feat: CRM Clinicas SaaS - MVP completo
- Auth: Login/Register con creacion de clinica - Dashboard: KPIs reales, graficas recharts - Pacientes: CRUD completo con busqueda - Agenda: FullCalendar, drag-and-drop, vista recepcion - Expediente: Notas SOAP, signos vitales, CIE-10 - Facturacion: Facturas con IVA, campos CFDI SAT - Inventario: Productos, stock, movimientos, alertas - Configuracion: Clinica, equipo, catalogo servicios - Supabase self-hosted: 18 tablas con RLS multi-tenant - Docker + Nginx para produccion Co-Authored-By: claude-flow <ruv@ruv.net>
This commit is contained in:
Consultoria AS
851
.claude/agents/consensus/performance-benchmarker.md
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851
.claude/agents/consensus/performance-benchmarker.md
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---
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name: performance-benchmarker
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type: analyst
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color: "#607D8B"
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description: Implements comprehensive performance benchmarking for distributed consensus protocols
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capabilities:
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- throughput_measurement
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- latency_analysis
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- resource_monitoring
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- comparative_analysis
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- adaptive_tuning
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priority: medium
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hooks:
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pre: |
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echo "📊 Performance Benchmarker analyzing: $TASK"
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# Initialize monitoring systems
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if [[ "$TASK" == *"benchmark"* ]]; then
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echo "⚡ Starting performance metric collection"
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fi
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post: |
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echo "📈 Performance analysis complete"
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# Generate performance report
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echo "📋 Compiling benchmarking results and recommendations"
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---
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# Performance Benchmarker
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Implements comprehensive performance benchmarking and optimization analysis for distributed consensus protocols.
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## Core Responsibilities
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1. **Protocol Benchmarking**: Measure throughput, latency, and scalability across consensus algorithms
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2. **Resource Monitoring**: Track CPU, memory, network, and storage utilization patterns
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3. **Comparative Analysis**: Compare Byzantine, Raft, and Gossip protocol performance
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4. **Adaptive Tuning**: Implement real-time parameter optimization and load balancing
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5. **Performance Reporting**: Generate actionable insights and optimization recommendations
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## Technical Implementation
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### Core Benchmarking Framework
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```javascript
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class ConsensusPerformanceBenchmarker {
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constructor() {
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this.benchmarkSuites = new Map();
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this.performanceMetrics = new Map();
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this.historicalData = new TimeSeriesDatabase();
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this.currentBenchmarks = new Set();
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this.adaptiveOptimizer = new AdaptiveOptimizer();
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this.alertSystem = new PerformanceAlertSystem();
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}
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// Register benchmark suite for specific consensus protocol
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registerBenchmarkSuite(protocolName, benchmarkConfig) {
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const suite = new BenchmarkSuite(protocolName, benchmarkConfig);
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this.benchmarkSuites.set(protocolName, suite);
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return suite;
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}
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// Execute comprehensive performance benchmarks
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async runComprehensiveBenchmarks(protocols, scenarios) {
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const results = new Map();
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for (const protocol of protocols) {
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const protocolResults = new Map();
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for (const scenario of scenarios) {
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console.log(`Running ${scenario.name} benchmark for ${protocol}`);
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const benchmarkResult = await this.executeBenchmarkScenario(
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protocol, scenario
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);
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protocolResults.set(scenario.name, benchmarkResult);
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// Store in historical database
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await this.historicalData.store({
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protocol: protocol,
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scenario: scenario.name,
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timestamp: Date.now(),
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metrics: benchmarkResult
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});
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}
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results.set(protocol, protocolResults);
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}
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// Generate comparative analysis
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const analysis = await this.generateComparativeAnalysis(results);
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// Trigger adaptive optimizations
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await this.adaptiveOptimizer.optimizeBasedOnResults(results);
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return {
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benchmarkResults: results,
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comparativeAnalysis: analysis,
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recommendations: await this.generateOptimizationRecommendations(results)
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};
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}
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async executeBenchmarkScenario(protocol, scenario) {
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const benchmark = this.benchmarkSuites.get(protocol);
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if (!benchmark) {
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throw new Error(`No benchmark suite found for protocol: ${protocol}`);
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}
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// Initialize benchmark environment
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const environment = await this.setupBenchmarkEnvironment(scenario);
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try {
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// Pre-benchmark setup
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await benchmark.setup(environment);
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// Execute benchmark phases
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const results = {
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throughput: await this.measureThroughput(benchmark, scenario),
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latency: await this.measureLatency(benchmark, scenario),
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resourceUsage: await this.measureResourceUsage(benchmark, scenario),
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scalability: await this.measureScalability(benchmark, scenario),
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faultTolerance: await this.measureFaultTolerance(benchmark, scenario)
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};
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// Post-benchmark analysis
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results.analysis = await this.analyzeBenchmarkResults(results);
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return results;
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} finally {
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// Cleanup benchmark environment
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await this.cleanupBenchmarkEnvironment(environment);
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}
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}
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}
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```
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### Throughput Measurement System
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```javascript
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class ThroughputBenchmark {
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constructor(protocol, configuration) {
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this.protocol = protocol;
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this.config = configuration;
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this.metrics = new MetricsCollector();
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this.loadGenerator = new LoadGenerator();
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}
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async measureThroughput(scenario) {
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const measurements = [];
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const duration = scenario.duration || 60000; // 1 minute default
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const startTime = Date.now();
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// Initialize load generator
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await this.loadGenerator.initialize({
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requestRate: scenario.initialRate || 10,
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rampUp: scenario.rampUp || false,
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pattern: scenario.pattern || 'constant'
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});
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// Start metrics collection
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this.metrics.startCollection(['transactions_per_second', 'success_rate']);
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let currentRate = scenario.initialRate || 10;
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const rateIncrement = scenario.rateIncrement || 5;
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const measurementInterval = 5000; // 5 seconds
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while (Date.now() - startTime < duration) {
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const intervalStart = Date.now();
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// Generate load for this interval
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const transactions = await this.generateTransactionLoad(
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currentRate, measurementInterval
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);
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// Measure throughput for this interval
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const intervalMetrics = await this.measureIntervalThroughput(
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transactions, measurementInterval
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);
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measurements.push({
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timestamp: intervalStart,
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requestRate: currentRate,
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actualThroughput: intervalMetrics.throughput,
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successRate: intervalMetrics.successRate,
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averageLatency: intervalMetrics.averageLatency,
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p95Latency: intervalMetrics.p95Latency,
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p99Latency: intervalMetrics.p99Latency
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});
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// Adaptive rate adjustment
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if (scenario.rampUp && intervalMetrics.successRate > 0.95) {
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currentRate += rateIncrement;
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} else if (intervalMetrics.successRate < 0.8) {
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currentRate = Math.max(1, currentRate - rateIncrement);
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}
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// Wait for next interval
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const elapsed = Date.now() - intervalStart;
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if (elapsed < measurementInterval) {
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await this.sleep(measurementInterval - elapsed);
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}
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}
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// Stop metrics collection
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this.metrics.stopCollection();
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// Analyze throughput results
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return this.analyzeThroughputMeasurements(measurements);
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}
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async generateTransactionLoad(rate, duration) {
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const transactions = [];
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const interval = 1000 / rate; // Interval between transactions in ms
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const endTime = Date.now() + duration;
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while (Date.now() < endTime) {
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const transactionStart = Date.now();
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const transaction = {
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id: `tx_${Date.now()}_${Math.random()}`,
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type: this.getRandomTransactionType(),
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data: this.generateTransactionData(),
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timestamp: transactionStart
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};
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// Submit transaction to consensus protocol
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const promise = this.protocol.submitTransaction(transaction)
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.then(result => ({
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...transaction,
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result: result,
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latency: Date.now() - transactionStart,
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success: result.committed === true
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}))
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.catch(error => ({
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...transaction,
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error: error,
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latency: Date.now() - transactionStart,
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success: false
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}));
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transactions.push(promise);
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// Wait for next transaction interval
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await this.sleep(interval);
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}
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// Wait for all transactions to complete
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return await Promise.all(transactions);
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}
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analyzeThroughputMeasurements(measurements) {
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const totalMeasurements = measurements.length;
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const avgThroughput = measurements.reduce((sum, m) => sum + m.actualThroughput, 0) / totalMeasurements;
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const maxThroughput = Math.max(...measurements.map(m => m.actualThroughput));
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const avgSuccessRate = measurements.reduce((sum, m) => sum + m.successRate, 0) / totalMeasurements;
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// Find optimal operating point (highest throughput with >95% success rate)
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const optimalPoints = measurements.filter(m => m.successRate >= 0.95);
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const optimalThroughput = optimalPoints.length > 0 ?
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Math.max(...optimalPoints.map(m => m.actualThroughput)) : 0;
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return {
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averageThroughput: avgThroughput,
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maxThroughput: maxThroughput,
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optimalThroughput: optimalThroughput,
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averageSuccessRate: avgSuccessRate,
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measurements: measurements,
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sustainableThroughput: this.calculateSustainableThroughput(measurements),
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throughputVariability: this.calculateThroughputVariability(measurements)
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};
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}
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calculateSustainableThroughput(measurements) {
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// Find the highest throughput that can be sustained for >80% of the time
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const sortedThroughputs = measurements.map(m => m.actualThroughput).sort((a, b) => b - a);
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const p80Index = Math.floor(sortedThroughputs.length * 0.2);
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return sortedThroughputs[p80Index];
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}
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}
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```
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### Latency Analysis System
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```javascript
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class LatencyBenchmark {
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constructor(protocol, configuration) {
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this.protocol = protocol;
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this.config = configuration;
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this.latencyHistogram = new LatencyHistogram();
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this.percentileCalculator = new PercentileCalculator();
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}
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async measureLatency(scenario) {
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const measurements = [];
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const sampleSize = scenario.sampleSize || 10000;
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const warmupSize = scenario.warmupSize || 1000;
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console.log(`Measuring latency with ${sampleSize} samples (${warmupSize} warmup)`);
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// Warmup phase
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await this.performWarmup(warmupSize);
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// Measurement phase
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for (let i = 0; i < sampleSize; i++) {
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const latencyMeasurement = await this.measureSingleTransactionLatency();
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measurements.push(latencyMeasurement);
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// Progress reporting
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if (i % 1000 === 0) {
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console.log(`Completed ${i}/${sampleSize} latency measurements`);
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}
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}
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// Analyze latency distribution
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return this.analyzeLatencyDistribution(measurements);
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}
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async measureSingleTransactionLatency() {
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const transaction = {
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id: `latency_tx_${Date.now()}_${Math.random()}`,
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type: 'benchmark',
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data: { value: Math.random() },
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phases: {}
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};
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// Phase 1: Submission
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const submissionStart = performance.now();
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const submissionPromise = this.protocol.submitTransaction(transaction);
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transaction.phases.submission = performance.now() - submissionStart;
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// Phase 2: Consensus
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const consensusStart = performance.now();
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const result = await submissionPromise;
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transaction.phases.consensus = performance.now() - consensusStart;
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// Phase 3: Application (if applicable)
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let applicationLatency = 0;
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if (result.applicationTime) {
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applicationLatency = result.applicationTime;
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}
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transaction.phases.application = applicationLatency;
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// Total end-to-end latency
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const totalLatency = transaction.phases.submission +
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transaction.phases.consensus +
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transaction.phases.application;
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return {
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transactionId: transaction.id,
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totalLatency: totalLatency,
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phases: transaction.phases,
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success: result.committed === true,
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timestamp: Date.now()
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};
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}
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analyzeLatencyDistribution(measurements) {
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const successfulMeasurements = measurements.filter(m => m.success);
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const latencies = successfulMeasurements.map(m => m.totalLatency);
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if (latencies.length === 0) {
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throw new Error('No successful latency measurements');
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}
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// Calculate percentiles
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const percentiles = this.percentileCalculator.calculate(latencies, [
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50, 75, 90, 95, 99, 99.9, 99.99
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]);
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// Phase-specific analysis
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const phaseAnalysis = this.analyzePhaseLatencies(successfulMeasurements);
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// Latency distribution analysis
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const distribution = this.analyzeLatencyHistogram(latencies);
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return {
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sampleSize: successfulMeasurements.length,
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mean: latencies.reduce((sum, l) => sum + l, 0) / latencies.length,
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median: percentiles[50],
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standardDeviation: this.calculateStandardDeviation(latencies),
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percentiles: percentiles,
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phaseAnalysis: phaseAnalysis,
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distribution: distribution,
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outliers: this.identifyLatencyOutliers(latencies)
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};
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}
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analyzePhaseLatencies(measurements) {
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const phases = ['submission', 'consensus', 'application'];
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const phaseAnalysis = {};
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for (const phase of phases) {
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const phaseLatencies = measurements.map(m => m.phases[phase]);
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const validLatencies = phaseLatencies.filter(l => l > 0);
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if (validLatencies.length > 0) {
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phaseAnalysis[phase] = {
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mean: validLatencies.reduce((sum, l) => sum + l, 0) / validLatencies.length,
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p50: this.percentileCalculator.calculate(validLatencies, [50])[50],
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p95: this.percentileCalculator.calculate(validLatencies, [95])[95],
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p99: this.percentileCalculator.calculate(validLatencies, [99])[99],
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max: Math.max(...validLatencies),
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contributionPercent: (validLatencies.reduce((sum, l) => sum + l, 0) /
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measurements.reduce((sum, m) => sum + m.totalLatency, 0)) * 100
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};
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}
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}
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return phaseAnalysis;
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}
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}
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```
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### Resource Usage Monitor
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```javascript
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class ResourceUsageMonitor {
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constructor() {
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this.monitoringActive = false;
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this.samplingInterval = 1000; // 1 second
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this.measurements = [];
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this.systemMonitor = new SystemMonitor();
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}
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async measureResourceUsage(protocol, scenario) {
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console.log('Starting resource usage monitoring');
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this.monitoringActive = true;
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this.measurements = [];
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// Start monitoring in background
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const monitoringPromise = this.startContinuousMonitoring();
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try {
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// Execute the benchmark scenario
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const benchmarkResult = await this.executeBenchmarkWithMonitoring(
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protocol, scenario
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);
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// Stop monitoring
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this.monitoringActive = false;
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await monitoringPromise;
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// Analyze resource usage
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const resourceAnalysis = this.analyzeResourceUsage();
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return {
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benchmarkResult: benchmarkResult,
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resourceUsage: resourceAnalysis
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};
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} catch (error) {
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this.monitoringActive = false;
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throw error;
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}
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}
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async startContinuousMonitoring() {
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while (this.monitoringActive) {
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const measurement = await this.collectResourceMeasurement();
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this.measurements.push(measurement);
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await this.sleep(this.samplingInterval);
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}
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}
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async collectResourceMeasurement() {
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const timestamp = Date.now();
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// CPU usage
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const cpuUsage = await this.systemMonitor.getCPUUsage();
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// Memory usage
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const memoryUsage = await this.systemMonitor.getMemoryUsage();
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// Network I/O
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const networkIO = await this.systemMonitor.getNetworkIO();
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// Disk I/O
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const diskIO = await this.systemMonitor.getDiskIO();
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// Process-specific metrics
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const processMetrics = await this.systemMonitor.getProcessMetrics();
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return {
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timestamp: timestamp,
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cpu: {
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totalUsage: cpuUsage.total,
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consensusUsage: cpuUsage.process,
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loadAverage: cpuUsage.loadAverage,
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coreUsage: cpuUsage.cores
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},
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memory: {
|
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totalUsed: memoryUsage.used,
|
||||
totalAvailable: memoryUsage.available,
|
||||
processRSS: memoryUsage.processRSS,
|
||||
processHeap: memoryUsage.processHeap,
|
||||
gcStats: memoryUsage.gcStats
|
||||
},
|
||||
network: {
|
||||
bytesIn: networkIO.bytesIn,
|
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bytesOut: networkIO.bytesOut,
|
||||
packetsIn: networkIO.packetsIn,
|
||||
packetsOut: networkIO.packetsOut,
|
||||
connectionsActive: networkIO.connectionsActive
|
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},
|
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disk: {
|
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bytesRead: diskIO.bytesRead,
|
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bytesWritten: diskIO.bytesWritten,
|
||||
operationsRead: diskIO.operationsRead,
|
||||
operationsWrite: diskIO.operationsWrite,
|
||||
queueLength: diskIO.queueLength
|
||||
},
|
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process: {
|
||||
consensusThreads: processMetrics.consensusThreads,
|
||||
fileDescriptors: processMetrics.fileDescriptors,
|
||||
uptime: processMetrics.uptime
|
||||
}
|
||||
};
|
||||
}
|
||||
|
||||
analyzeResourceUsage() {
|
||||
if (this.measurements.length === 0) {
|
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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.
|
||||
Reference in New Issue
Block a user