HeliosDB Edge AI Deployment Guide

HeliosDB Edge AI Deployment Guide

F5.3.2 Edge AI Processing

Version: 1.0 Date: November 2, 2025 Status: Production Ready

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Executive Summary

This guide provides comprehensive deployment instructions for HeliosDB Edge AI Processing (F5.3.2), enabling distributed AI inference at the edge with <50ms latency, federated learning integration, and intelligent resource management.

Key Capabilities

• Multi-Runtime Support: ONNX, TensorFlow Lite, PyTorch Mobile, Core ML
• Intelligent Scheduling: Device-aware task scheduling with load balancing
• Resource Monitoring: Real-time CPU/GPU/memory tracking with alerts
• Federated Learning: Privacy-preserving model aggregation
• High Performance: <50ms CPU latency, <10ms GPU latency
• Throughput: 1K+ inferences/second per edge node

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Architecture Overview

┌─────────────────────────────────────────────────────────────┐
│                    Edge AI Platform                         │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐     │
│  │  Runtime     │  │  Scheduler   │  │   Monitor    │     │
│  │              │  │              │  │              │     │
│  │ ONNX/TFLite  │  │ Load Balance │  │ CPU/GPU/Mem  │     │
│  └──────────────┘  └──────────────┘  └──────────────┘     │
│                                                             │
│  ┌──────────────┐  ┌──────────────┐  ┌──────────────┐     │
│  │ Model Cache  │  │  Inference   │  │  Federated   │     │
│  │              │  │  Cache       │  │  Learning    │     │
│  │ LRU/LFU/TTL  │  │              │  │              │     │
│  └──────────────┘  └──────────────┘  └──────────────┘     │
│                                                             │
└─────────────────────────────────────────────────────────────┘

---

Prerequisites

System Requirements

Minimum (Development)

• CPU: 4 cores, 2.0 GHz
• RAM: 8 GB
• Disk: 20 GB SSD
• OS: Linux (Ubuntu 20.04+), macOS 11+, Windows 10+

Recommended (Production)

• CPU: 8+ cores, 3.0 GHz (x86_64 or ARM64)
• RAM: 16 GB+
• Disk: 100 GB NVMe SSD
• GPU: Optional but recommended (NVIDIA, AMD, or Apple Silicon)
• OS: Linux (Ubuntu 22.04 LTS)

Software Dependencies

# Rust toolchain
rustc 1.70.0+

# Optional: GPU support
CUDA 11.8+ (NVIDIA)
ROCm 5.0+ (AMD)
Metal (Apple Silicon)

# System libraries
libssl-dev
build-essential
pkg-config

---

Installation

1. Add to Cargo.toml

[dependencies]
heliosdb-edge-ai = "5.3.2"
heliosdb-federated-learning = "5.2.2"  # For federated features
tokio = { version = "1.48", features = ["full"] }

2. Build from Source

# Clone repository
git clone https://github.com/heliosdb/heliosdb.git
cd heliosdb

# Build edge-ai package
cargo build --package heliosdb-edge-ai --release

# Run tests
cargo test --package heliosdb-edge-ai --release

3. Docker Deployment

FROM rust:1.70 as builder

WORKDIR /app
COPY . .

RUN cargo build --package heliosdb-edge-ai --release

FROM debian:bookworm-slim

COPY --from=builder /app/target/release/libheliosdb_edge_ai.so /usr/local/lib/

# Install runtime dependencies
RUN apt-get update && apt-get install -y \
    libssl3 \
    && rm -rf /var/lib/apt/lists/*

EXPOSE 8080

CMD ["heliosdb-edge-ai"]

---

Configuration

Basic Configuration

use heliosdb_edge_ai::*;

#[tokio::main]
async fn main() {
    // Configure inference engine
    let inference_config = InferenceEngineConfig {
        max_concurrent_inferences: 10,
        model_cache_size_mb: 500,
        inference_cache_size_mb: 1000,
        default_timeout_ms: 50,
        enable_batching: true,
        max_batch_size: 32,
        ..Default::default()
    };

    // Configure scheduler
    let scheduler_config = SchedulerConfig {
        strategy: SchedulingStrategy::LoadBalanced,
        max_queue_size: 1000,
        max_concurrent_tasks: 10,
        ..Default::default()
    };

    // Configure resource monitor
    let monitor_config = MonitorConfig {
        sampling_interval_ms: 1000,
        history_size: 3600,
        enable_gpu_monitoring: true,
        ..Default::default()
    };

    // Create edge AI runtime
    let runtime = EdgeAIRuntime::new();
    let scheduler = InferenceScheduler::new(scheduler_config, runtime.capabilities().clone());
    let monitor = ResourceMonitor::new(monitor_config);

    println!("Edge AI Platform initialized successfully!");
}

Advanced Configuration

// GPU acceleration
let runtime_config = RuntimeConfig {
    backend: RuntimeBackend::ONNX,
    device: DeviceType::GPU,
    num_threads: 8,
    optimization_level: 3,
    ..Default::default()
};

// Federated learning
let federated_config = FederatedConfig {
    aggregation_strategy: AggregationStrategy::FederatedAveraging,
    differential_privacy: true,
    epsilon: 3.0,
    delta: 1e-5,
    clip_norm: 1.0,
    min_participants: 5,
    ..Default::default()
};

let aggregator = FederatedAggregator::new(federated_config);

---

Deployment Scenarios

Scenario 1: Edge Inference Server

use heliosdb_edge_ai::*;
use std::sync::Arc;

#[tokio::main]
async fn main() {
    // Initialize components
    let engine = Arc::new(InferenceEngine::new(InferenceEngineConfig::default()));
    let monitor = Arc::new(ResourceMonitor::new(MonitorConfig::default()));

    // Start monitoring
    let monitor_clone = Arc::clone(&monitor);
    tokio::spawn(async move {
        loop {
            monitor_clone.sample();
            tokio::time::sleep(tokio::time::Duration::from_secs(1)).await;
        }
    });

    // Serve inference requests
    loop {
        // Check system health
        if !monitor.is_healthy() {
            eprintln!("System unhealthy, throttling requests");
            tokio::time::sleep(tokio::time::Duration::from_secs(1)).await;
            continue;
        }

        // Process inference request
        let inputs = /* receive from network */;
        let request = InferenceRequest::new("model_id".to_string(), inputs);

        match engine.infer(request).await {
            Ok(response) => {
                println!("Inference completed in {}ms", response.latency_ms);
            }
            Err(e) => eprintln!("Inference error: {}", e),
        }
    }
}

Scenario 2: Federated Learning Node

use heliosdb_edge_ai::*;

#[tokio::main]
async fn main() {
    let runtime = EdgeAIRuntime::new();
    let aggregator = FederatedAggregator::new(FederatedConfig::default());

    // Load model
    let model = /* load model */;
    let session = runtime.create_session(
        "model_id".to_string(),
        Arc::new(model),
        None,
    ).unwrap();

    // Local training loop
    for epoch in 0..10 {
        // Train on local data
        let local_gradients = /* compute gradients */;

        // Create model update
        let update = ModelUpdate::new(
            "model_id".to_string(),
            "v1".to_string(),
            local_gradients,
            1000, // num samples
        );

        // Submit to aggregator
        aggregator.submit_update(update).unwrap();
    }

    // Aggregate (coordinator only)
    if is_coordinator() {
        let result = aggregator.aggregate(1).unwrap();
        println!("Round 1 aggregation: {} participants", result.num_participants);
    }
}

Scenario 3: Multi-Model Serving

use heliosdb_edge_ai::*;
use std::sync::Arc;

#[tokio::main]
async fn main() {
    let runtime = EdgeAIRuntime::new();
    let registry = Arc::new(ModelRegistry::new());

    // Register multiple models
    let models = vec!["resnet50", "mobilenet", "efficientnet"];

    for model_name in models {
        let version = ModelVersion::new(
            model_name.to_string(),
            "v1".to_string(),
            1,
        );

        let model = /* load model */;
        let deployment = DeploymentConfig::new(version.clone());

        registry.register(version, model, deployment).await.unwrap();
    }

    // Create sessions for each model
    for model_name in &models {
        let version = ModelVersion::new(
            model_name.to_string(),
            "v1".to_string(),
            1,
        );

        let model = registry.get_model(&version).await.unwrap();
        runtime.create_session(
            model_name.to_string(),
            model,
            None,
        ).unwrap();
    }

    println!("Registered {} models", runtime.session_count());
}

---

Performance Tuning

Latency Optimization

// Minimize latency
let config = InferenceEngineConfig {
    max_concurrent_inferences: 1,  // Single-threaded
    model_cache_size_mb: 1000,     // Large cache
    inference_cache_enabled: true,  // Enable result caching
    default_timeout_ms: 10,         // Strict timeout
    ..Default::default()
};

// Use GPU for large models
let runtime_config = RuntimeConfig {
    device: DeviceType::GPU,
    optimization_level: 3,
    ..Default::default()
};

Throughput Optimization

// Maximize throughput
let config = InferenceEngineConfig {
    max_concurrent_inferences: 100,  // High concurrency
    enable_batching: true,           // Batch requests
    max_batch_size: 32,              // Large batches
    batch_timeout_ms: 5,             // Quick batching
    ..Default::default()
};

// Use scheduler for load balancing
let scheduler_config = SchedulerConfig {
    strategy: SchedulingStrategy::LoadBalanced,
    max_concurrent_tasks: 50,
    ..Default::default()
};

Memory Optimization

// Minimize memory footprint
let config = InferenceEngineConfig {
    model_cache_size_mb: 100,       // Small cache
    inference_cache_size_mb: 200,   // Limit result cache
    ..Default::default()
};

// Use model quantization
let model = /* load quantized INT8 model */;

---

Monitoring & Observability

Metrics Collection

use heliosdb_edge_ai::*;

fn collect_metrics(
    engine: &InferenceEngine,
    scheduler: &InferenceScheduler,
    monitor: &ResourceMonitor,
) {
    // Inference metrics
    let stats = engine.get_stats();
    println!("Total requests: {}", stats.total_requests);
    println!("Cache hit rate: {:.2}%",
        stats.cache_hits as f64 / stats.total_requests as f64 * 100.0);
    println!("Avg latency: {:.2}ms", stats.avg_latency_ms);
    println!("P95 latency: {:.2}ms", stats.p95_latency_ms);
    println!("P99 latency: {:.2}ms", stats.p99_latency_ms);

    // Scheduler metrics
    let sched_stats = scheduler.stats();
    println!("Queue length: {}", sched_stats.queue_length);
    println!("Active tasks: {}", sched_stats.active_tasks);
    println!("Avg wait time: {:.2}ms", sched_stats.avg_wait_time_ms);

    // Resource metrics
    let resource_stats = monitor.stats();
    println!("Avg CPU: {:.1}%", resource_stats.avg_cpu_percent);
    println!("Peak CPU: {:.1}%", resource_stats.peak_cpu_percent);
    println!("Avg Memory: {:.1}%", resource_stats.avg_memory_percent);
}

Health Checks

fn health_check(monitor: &ResourceMonitor) -> bool {
    monitor.sample();

    let snapshot = monitor.current();

    // Check critical thresholds
    if snapshot.is_critical() {
        eprintln!("CRITICAL: System resources critically high!");
        return false;
    }

    // Check high load
    if snapshot.is_high_load() {
        eprintln!("WARNING: System under high load");
    }

    // Check GPU temperature (if available)
    if let Some(temp) = snapshot.temperature_celsius {
        if temp > 85.0 {
            eprintln!("WARNING: High GPU temperature: {}°C", temp);
        }
    }

    true
}

Alert Handling

fn handle_alerts(monitor: &ResourceMonitor) {
    let alerts = monitor.alerts(10);  // Get last 10 alerts

    for alert in alerts {
        match alert.severity {
            AlertSeverity::Critical => {
                eprintln!("CRITICAL ALERT: {}", alert.message);
                // Take corrective action
                // - Reject new requests
                // - Scale up resources
                // - Alert ops team
            }
            AlertSeverity::Warning => {
                eprintln!("WARNING: {}", alert.message);
                // Monitor closely
            }
            AlertSeverity::Info => {
                println!("INFO: {}", alert.message);
            }
        }
    }
}

---

Troubleshooting

Common Issues

1. High Latency

Symptoms: Inference latency >100ms Causes:

• CPU overload
• Model too large
• Cache disabled

Solutions:

// Enable caching
let config = InferenceEngineConfig {
    inference_cache_enabled: true,
    model_cache_size_mb: 1000,
    ..Default::default()
};

// Use GPU acceleration
let runtime_config = RuntimeConfig {
    device: DeviceType::GPU,
    ..Default::default()
};

// Reduce concurrent requests
let config = InferenceEngineConfig {
    max_concurrent_inferences: 5,
    ..Default::default()
};

2. Out of Memory

Symptoms: OOM errors, high memory usage Solutions:

// Reduce cache sizes
let config = InferenceEngineConfig {
    model_cache_size_mb: 100,
    inference_cache_size_mb: 200,
    ..Default::default()
};

// Limit queue size
let scheduler_config = SchedulerConfig {
    max_queue_size: 100,
    ..Default::default()
};

// Use model quantization (INT8)

3. Low Throughput

Symptoms: <100 inferences/sec Solutions:

// Enable batching
let config = InferenceEngineConfig {
    enable_batching: true,
    max_batch_size: 32,
    max_concurrent_inferences: 50,
    ..Default::default()
};

// Use load balancing
let scheduler_config = SchedulerConfig {
    strategy: SchedulingStrategy::LoadBalanced,
    ..Default::default()
};

---

Production Checklist

Pre-Deployment

• Test on production-like hardware
• Benchmark latency and throughput
• Configure resource limits
• Set up monitoring and alerts
• Plan capacity (CPU, RAM, GPU)
• Document runbook

Deployment

• Deploy with health checks
• Configure auto-scaling
• Set up log aggregation
• Enable metrics collection
• Configure backup models
• Test failover scenarios

Post-Deployment

• Monitor latency metrics
• Track resource utilization
• Review error logs
• Validate model accuracy
• Optimize based on metrics
• Document lessons learned

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Security Considerations

Model Security

• Use encrypted model storage
• Validate model checksums
• Implement access controls
• Audit model deployments

Data Privacy

• Enable differential privacy for federated learning
• Configure appropriate epsilon/delta values
• Implement gradient clipping
• Use secure aggregation

Network Security

• Use TLS for all communication
• Implement authentication/authorization
• Rate limit inference requests
• Monitor for anomalies

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Support & Resources

Documentation

• API Reference
• Architecture Guide
• Federated Learning Integration

Community

• GitHub: https://github.com/heliosdb/heliosdb
• Discord: https://discord.gg/heliosdb
• Forum: https://forum.heliosdb.com

Commercial Support

• Enterprise support available
• Custom deployment assistance
• Performance optimization consulting
• Training and workshops

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Document Version: 1.0 Last Updated: November 2, 2025 Maintained By: HeliosDB Edge AI Team