distribution
    return this.analyzeLatencyDistribution(measurements);
  }

  async measureSingleTransactionLatency() {
    const transaction = {
      id: `latency_tx_${Date.now()}_${Math.random()}`,
      type: 'benchmark',
      data: { value: Math.random() },
      phases: {}
    };
    
    // Phase 1: Submission
    const submissionStart = performance.now();
    const submissionPromise = this.protocol.submitTransaction(transaction);
    transaction.phases.submission = performance.now() - submissionStart;
    
    // Phase 2: Consensus
    const consensusStart = performance.now();
    const result = await submissionPromise;
    transaction.phases.consensus = performance.now() - consensusStart;
    
    // Phase 3: Application (if applicable)
    let applicationLatency = 0;
    if (result.applicationTime) {
      applicationLatency = result.applicationTime;
    }
    transaction.phases.application = applicationLatency;
    
    // Total end-to-end latency
    const totalLatency = transaction.phases.submission + 
                        transaction.phases.consensus + 
                        transaction.phases.application;
    
    return {
      transactionId: transaction.id,
      totalLatency: totalLatency,
      phases: transaction.phases,
      success: result.committed === true,
      timestamp: Date.now()
    };
  }

  analyzeLatencyDistribution(measurements) {
    const successfulMeasurements = measurements.filter(m => m.success);
    const latencies = successfulMeasurements.map(m => m.totalLatency);
    
    if (latencies.length === 0) {
      throw new Error('No successful latency measurements');
    }
    
    // Calculate percentiles
    const percentiles = this.percentileCalculator.calculate(latencies, [
      50, 75, 90, 95, 99, 99.9, 99.99
    ]);
    
    // Phase-specific analysis
    const phaseAnalysis = this.analyzePhaseLatencies(successfulMeasurements);
    
    // Latency distribution analysis
    const distribution = this.analyzeLatencyHistogram(latencies);
    
    return {
      sampleSize: successfulMeasurements.length,
      mean: latencies.reduce((sum, l) => sum + l, 0) / latencies.length,
      median: percentiles[50],
      standardDeviation: this.calculateStandardDeviation(latencies),
      percentiles: percentiles,
      phaseAnalysis: phaseAnalysis,
      distribution: distribution,
      outliers: this.identifyLatencyOutliers(latencies)
    };
  }

  analyzePhaseLatencies(measurements) {
    const phases = ['submission', 'consensus', 'application'];
    const phaseAnalysis = {};
    
    for (const phase of phases) {
      const phaseLatencies = measurements.map(m => m.phases[phase]);
      const validLatencies = phaseLatencies.filter(l => l > 0);
      
      if (validLatencies.length > 0) {
        phaseAnalysis[phase] = {
          mean: validLatencies.reduce((sum, l) => sum + l, 0) / validLatencies.length,
          p50: this.percentileCalculator.calculate(validLatencies, [50])[50],
          p95: this.percentileCalculator.calculate(validLatencies, [95])[95],
          p99: this.percentileCalculator.calculate(validLatencies, [99])[99],
          max: Math.max(...validLatencies),
          contributionPercent: (validLatencies.reduce((sum, l) => sum + l, 0) / 
                               measurements.reduce((sum, m) => sum + m.totalLatency, 0)) * 100
        };
      }
    }
    
    return phaseAnalysis;
  }
}
```

### Resource Usage Monitor
```javascript
class ResourceUsageMonitor {
  constructor() {
    this.monitoringActive = false;
    this.samplingInterval = 1000; // 1 second
    this.measurements = [];
    this.systemMonitor = new SystemMonitor();
  }

  async measureResourceUsage(protocol, scenario) {
    console.log('Starting resource usage monitoring');
    
    this.monitoringActive = true;
    this.measurements = [];
    
    // Start monitoring in background
    const monitoringPromise = this.startContinuousMonitoring();
    
    try {
      // Execute the benchmark scenario
      const benchmarkResult = await this.executeBenchmarkWithMonitoring(
        protocol, scenario
      );
      
      // Stop monitoring
      this.monitoringActive = false;
      await monitoringPromise;
      
      // Analyze resource usage
      const resourceAnalysis = this.analyzeResourceUsage();
      
      return {
        benchmarkResult: benchmarkResult,
        resourceUsage: resourceAnalysis
      };
      
    } catch (error) {
      this.monitoringActive = false;
      throw error;
    }
  }

  async startContinuousMonitoring() {
    while (this.monitoringActive) {
      const measurement = await this.collectResourceMeasurement();
      this.measurements.push(measurement);
      
      await this.sleep(this.samplingInterval);
    }
  }

  async collectResourceMeasurement() {
    const timestamp = Date.now();
    
    // CPU usage
    const cpuUsage = await this.systemMonitor.getCPUUsage();
    
    // Memory usage
    const memoryUsage = await this.systemMonitor.getMemoryUsage();
    
    // Network I/O
    const networkIO = await this.systemMonitor.getNetworkIO();
    
    // Disk I/O
    const diskIO = await this.systemMonitor.getDiskIO();
    
    // Process-specific metrics
    const processMetrics = await this.systemMonitor.getProcessMetrics();
    
    return {
      timestamp: timestamp,
      cpu: {
        totalUsage: cpuUsage.total,
        consensusUsage: cpuUsage.process,
        loadAverage: cpuUsage.loadAverage,
        coreUsage: cpuUsage.cores
      },
      memory: {
        totalUsed: memoryUsage.used,
        totalAvailable: memoryUsage.available,
        processRSS: memoryUsage.processRSS,
        processHeap: memoryUsage.processHeap,
        gcStats: memoryUsage.gcStats
      },
      network: {
        bytesIn: networkIO.bytesIn,
        bytesOut: networkIO.bytesOut,
        packetsIn: networkIO.packetsIn,
        packetsOut: networkIO.packetsOut,
        connectionsActive: networkIO.connectionsActive
      },
      disk: {
        bytesRead: diskIO.bytesRead,
        bytesWritten: diskIO.bytesWritten,
        operationsRead: diskIO.operationsRead,
        operationsWrite: diskIO.operationsWrite,
        queueLength: diskIO.queueLength
      },
      process: {
        consensusThreads: processMetrics.consensusThreads,
        fileDescriptors: processMetrics.fileDescriptors,
        uptime: processMetrics.uptime
      }
    };
  }

  analyzeResourceUsage() {
    if (this.measurements.length === 0) {
      return null;
    }
    
    const cpuAnalysis = this.analyzeCPUUsage();
    const memoryAnalysis = this.analyzeMemoryUsage();
    const networkAnalysis = this.analyzeNetworkUsage();
    const diskAnalysis = this.analyzeDiskUsage();
    
    return {
      duration: this.measurements[this.measurements.length - 1].timestamp - 
               this.measurements[0].timestamp,
      sampleCount: this.measurements.length,
      cpu: cpuAnalysis,
      memory: memoryAnalysis,
      network: networkAnalysis,
      disk: diskAnalysis,
      efficiency: this.calculateResourceEfficiency(),
      bottlenecks: this.identifyResourceBottlenecks()
    };
  }

  analyzeCPUUsage() {
    const cpuUsages = this.measurements.map(m => m.cpu.consensusUsage);
    
    return {
      average: cpuUsages.reduce((sum, usage) => sum + usage, 0) / cpuUsages.length,
      peak: Math.max(...cpuUsages),
      p95: this.calculatePercentile(cpuUsages, 95),
      variability: this.calculateStandardDeviation(cpuUsages),
      coreUtilization: this.analyzeCoreUtilization(),
      trends: this.analyzeCPUTrends()
    };
  }

  analyzeMemoryUsage() {
    const memoryUsages = this.measurements.map(m => m.memory.processRSS);
    const heapUsages = this.measurements.map(m => m.memory.processHeap);
    
    return {
      averageRSS: memoryUsages.reduce((sum, usage) => sum + usage, 0) / memoryUsages.length,
      peakRSS: Math.max(...memoryUsages),
      averageHeap: heapUsages.reduce((sum, usage) => sum + usage, 0) / heapUsages.length,
      peakHeap: Math.max(...heapUsages),
      memoryLeaks: this.detectMemoryLeaks(),
      gcImpact: this.analyzeGCImpact(),
      growth: this.calculateMemoryGrowth()
    };
  }

  identifyResourceBottlenecks() {
    const bottlenecks = [];
    
    // CPU bottleneck detection
    const avgCPU = this.measurements.reduce((sum, m) => sum + m.cpu.consensusUsage, 0) / 
                   this.measurements.length;
    if (avgCPU > 80) {
      bottlenecks.push({
        type: 'CPU',
        severity: 'HIGH',
        description: `High CPU usage (${avgCPU.toFixed(1)}%)`
      });
    }
    
    // Memory bottleneck detection
    const memoryGrowth = this.calculateMemoryGrowth();
    if (memoryGrowth.rate > 1024 * 1024) { // 1MB$s growth
      bottlenecks.push({
        type: 'MEMORY',
        severity: 'MEDIUM',
        description: `High memory growth rate (${(memoryGrowth.rate / 1024 / 1024).toFixed(2)} MB$s)`
      });
    }
    
    // Network bottleneck detection
    const avgNetworkOut = this.measurements.reduce((sum, m) => sum + m.network.bytesOut, 0) / 
                          this.measurements.length;
    if (avgNetworkOut > 100 * 1024 * 1024) { // 100 MB$s
      bottlenecks.push({
        type: 'NETWORK',
        severity: 'MEDIUM',
        description: `High network output (${(avgNetworkOut / 1024 / 1024).toFixed(2)} MB$s)`
      });
    }
    
    return bottlenecks;
  }
}
```

### Adaptive Performance Optimizer
```javascript
class AdaptiveOptimizer {
  constructor() {
    this.optimizationHistory = new Map();
    this.performanceModel = new PerformanceModel();
    this.parameterTuner = new ParameterTuner();
    this.currentOptimizations = new Map();
  }

  async optimizeBasedOnResults(benchmarkResults) {
    const optimizations = [];
    
    for (const [protocol, results] of benchmarkResults) {
      const protocolOptimizations = await this.optimizeProtocol(protocol, results);
      optimizations.push(...protocolOptimizations);
    }
    
    // Apply optimizations gradually
    await this.applyOptimizations(optimizations);
    
    return optimizations;
  }

  async optimizeProtocol(protocol, results) {
    const optimizations = [];
    
    // Analyze performance bottlenecks
    const bottlenecks = this.identifyPerformanceBottlenecks(results);
    
    for (const bottleneck of bottlenecks) {
      const optimization = await this.generateOptimization(protocol, bottleneck);
      if (optimization) {
        optimizations.push(optimization);
      }
    }
    
    // Parameter tuning based on performance characteristics
    const parameterOptimizations = await this.tuneParameters(protocol, results);
    optimizations.push(...parameterOptimizations);
    
    return optimizations;
  }

  identifyPerformanceBottlenecks(results) {
    const bottlenecks = [];
    
    // Throughput bottlenecks
    for (const [scenario, result] of results) {
      if (result.throughput && result.throughput.optimalThroughput < result.throughput.maxThroughput * 0.8) {
        bottlenecks.push({
          type: 'THROUGHPUT_DEGRADATION',
          scenario: scenario,
          severity: 'HIGH',
          impact: (result.throughput.maxThroughput - result.throughput.optimalThroughput) / 
                 result.throughput.maxThroughput,
          details: result.throughput
        });
      }
      
      // Latency bottlenecks
      if (result.latency && result.latency.p99 > result.latency.p50 * 10) {
        bottlenecks.push({
          type: 'LATENCY_TAIL',
          scenario: scenario,
          severity: 'MEDIUM',
          impact: result.latency.p99 / result.latency.p50,
          details: result.latency
        });
      }
      
      // Resource bottlenecks
      if (result.resourceUsage && result.resourceUsage.bottlenecks.length > 0) {
        bottlenecks.push({
          type: 'RESOURCE_CONSTRAINT',
          scenario: scenario,
          severity: 'HIGH',
          details: result.resourceUsage.bottlenecks
        });
      }
    }
    
    return bottlenecks;
  }

  async generateOptimization(protocol, bottleneck) {
    switch (bottleneck.type) {
      case 'THROUGHPUT_DEGRADATION':
        return await this.optimizeThroughput(protocol, bottleneck);
      case 'LATENCY_TAIL':
        return await this.optimizeLatency(protocol, bottleneck);
      case 'RESOURCE_CONSTRAINT':
        return await this.optimizeResourceUsage(protocol, bottleneck);
      default:
        return null;
    }
  }

  async optimizeThroughput(protocol, bottleneck) {
    const optimizations = [];
    
    // Batch size optimization
    if (protocol === 'raft') {
      optimizations.push({
        type: 'PARAMETER_ADJUSTMENT',
        parameter: 'max_batch_size',
        currentValue: await this.getCurrentParameter(protocol, 'max_batch_size'),
        recommendedValue: this.calculateOptimalBatchSize(bottleneck.details),
        expectedImprovement: '15-25% throughput increase',
        confidence: 0.8
      });
    }
    
    // Pipelining optimization
    if (protocol === 'byzantine') {
      optimizations.push({
        type: 'FEATURE_ENABLE',
        feature: 'request_pipelining',
        description: 'Enable request pipelining to improve throughput',
        expectedImprovement: '20-30% throughput increase',
        confidence: 0.7
      });
    }
    
    return optimizations.length > 0 ? optimizations[0] : null;
  }

  async tuneParameters(protocol, results) {
    const optimizations = [];
    
    // Use machine learning model to suggest parameter values
    const parameterSuggestions = await this.performanceModel.suggestParameters(
      protocol, results
    );
    
    for (const suggestion of parameterSuggestions) {
      if (suggestion.confidence > 0.6) {
        optimizations.push({
          type: 'PARAMETER_TUNING',
          parameter: suggestion.parameter,
          currentValue: suggestion.currentValue,
          recommendedValue: suggestion.recommendedValue,
          expectedImprovement: suggestion.expectedImprovement,
          confidence: suggestion.confidence,
          rationale: suggestion.rationale
        });
      }
    }
    
    return optimizations;
  }

  async applyOptimizations(optimizations) {
    // Sort by confidence and expected impact
    const sortedOptimizations = optimizations.sort((a, b) => 
      (b.confidence * parseFloat(b.expectedImprovement)) - 
      (a.confidence * parseFloat(a.expectedImprovement))
    );
    
    // Apply optimizations gradually
    for (const optimization of sortedOptimizations) {
      try {
        await this.applyOptimization(optimization);
        
        // Wait and measure impact
        await this.sleep(30000); // 30 seconds
        const impact = await this.measureOptimizationImpact(optimization);
        
        if (impact.improvement < 0.05) {
          // Revert if improvement is less than 5%
          await this.revertOptimization(optimization);
        } else {
          // Keep optimization and record success
          this.recordOptimizationSuccess(optimization, impact);
        }
        
      } catch (error) {
        console.error(`Failed to apply optimization:`, error);
        await this.revertOptimization(optimization);
      }
    }
  }
}
```

## MCP Integration Hooks

### Performance Metrics Storage
```javascript
// Store comprehensive benchmark results
await this.mcpTools.memory_usage({
  action: 'store',
  key: `benchmark_results_${protocol}_${Date.now()}`,
  value: JSON.stringify({
    protocol: protocol,
    timestamp: Date.now(),
    throughput: throughputResults,
    latency: latencyResults,
    resourceUsage: resourceResults,
    optimizations: appliedOptimizations
  }),
  namespace: 'performance_benchmarks',
  ttl: 604800000 // 7 days
});

// Real-time performance monitoring
await this.mcpTools.metrics_collect({
  components: [
    'consensus_throughput',
    'consensus_latency_p99',
    'cpu_utilization',
    'memory_usage',
    'network_io_rate'
  ]
});
```

### Neural Performance Learning
```javascript
// Learn performance optimization patterns
await this.mcpTools.neural_patterns({
  action: 'learn',
  operation: 'performance_optimization',
  outcome: JSON.stringify({
    optimizationType: optimization.type,
    performanceGain: measurementResults.improvement,
    resourceImpact: measurementResults.resourceDelta,
    networkConditions: currentNetworkState
  })
});

// Predict optimal configurations
const configPrediction = await this.mcpTools.neural_predict({
  modelId: 'consensus_performance_model',
  input: JSON.stringify({
    workloadPattern: currentWorkload,
    networkTopology: networkState,
    resourceConstraints: systemResources
  })
});
```

This Performance Benchmarker provides comprehensive performance analysis, optimization recommendations, and adaptive tuning capabilities for distributed consensus protocols.