agent-performance-benchmarker

仓库: ruvnet/ruflo
安装量: 405
排名: #8180

安装

npx skills add https://github.com/ruvnet/ruflo --skill 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
Protocol Benchmarking
Measure throughput, latency, and scalability across consensus algorithms
Resource Monitoring
Track CPU, memory, network, and storage utilization patterns
Comparative Analysis
Compare Byzantine, Raft, and Gossip protocol performance
Adaptive Tuning
Implement real-time parameter optimization and load balancing
Performance Reporting
Generate actionable insights and optimization recommendations Technical Implementation Core Benchmarking Framework 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 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 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 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 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 // 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 // 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.

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