performance-benchmarker.mdā¢27.2 kB
---
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 "š Performance Benchmarker analyzing: $TASK"
# Initialize monitoring systems
if [[ "$TASK" == *"benchmark"* ]]; then
echo "ā” Starting performance metric collection"
fi
post: |
echo "š Performance analysis complete"
# Generate performance report
echo "š 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.