Performance and Scaling Considerations

Explore the performance characteristics of AgentHub, scaling patterns, and optimization strategies for different deployment scenarios.

Performance and Scaling Considerations

This document explores the performance characteristics of AgentHub, scaling patterns, and optimization strategies for different deployment scenarios.

Performance Characteristics

Baseline Performance Metrics

Test Environment:

  • 4-core Intel i7 processor
  • 16GB RAM
  • Local network (localhost)
  • Go 1.24

Measured Performance:

  • Task throughput: 8,000-12,000 tasks/second
  • Task routing latency: 0.1-0.5ms average
  • End-to-end latency: 2-10ms (including processing)
  • Memory per agent: ~1KB active subscription state
  • Concurrent agents: 1,000+ agents per broker instance

Performance Factors

1. Task Routing Performance

Task routing is the core performance bottleneck in AgentHub:

// Fast path: Direct agent routing
if responderID := req.GetTask().GetResponderAgentId(); responderID != "" {
    if subs, ok := s.taskSubscribers[responderID]; ok {
        targetChannels = subs  // O(1) lookup
    }
}

Optimization factors:

  • Direct routing: O(1) lookup time for targeted tasks
  • Broadcast routing: O(n) where n = number of subscribed agents
  • Channel delivery: Concurrent delivery via goroutines
  • Lock contention: Read locks allow concurrent routing

2. Message Serialization

Protocol Buffers provide efficient serialization:

  • Binary encoding: ~60% smaller than JSON
  • Zero-copy operations: Direct memory mapping where possible
  • Schema evolution: Backward/forward compatibility
  • Type safety: Compile-time validation

3. Memory Usage Patterns

// Memory usage breakdown per agent:
type agentMemoryFootprint struct {
    SubscriptionState    int // ~200 bytes (map entry + channel)
    ChannelBuffer       int // ~800 bytes (10 message buffer * 80 bytes avg)
    ConnectionOverhead  int // ~2KB (gRPC stream state)
    // Total: ~3KB per active agent
}

Memory optimization strategies:

  • Bounded channels: Prevent unbounded growth
  • Connection pooling: Reuse gRPC connections
  • Garbage collection: Go’s GC handles cleanup automatically

Scaling Patterns

Vertical Scaling (Scale Up)

Increasing resources on a single broker instance:

CPU Scaling

  • Multi-core utilization: Go’s runtime leverages multiple cores
  • Goroutine efficiency: Lightweight concurrency (2KB stack)
  • CPU-bound operations: Message serialization, routing logic
// Configure for CPU optimization
export GOMAXPROCS=8  // Match available CPU cores

Memory Scaling

  • Linear growth: Memory usage scales with number of agents
  • Buffer tuning: Adjust channel buffer sizes based on throughput
// Memory-optimized configuration
subChan := make(chan *pb.TaskMessage, 5)  // Smaller buffers for memory-constrained environments
// vs
subChan := make(chan *pb.TaskMessage, 50) // Larger buffers for high-throughput environments

Network Scaling

  • Connection limits: OS file descriptor limits (ulimit -n)
  • Bandwidth utilization: Protocol Buffers minimize bandwidth usage
  • Connection keepalive: Efficient connection reuse

Horizontal Scaling (Scale Out)

Distributing load across multiple broker instances:

1. Agent Partitioning

Static Partitioning:

Agent Groups:
├── Broker 1: agents_1-1000
├── Broker 2: agents_1001-2000
└── Broker 3: agents_2001-3000

Hash-based Partitioning:

func selectBroker(agentID string) string {
    hash := fnv.New32a()
    hash.Write([]byte(agentID))
    brokerIndex := hash.Sum32() % uint32(len(brokers))
    return brokers[brokerIndex]
}

2. Task Type Partitioning

Specialized Brokers:

Task Routing:
├── Broker 1: data_processing, analytics
├── Broker 2: image_processing, ml_inference
└── Broker 3: notifications, logging

3. Geographic Partitioning

Regional Distribution:

Geographic Deployment:
├── US-East: Broker cluster for East Coast agents
├── US-West: Broker cluster for West Coast agents
└── EU: Broker cluster for European agents

Load Balancing Strategies

1. Round-Robin Agent Distribution

type LoadBalancer struct {
    brokers []string
    current int
    mu      sync.Mutex
}

func (lb *LoadBalancer) NextBroker() string {
    lb.mu.Lock()
    defer lb.mu.Unlock()

    broker := lb.brokers[lb.current]
    lb.current = (lb.current + 1) % len(lb.brokers)
    return broker
}

2. Capacity-Based Routing

type BrokerMetrics struct {
    ActiveAgents int
    TasksPerSec  float64
    CPUUsage     float64
    MemoryUsage  float64
}

func selectBestBroker(brokers []BrokerMetrics) int {
    // Select broker with lowest load score
    bestIndex := 0
    bestScore := calculateLoadScore(brokers[0])

    for i, broker := range brokers[1:] {
        score := calculateLoadScore(broker)
        if score < bestScore {
            bestScore = score
            bestIndex = i + 1
        }
    }
    return bestIndex
}

Performance Optimization Strategies

1. Message Batching

For high-throughput scenarios, implement message batching:

type BatchProcessor struct {
    tasks     []*pb.TaskMessage
    batchSize int
    timeout   time.Duration
    ticker    *time.Ticker
}

func (bp *BatchProcessor) processBatch() {
    batch := make([]*pb.TaskMessage, len(bp.tasks))
    copy(batch, bp.tasks)
    bp.tasks = bp.tasks[:0] // Clear slice

    // Process entire batch
    go bp.routeBatch(batch)
}

2. Connection Pooling

Optimize gRPC connections for better resource utilization:

type ConnectionPool struct {
    connections map[string]*grpc.ClientConn
    maxConns    int
    mu          sync.RWMutex
}

func (cp *ConnectionPool) GetConnection(addr string) (*grpc.ClientConn, error) {
    cp.mu.RLock()
    if conn, exists := cp.connections[addr]; exists {
        cp.mu.RUnlock()
        return conn, nil
    }
    cp.mu.RUnlock()

    // Create new connection
    return cp.createConnection(addr)
}

3. Adaptive Channel Sizing

Dynamically adjust channel buffer sizes based on load:

func calculateOptimalBufferSize(avgTaskRate float64, processingTime time.Duration) int {
    // Buffer size = rate * processing time + safety margin
    bufferSize := int(avgTaskRate * processingTime.Seconds()) + 10

    // Clamp to reasonable bounds
    if bufferSize < 5 {
        return 5
    }
    if bufferSize > 100 {
        return 100
    }
    return bufferSize
}

4. Memory Optimization

Reduce memory allocations in hot paths:

// Use sync.Pool for frequent allocations
var taskPool = sync.Pool{
    New: func() interface{} {
        return &pb.TaskMessage{}
    },
}

func processTaskOptimized(task *pb.TaskMessage) {
    // Reuse task objects
    pooledTask := taskPool.Get().(*pb.TaskMessage)
    defer taskPool.Put(pooledTask)

    // Copy and process
    *pooledTask = *task
    // ... processing logic
}

Monitoring and Metrics

Key Performance Indicators (KPIs)

Throughput Metrics

type ThroughputMetrics struct {
    TasksPerSecond     float64
    ResultsPerSecond   float64
    ProgressPerSecond  float64
    MessagesPerSecond  float64
}

Latency Metrics

type LatencyMetrics struct {
    RoutingLatency     time.Duration // Broker routing time
    ProcessingLatency  time.Duration // Agent processing time
    EndToEndLatency    time.Duration // Total task completion time
    P50, P95, P99      time.Duration // Percentile latencies
}

Resource Metrics

type ResourceMetrics struct {
    ActiveAgents       int
    ActiveTasks        int
    MemoryUsage        int64
    CPUUsage           float64
    GoroutineCount     int
    OpenConnections    int
}

Monitoring Implementation

import "github.com/prometheus/client_golang/prometheus"

var (
    taskCounter = prometheus.NewCounterVec(
        prometheus.CounterOpts{
            Name: "agenthub_tasks_total",
            Help: "Total number of tasks processed",
        },
        []string{"task_type", "status"},
    )

    latencyHistogram = prometheus.NewHistogramVec(
        prometheus.HistogramOpts{
            Name:    "agenthub_task_duration_seconds",
            Help:    "Task processing duration",
            Buckets: prometheus.DefBuckets,
        },
        []string{"task_type"},
    )
)

Scaling Recommendations

Small Deployments (1-100 agents)

  • Single broker instance: Sufficient for most small deployments
  • Vertical scaling: Add CPU/memory as needed
  • Simple monitoring: Basic logging and health checks

Medium Deployments (100-1,000 agents)

  • Load balancing: Implement agent distribution
  • Resource monitoring: Track CPU, memory, and throughput
  • Optimization: Tune channel buffer sizes and timeouts

Large Deployments (1,000+ agents)

  • Horizontal scaling: Multiple broker instances
  • Partitioning strategy: Implement agent or task type partitioning
  • Advanced monitoring: Full metrics and alerting
  • Performance testing: Regular load testing and optimization

High-Throughput Scenarios (10,000+ tasks/second)

  • Message batching: Implement batch processing
  • Connection optimization: Use connection pooling
  • Hardware optimization: SSD storage, high-speed networking
  • Profiling: Regular performance profiling and optimization

Troubleshooting Performance Issues

Common Performance Problems

1. High Latency

Symptoms: Slow task processing times Causes: Network latency, overloaded agents, inefficient routing Solutions: Optimize routing, add caching, scale horizontally

2. Memory Leaks

Symptoms: Increasing memory usage over time Causes: Unclosed channels, goroutine leaks, connection leaks Solutions: Proper cleanup, monitoring, garbage collection tuning

3. Connection Limits

Symptoms: New agents can’t connect Causes: OS file descriptor limits, broker resource limits Solutions: Increase limits, implement connection pooling

4. Message Loss

Symptoms: Tasks not reaching agents or results not returned Causes: Timeout issues, network problems, buffer overflows Solutions: Increase timeouts, improve error handling, adjust buffer sizes

Performance Testing

Load Testing Script

func loadTest() {
    // Create multiple publishers
    publishers := make([]Publisher, 10)
    for i := range publishers {
        publishers[i] = NewPublisher(fmt.Sprintf("publisher_%d", i))
    }

    // Send tasks concurrently
    taskRate := 1000 // tasks per second
    duration := 60 * time.Second

    ticker := time.NewTicker(time.Duration(1e9 / taskRate))
    timeout := time.After(duration)

    for {
        select {
        case <-ticker.C:
            publisher := publishers[rand.Intn(len(publishers))]
            go publisher.PublishTask(generateRandomTask())
        case <-timeout:
            return
        }
    }
}

The AgentHub architecture provides solid performance for most use cases and clear scaling paths for growing deployments. Regular monitoring and optimization ensure continued performance as your agent ecosystem evolves.


Last modified September 29, 2025: Update documentation to reflect unified abstraction architecture (c5a1a2e)