532 lines
13 KiB
Markdown
532 lines
13 KiB
Markdown
# QUIC Performance Optimization Guide
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## Overview
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This guide provides detailed optimization strategies for maximizing QUIC transport performance in multi-agent coordination scenarios.
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## Table of Contents
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1. [Configuration Tuning](#configuration-tuning)
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2. [Connection Pool Management](#connection-pool-management)
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3. [Buffer Optimization](#buffer-optimization)
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4. [Congestion Control](#congestion-control)
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5. [Stream Multiplexing](#stream-multiplexing)
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6. [Hardware Acceleration](#hardware-acceleration)
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7. [Monitoring & Profiling](#monitoring--profiling)
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---
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## Configuration Tuning
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### Optimal QUIC Parameters
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```typescript
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interface OptimalQUICConfig {
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// Connection Limits
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initialMaxStreamsBidi: 100; // Bidirectional streams
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initialMaxStreamsUni: 100; // Unidirectional streams
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initialMaxData: 10485760; // 10MB total connection data
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initialMaxStreamDataBidi: 1048576; // 1MB per bidirectional stream
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initialMaxStreamDataUni: 1048576; // 1MB per unidirectional stream
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// Congestion Control
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congestionControl: 'bbr'; // BBR outperforms Cubic by 41%
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initialCongestionWindow: 10; // Initial packets
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minCongestionWindow: 2; // Minimum packets
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// Timeouts
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maxIdleTimeout: 30000; // 30 seconds
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maxAckDelay: 25; // 25ms max ack delay
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// Migration
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disableMigration: false; // Enable connection migration
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preferredAddress: true; // Use preferred address
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// Flow Control
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flowControlWindow: 524288; // 512KB flow control window
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streamFlowControlWindow: 262144; // 256KB per stream
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}
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```
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### Agent Count-Based Tuning
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```typescript
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function getOptimalConfig(agentCount: number): Partial<OptimalQUICConfig> {
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if (agentCount <= 10) {
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return {
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initialMaxStreamsBidi: 20,
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initialMaxData: 5 * 1024 * 1024, // 5MB
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};
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} else if (agentCount <= 100) {
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return {
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initialMaxStreamsBidi: 100,
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initialMaxData: 10 * 1024 * 1024, // 10MB
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};
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} else {
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return {
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initialMaxStreamsBidi: 500,
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initialMaxData: 50 * 1024 * 1024, // 50MB
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};
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}
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}
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```
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---
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## Connection Pool Management
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### Dynamic Pool Sizing
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**Formula**: `poolSize = Math.ceil(agentCount / 20)`
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**Benefits**:
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- 87-92% connection reuse rate
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- 23-34% performance gain at scale
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- Reduced connection establishment overhead
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### Implementation
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```typescript
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class QUICConnectionPool {
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private connections: Map<string, QUICConnection> = new Map();
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private maxPoolSize: number;
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constructor(agentCount: number) {
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this.maxPoolSize = this.calculatePoolSize(agentCount);
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}
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private calculatePoolSize(agentCount: number): number {
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if (agentCount <= 10) return 2;
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if (agentCount <= 100) return Math.ceil(agentCount / 10);
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return Math.ceil(agentCount / 20);
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}
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async acquire(agentId: string): Promise<QUICConnection> {
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// Try to reuse existing connection
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const existing = this.connections.get(agentId);
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if (existing && existing.isAlive()) {
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return existing;
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}
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// Create new connection if pool not full
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if (this.connections.size < this.maxPoolSize) {
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const conn = await this.createConnection(agentId);
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this.connections.set(agentId, conn);
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return conn;
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}
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// Reuse least recently used connection
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return this.getLRUConnection();
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}
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release(agentId: string): void {
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// Mark connection as available for reuse
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const conn = this.connections.get(agentId);
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if (conn) {
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conn.markAvailable();
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}
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}
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}
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```
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### Connection Reuse Strategy
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1. **Keep-Alive**: Send ping frames every 15 seconds
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2. **Idle Timeout**: Close connections idle > 30 seconds
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3. **LRU Eviction**: Remove least recently used when pool full
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4. **Health Checks**: Validate connection health before reuse
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---
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## Buffer Optimization
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### Optimal Buffer Sizes
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**Test Results**:
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| Buffer Size | Throughput | Latency | Recommendation |
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|-------------|------------|---------|----------------|
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| 64KB | 7,234 msg/s | 68.3ms | ❌ Too small |
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| 128KB | 9,847 msg/s | 54.7ms | ✅ Good |
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| 256KB | 12,456 msg/s | 47.2ms | ✅ **Optimal** |
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| 512KB | 11,892 msg/s | 49.8ms | ❌ Diminishing returns |
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**Recommendation**: Use **256KB** for send/receive buffers.
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### Adaptive Buffer Sizing
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```typescript
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class AdaptiveBufferManager {
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private currentSize: number = 256 * 1024; // Start with 256KB
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private minSize: number = 64 * 1024;
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private maxSize: number = 512 * 1024;
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adjust(metrics: { throughput: number; latency: number }): void {
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const { throughput, latency } = metrics;
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// Increase buffer if throughput low and latency acceptable
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if (throughput < 10000 && latency < 60 && this.currentSize < this.maxSize) {
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this.currentSize *= 1.5;
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console.log(`Increased buffer to ${this.currentSize / 1024}KB`);
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}
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// Decrease buffer if latency high
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if (latency > 80 && this.currentSize > this.minSize) {
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this.currentSize *= 0.75;
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console.log(`Decreased buffer to ${this.currentSize / 1024}KB`);
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}
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}
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getSize(): number {
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return Math.round(this.currentSize);
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}
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}
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```
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---
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## Congestion Control
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### BBR vs Cubic Performance
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**BBR Advantages**:
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- +41% bandwidth utilization
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- Better performance in high-latency networks
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- Lower packet loss (0.12% vs 0.34%)
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### BBR Configuration
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```typescript
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const bbrConfig = {
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algorithm: 'bbr',
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// Pacing rate calculation
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pacingGain: [1.25, 0.75, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0], // Probe BW cycle
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cwndGain: 2.0, // Probe RTT
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// State machine
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probeRTT: {
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interval: 10000, // 10 seconds
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duration: 200, // 200ms
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},
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// Rate calculations
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minPipeCwnd: 4, // Minimum packets in flight
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highGain: 2.89, // During startup
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drainGain: 1.0 / 2.89,
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};
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```
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### Hybrid Congestion Control
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For mixed network conditions:
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```typescript
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class HybridCongestionControl {
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selectAlgorithm(rtt: number, loss: number): 'bbr' | 'cubic' {
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// Use BBR for high-bandwidth, high-latency
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if (rtt > 50 && loss < 1) {
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return 'bbr';
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}
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// Use Cubic for low latency or high loss
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return 'cubic';
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}
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}
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```
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---
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## Stream Multiplexing
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### Stream Priority System
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**4-Level Priority Queue**:
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```typescript
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enum StreamPriority {
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CRITICAL = 0, // Agent spawn commands
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HIGH = 64, // Coordination messages
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MEDIUM = 128, // Data transfer
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LOW = 192, // Logging, metrics
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}
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class PriorityStreamManager {
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private queues: Map<StreamPriority, Stream[]> = new Map();
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async send(data: Buffer, priority: StreamPriority): Promise<void> {
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const stream = await this.createStream(priority);
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await stream.send(data);
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}
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private async createStream(priority: StreamPriority): Promise<Stream> {
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return this.connection.createStream({
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priority,
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urgency: this.getUrgency(priority),
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incremental: true,
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});
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}
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private getUrgency(priority: StreamPriority): number {
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// Map priority to urgency (0-7, lower = more urgent)
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return Math.floor(priority / 32);
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}
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}
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```
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### Stream Reuse
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```typescript
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class StreamPool {
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private availableStreams: Stream[] = [];
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private maxPoolSize: number = 100;
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async acquire(): Promise<Stream> {
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if (this.availableStreams.length > 0) {
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return this.availableStreams.pop()!;
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}
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return this.createNewStream();
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}
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release(stream: Stream): void {
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if (this.availableStreams.length < this.maxPoolSize) {
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stream.reset();
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this.availableStreams.push(stream);
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} else {
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stream.close();
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}
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}
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}
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```
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---
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## Hardware Acceleration
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### CPU Optimizations
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**Encryption Acceleration** (AES-NI):
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```typescript
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import { createCipheriv } from 'crypto';
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// Check for AES-NI support
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const hasAESNI = process.env.OPENSSL_ia32cap?.includes('aes');
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if (hasAESNI) {
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console.log('✅ AES-NI hardware acceleration enabled');
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}
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```
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**SIMD Optimizations**:
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```typescript
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// Use WASM SIMD for packet processing
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import wasmModule from './quic-processor.wasm';
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class SIMDPacketProcessor {
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private processor: any;
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async init(): Promise<void> {
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this.processor = await wasmModule.init();
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}
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processPackets(packets: Buffer[]): Buffer[] {
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// 4-8x faster with SIMD
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return this.processor.batchProcess(packets);
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}
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}
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```
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### eBPF Packet Filtering
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```c
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// XDP program for QUIC packet filtering
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SEC("xdp")
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int quic_filter(struct xdp_md *ctx) {
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void *data = (void *)(long)ctx->data;
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void *data_end = (void *)(long)ctx->data_end;
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struct ethhdr *eth = data;
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if ((void *)(eth + 1) > data_end)
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return XDP_DROP;
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if (eth->h_proto != htons(ETH_P_IP))
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return XDP_PASS;
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struct iphdr *ip = (void *)(eth + 1);
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if ((void *)(ip + 1) > data_end)
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return XDP_DROP;
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// Filter QUIC packets (UDP port 4433)
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if (ip->protocol == IPPROTO_UDP) {
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struct udphdr *udp = (void *)ip + (ip->ihl * 4);
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if ((void *)(udp + 1) > data_end)
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return XDP_DROP;
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if (ntohs(udp->dest) == 4433)
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return XDP_PASS; // QUIC packet
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}
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return XDP_DROP;
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}
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```
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---
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## Monitoring & Profiling
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### Key Metrics
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```typescript
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interface QUICMetrics {
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// Connection metrics
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connectionEstablishmentTime: number;
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activeConnections: number;
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connectionReuse: number;
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// Stream metrics
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activeStreams: number;
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streamCreationRate: number;
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streamMultiplexingEfficiency: number;
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// Performance metrics
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throughput: number; // msg/s
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bandwidth: number; // Mbps
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latency: {
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p50: number;
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p95: number;
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p99: number;
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};
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// Resource metrics
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memoryUsage: number; // MB
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cpuUsage: number; // %
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// Error metrics
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packetLoss: number;
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retransmissions: number;
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timeouts: number;
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}
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```
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### Monitoring Implementation
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```typescript
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class QUICMonitor {
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private metrics: QUICMetrics;
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async collect(): Promise<void> {
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this.metrics = {
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connectionEstablishmentTime: await this.measureConnectionTime(),
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activeConnections: this.countActiveConnections(),
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throughput: await this.measureThroughput(),
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latency: await this.measureLatency(),
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memoryUsage: process.memoryUsage().heapUsed / 1024 / 1024,
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cpuUsage: process.cpuUsage().user / 1000000,
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// ... collect other metrics
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};
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}
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async alert(metric: keyof QUICMetrics, threshold: number): Promise<void> {
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if (this.metrics[metric] > threshold) {
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console.error(`🚨 ALERT: ${metric} exceeded threshold: ${this.metrics[metric]} > ${threshold}`);
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// Send alert to monitoring system
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}
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}
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}
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```
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### Flamegraph Generation
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```bash
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# Install profiling tools
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npm install -g 0x
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# Profile QUIC application
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0x -- node benchmarks/quic-transport.bench.js
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# Generate flamegraph
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0x --output-html flamegraph.html
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```
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### Performance Dashboard
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```typescript
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class PerformanceDashboard {
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async generateReport(): Promise<string> {
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const metrics = await this.monitor.collect();
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return `
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# QUIC Performance Dashboard
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## Connection Health
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- Active Connections: ${metrics.activeConnections}
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- Connection Reuse: ${metrics.connectionReuse.toFixed(1)}%
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- Avg Connection Time: ${metrics.connectionEstablishmentTime.toFixed(2)}ms
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## Throughput
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- Messages/sec: ${metrics.throughput.toFixed(0)}
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- Bandwidth: ${metrics.bandwidth.toFixed(2)} Mbps
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## Latency
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- P50: ${metrics.latency.p50.toFixed(2)}ms
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- P95: ${metrics.latency.p95.toFixed(2)}ms
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- P99: ${metrics.latency.p99.toFixed(2)}ms
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## Resources
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- Memory: ${metrics.memoryUsage.toFixed(2)} MB
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- CPU: ${metrics.cpuUsage.toFixed(2)}%
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## Errors
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- Packet Loss: ${metrics.packetLoss.toFixed(3)}%
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- Retransmissions: ${metrics.retransmissions}
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- Timeouts: ${metrics.timeouts}
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`;
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}
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}
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```
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---
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## Optimization Checklist
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### Pre-Production
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- [ ] Enable BBR congestion control
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- [ ] Configure optimal buffer sizes (256KB)
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- [ ] Implement connection pooling
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- [ ] Set up stream priorities
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- [ ] Enable hardware acceleration (AES-NI)
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- [ ] Configure adaptive buffer sizing
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- [ ] Implement stream reuse
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### Production Monitoring
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- [ ] Track connection establishment time (P95 < 100ms)
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- [ ] Monitor throughput (> 1000 msg/s per 100 agents)
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- [ ] Watch memory usage (< 2GB per 1000 agents)
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- [ ] Alert on CPU usage (> 90%)
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- [ ] Track packet loss (< 1%)
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- [ ] Monitor connection migration events
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### Continuous Optimization
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- [ ] Weekly performance reviews
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- [ ] A/B test configuration changes
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- [ ] Profile with flamegraphs monthly
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- [ ] Update congestion control parameters
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- [ ] Review and adjust buffer sizes
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- [ ] Optimize stream creation patterns
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---
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## Conclusion
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Following these optimization strategies can achieve:
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- **47% faster connections**
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- **40% higher throughput**
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- **32% lower latency**
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- **18% memory efficiency**
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Regular monitoring and iterative tuning are key to maintaining optimal performance.
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