Edge Computing with WebAssembly: Lightweight Computing at Scale

Edge Computing with WebAssembly: Lightweight Computing at Scale

Edge computing with WebAssembly represents the perfect convergence of performance, portability, and security for distributed applications. WASM's lightweight nature, combined with its near-native performance and strong sandboxing, makes it ideal for edge deployments where resources are constrained and latency is critical.

Table of Contents

1. Edge Computing Fundamentals
2. Why WASM for Edge Computing?
3. Edge Platforms and Providers
4. Building Edge Applications
5. CDN Edge Functions
6. IoT and Embedded Systems
7. Edge Orchestration Strategies
8. Real-time Data Processing
9. Security at the Edge
10. Performance Optimization

Edge Computing Fundamentals

The Edge Computing Landscape

Edge Computing Hierarchy:
┌─────────────────────────────────────────────────┐
│                  Cloud (Central)                │
│               • Heavy computation               │
│               • Data warehousing                │
│               • ML model training               │
└─────────────────┬───────────────────────────────┘
                  │
┌─────────────────▼───────────────────────────────┐
│              Regional Edge                      │
│            • Content caching                    │
│            • API gateways                       │
│            • Stream processing                  │
└─────────────────┬───────────────────────────────┘
                  │
┌─────────────────▼───────────────────────────────┐
│              Local Edge                         │
│            • Real-time processing               │
│            • Device management                  │
│            • Local storage                      │
└─────────────────┬───────────────────────────────┘
                  │
┌─────────────────▼───────────────────────────────┐
│              Device Edge                        │
│            • Sensor processing                  │
│            • Immediate response                 │
│            • Offline capability                 │
└─────────────────────────────────────────────────┘

Edge Computing Benefits

🚀 Ultra-Low Latency

• Sub-10ms response times
• Reduced network hops
• Local processing capabilities
• Real-time decision making

⚡ Bandwidth Optimization

• Process data where it's generated
• Reduce data transmission costs
• Filter and aggregate before sending to cloud
• Efficient use of network resources

🔒 Data Privacy and Compliance

• Keep sensitive data local
• Meet regulatory requirements
• Reduce data exposure risks
• Enable GDPR compliance

🌐 Improved Availability

• Offline-first architectures
• Resilience to network failures
• Distributed failure domains
• Autonomous operation capability

Why WASM for Edge Computing?

Perfect Match for Edge Constraints

// Edge-optimized WASM module
#![no_std]
#![no_main]

use core::panic::PanicInfo;

// Minimal memory footprint
#[global_allocator]
static ALLOC: linked_list_allocator::LockedHeap = 
    linked_list_allocator::LockedHeap::empty();

// Fast startup for edge functions
#[no_mangle]
pub extern "C" fn _start() {
    // Initialize with minimal overhead
    init_heap();
    main();
}

// Process sensor data at the edge
#[no_mangle]
pub extern "C" fn process_sensor_data(ptr: *const u8, len: usize) -> u32 {
    let data = unsafe { core::slice::from_raw_parts(ptr, len) };
    
    // Real-time processing without heap allocation
    let mut sum = 0u32;
    let mut max = 0u8;
    
    for &value in data {
        sum = sum.wrapping_add(value as u32);
        if value > max {
            max = value;
        }
    }
    
    // Return anomaly score
    if max > 200 || sum > 10000 {
        1 // Anomaly detected
    } else {
        0 // Normal operation
    }
}

#[panic_handler]
fn panic(_info: &PanicInfo) -> ! {
    loop {}
}

fn init_heap() {
    // Initialize minimal heap for edge deployment
    unsafe {
        ALLOC.lock().init(heap_start(), HEAP_SIZE);
    }
}

WASM Edge Advantages

Aspect	Traditional Edge	WASM Edge	Improvement
Cold Start	100-500ms	<5ms	100x faster
Memory Usage	50-200MB	1-10MB	20x smaller
Deployment Size	10-100MB	100KB-2MB	50x smaller
Startup Energy	High	Minimal	10x more efficient
Security Isolation	Process-based	Memory-safe	Stronger guarantees
Platform Independence	Architecture-specific	Universal	Complete portability

Edge Platforms and Providers

Cloudflare Workers

// Cloudflare Workers with WASM
export default {
  async fetch(request, env, ctx) {
    // Load WASM module
    const wasmModule = new WebAssembly.Module(env.WASM_BINARY);
    const wasmInstance = new WebAssembly.Instance(wasmModule);
    
    // Process request with WASM
    const url = new URL(request.url);
    const path = url.pathname;
    
    if (path === '/api/process') {
      const data = await request.arrayBuffer();
      const result = wasmInstance.exports.process_data(
        new Uint8Array(data)
      );
      
      return new Response(JSON.stringify({ result }), {
        headers: { 'Content-Type': 'application/json' }
      });
    }
    
    // Geolocation-based routing
    const country = request.cf.country;
    const colo = request.cf.colo;
    
    return new Response(`Hello from ${colo}, ${country}!`);
  }
};

Fastly Compute@Edge

// Fastly Compute@Edge with WASM
use fastly::http::{Method, StatusCode};
use fastly::{Error, Request, Response};

#[fastly::main]
fn main(mut req: Request) -> Result<Response, Error> {
    match req.get_method() {
        &Method::GET => handle_get(req),
        &Method::POST => handle_post(req),
        _ => Ok(Response::from_status(StatusCode::METHOD_NOT_ALLOWED)),
    }
}

fn handle_get(req: Request) -> Result<Response, Error> {
    let path = req.get_path();
    
    match path {
        "/health" => {
            Ok(Response::from_status(StatusCode::OK)
                .with_body_text_plain("OK"))
        }
        "/api/data" => {
            // Edge data processing
            let client_geo = req.get_client_geo_info()?;
            let response_data = format!(
                r#"{{"location": "{}", "latency": "2ms"}}"#,
                client_geo.city().unwrap_or("Unknown")
            );
            
            Ok(Response::from_status(StatusCode::OK)
                .with_body_text_plain(&response_data)
                .with_header("Content-Type", "application/json"))
        }
        _ => Ok(Response::from_status(StatusCode::NOT_FOUND)),
    }
}

fn handle_post(mut req: Request) -> Result<Response, Error> {
    let body = req.take_body();
    let data = body.into_bytes();
    
    // Process data at the edge
    let processed = process_edge_data(&data);
    
    Ok(Response::from_status(StatusCode::OK)
        .with_body_text_plain(&processed))
}

fn process_edge_data(data: &[u8]) -> String {
    // Real-time data transformation
    let checksum: u32 = data.iter().map(|&b| b as u32).sum();
    format!("Processed {} bytes, checksum: {}", data.len(), checksum)
}

AWS Lambda@Edge with WASM

// Lambda@Edge with WASM support
const fs = require('fs');
const wasmModule = fs.readFileSync('./processor.wasm');

exports.handler = async (event) => {
    const { Records } = event;
    const request = Records[0].cf.request;
    
    // Load WASM module
    const wasm = await WebAssembly.instantiate(wasmModule);
    const { process_request } = wasm.instance.exports;
    
    // Extract request data
    const uri = request.uri;
    const method = request.method;
    const headers = request.headers;
    
    // Process at edge location
    const result = process_request(
        Buffer.from(uri).byteOffset,
        uri.length
    );
    
    if (result === 1) {
        // Redirect to cache
        return {
            status: '302',
            statusDescription: 'Found',
            headers: {
                location: [{
                    key: 'Location',
                    value: 'https://cache.example.com' + uri
                }]
            }
        };
    }
    
    // Continue to origin
    return request;
};

Edge Runtime Comparison

Platform	Runtime	Cold Start	Memory Limit	CPU Time	Use Cases
Cloudflare Workers	V8 + WASM	<1ms	128MB	50ms/100ms	API, Auth, Transform
Fastly Compute@Edge	Wasmtime	<1ms	50MB	50ms	CDN, Security, Routing
AWS Lambda@Edge	Node.js	50-100ms	128MB	5s	A/B Testing, Headers
Vercel Edge	V8 + WASM	<1ms	64MB	30s	SSR, API Routes
Deno Deploy	V8 + WASM	<1ms	512MB	Unlimited	Full Applications

Building Edge Applications

Image Processing at the Edge

// Edge image processing with WASM
use image::{ImageBuffer, Rgb, RgbImage};
use std::io::Cursor;

#[no_mangle]
pub extern "C" fn resize_image(
    input_ptr: *const u8,
    input_len: usize,
    width: u32,
    height: u32,
    output_ptr: *mut u8,
    output_len: *mut usize,
) -> i32 {
    let input_data = unsafe {
        std::slice::from_raw_parts(input_ptr, input_len)
    };
    
    // Load image
    let img = match image::load_from_memory(input_data) {
        Ok(img) => img,
        Err(_) => return -1,
    };
    
    // Resize at edge
    let resized = img.resize(
        width,
        height,
        image::imageops::FilterType::Lanczos3
    );
    
    // Encode result
    let mut output_buffer = Vec::new();
    let mut cursor = Cursor::new(&mut output_buffer);
    
    match resized.write_to(&mut cursor, image::ImageOutputFormat::Jpeg(85)) {
        Ok(_) => {
            let output_slice = unsafe {
                std::slice::from_raw_parts_mut(output_ptr, output_buffer.len())
            };
            
            output_slice.copy_from_slice(&output_buffer);
            unsafe { *output_len = output_buffer.len(); }
            
            0 // Success
        }
        Err(_) => -2,
    }
}

// Smart cropping based on content analysis
#[no_mangle]
pub extern "C" fn smart_crop(
    input_ptr: *const u8,
    input_len: usize,
    aspect_ratio: f32,
) -> *mut u8 {
    let input_data = unsafe {
        std::slice::from_raw_parts(input_ptr, input_len)
    };
    
    let img = image::load_from_memory(input_data).unwrap();
    let (orig_width, orig_height) = img.dimensions();
    
    // Calculate optimal crop region using edge detection
    let crop_region = detect_focus_region(&img);
    
    let cropped = img.crop_imm(
        crop_region.x,
        crop_region.y,
        crop_region.width,
        crop_region.height,
    );
    
    // Return processed image pointer
    Box::into_raw(Box::new(cropped)) as *mut u8
}

struct CropRegion {
    x: u32,
    y: u32,
    width: u32,
    height: u32,
}

fn detect_focus_region(img: &image::DynamicImage) -> CropRegion {
    // Simplified focus detection algorithm
    let (width, height) = img.dimensions();
    
    // Use rule of thirds for smart cropping
    CropRegion {
        x: width / 6,
        y: height / 6,
        width: (width * 2) / 3,
        height: (height * 2) / 3,
    }
}

Real-time Analytics at Edge

// Edge analytics with WASM
class EdgeAnalytics {
    constructor(wasmModule) {
        this.wasm = wasmModule;
        this.sessionStore = new Map();
    }
    
    async processEvent(event) {
        const { userId, eventType, timestamp, data } = event;
        
        // Use WASM for fast event processing
        const processedEvent = this.wasm.instance.exports.process_event(
            this.stringToWasmPtr(JSON.stringify(event))
        );
        
        // Real-time aggregation
        this.updateSession(userId, processedEvent);
        
        // Anomaly detection at edge
        const anomaly = this.detectAnomaly(userId, processedEvent);
        
        if (anomaly) {
            await this.sendAlert(userId, anomaly);
        }
        
        return processedEvent;
    }
    
    updateSession(userId, event) {
        if (!this.sessionStore.has(userId)) {
            this.sessionStore.set(userId, {
                events: [],
                startTime: Date.now(),
                totalEvents: 0
            });
        }
        
        const session = this.sessionStore.get(userId);
        session.events.push(event);
        session.totalEvents++;
        
        // Keep only recent events for memory efficiency
        if (session.events.length > 100) {
            session.events.shift();
        }
    }
    
    detectAnomaly(userId, event) {
        const session = this.sessionStore.get(userId);
        if (!session || session.totalEvents < 10) return null;
        
        // Use WASM for fast anomaly detection
        const anomalyScore = this.wasm.instance.exports.calculate_anomaly_score(
            session.totalEvents,
            event.duration || 0,
            event.errorCount || 0
        );
        
        return anomalyScore > 0.8 ? {
            score: anomalyScore,
            reason: 'unusual_behavior',
            timestamp: Date.now()
        } : null;
    }
    
    stringToWasmPtr(str) {
        const encoder = new TextEncoder();
        const bytes = encoder.encode(str);
        const ptr = this.wasm.instance.exports.allocate(bytes.length);
        const memory = new Uint8Array(this.wasm.instance.exports.memory.buffer);
        memory.set(bytes, ptr);
        return ptr;
    }
}

// Usage in edge function
export default {
    async fetch(request, env) {
        const analytics = new EdgeAnalytics(env.ANALYTICS_WASM);
        
        const event = {
            userId: request.headers.get('x-user-id'),
            eventType: 'page_view',
            timestamp: Date.now(),
            data: {
                url: request.url,
                userAgent: request.headers.get('user-agent'),
                country: request.cf.country
            }
        };
        
        await analytics.processEvent(event);
        
        return new Response('Event processed', { status: 200 });
    }
};

CDN Edge Functions

Content Optimization Pipeline

// CDN edge optimization with WASM
use serde::{Deserialize, Serialize};

#[derive(Deserialize)]
struct OptimizationRequest {
    content_type: String,
    content: Vec<u8>,
    client_hints: ClientHints,
}

#[derive(Deserialize)]
struct ClientHints {
    device_type: String, // mobile, tablet, desktop
    connection: String,  // slow-2g, 2g, 3g, 4g, 5g
    viewport_width: u32,
    supports_webp: bool,
    supports_avif: bool,
}

#[derive(Serialize)]
struct OptimizedContent {
    content: Vec<u8>,
    content_type: String,
    cache_ttl: u32,
    optimizations_applied: Vec<String>,
}

#[no_mangle]
pub extern "C" fn optimize_content(
    request_ptr: *const u8,
    request_len: usize,
) -> *mut u8 {
    let request_data = unsafe {
        std::slice::from_raw_parts(request_ptr, request_len)
    };
    
    let request: OptimizationRequest = 
        serde_json::from_slice(request_data).unwrap();
    
    let mut optimizations = Vec::new();
    let mut content = request.content;
    let mut content_type = request.content_type;
    
    // Device-specific optimization
    match request.client_hints.device_type.as_str() {
        "mobile" => {
            content = optimize_for_mobile(content, &mut optimizations);
        }
        "tablet" => {
            content = optimize_for_tablet(content, &mut optimizations);
        }
        _ => {
            content = optimize_for_desktop(content, &mut optimizations);
        }
    }
    
    // Connection-aware optimization
    match request.client_hints.connection.as_str() {
        "slow-2g" | "2g" => {
            content = aggressive_compression(content, &mut optimizations);
        }
        "3g" => {
            content = balanced_compression(content, &mut optimizations);
        }
        _ => {
            content = light_compression(content, &mut optimizations);
        }
    }
    
    // Format optimization
    if content_type.starts_with("image/") {
        if request.client_hints.supports_avif {
            content = convert_to_avif(content);
            content_type = "image/avif".to_string();
            optimizations.push("avif_conversion".to_string());
        } else if request.client_hints.supports_webp {
            content = convert_to_webp(content);
            content_type = "image/webp".to_string();
            optimizations.push("webp_conversion".to_string());
        }
    }
    
    let result = OptimizedContent {
        content,
        content_type,
        cache_ttl: calculate_cache_ttl(&optimizations),
        optimizations_applied: optimizations,
    };
    
    let serialized = serde_json::to_vec(&result).unwrap();
    Box::into_raw(serialized.into_boxed_slice()) as *mut u8
}

fn optimize_for_mobile(content: Vec<u8>, opts: &mut Vec<String>) -> Vec<u8> {
    opts.push("mobile_optimization".to_string());
    // Aggressive size reduction for mobile
    content.into_iter().step_by(2).collect() // Simplified
}

fn aggressive_compression(content: Vec<u8>, opts: &mut Vec<String>) -> Vec<u8> {
    opts.push("aggressive_compression".to_string());
    // Implement aggressive compression for slow connections
    content
}

fn convert_to_avif(content: Vec<u8>) -> Vec<u8> {
    // Convert image to AVIF format
    content // Simplified implementation
}

fn calculate_cache_ttl(optimizations: &[String]) -> u32 {
    // Dynamic cache TTL based on optimizations applied
    if optimizations.len() > 3 {
        3600 // 1 hour for heavily optimized content
    } else {
        1800 // 30 minutes for lightly optimized content
    }
}

A/B Testing at the Edge

// Edge A/B testing with WASM
class EdgeExperimentation {
    constructor(wasmModule) {
        this.wasm = wasmModule;
        this.experiments = new Map();
    }
    
    async getVariant(userId, experimentId, request) {
        // Use WASM for fast hash calculation
        const userHash = this.wasm.instance.exports.calculate_user_hash(
            this.stringToPtr(userId + experimentId)
        );
        
        // Determine variant based on experiment configuration
        const experiment = await this.getExperiment(experimentId);
        if (!experiment || !experiment.active) {
            return { variant: 'control', experiment: null };
        }
        
        const variantIndex = userHash % 100;
        const variant = this.selectVariant(variantIndex, experiment.variants);
        
        // Geolocation-based experiments
        const country = request.cf?.country || 'US';
        if (experiment.geoTargeting && 
            !experiment.geoTargeting.includes(country)) {
            return { variant: 'control', experiment: null };
        }
        
        // Device-based experiments
        const deviceType = this.detectDevice(request.headers.get('user-agent'));
        if (experiment.deviceTargeting && 
            !experiment.deviceTargeting.includes(deviceType)) {
            return { variant: 'control', experiment: null };
        }
        
        // Log experiment participation
        await this.logParticipation(userId, experimentId, variant);
        
        return { variant, experiment };
    }
    
    selectVariant(hash, variants) {
        let cumulative = 0;
        for (const variant of variants) {
            cumulative += variant.traffic;
            if (hash < cumulative) {
                return variant.name;
            }
        }
        return 'control';
    }
    
    async getExperiment(experimentId) {
        // Cache experiments at edge for performance
        if (this.experiments.has(experimentId)) {
            return this.experiments.get(experimentId);
        }
        
        // Fetch from edge KV store
        const experiment = await this.fetchExperimentConfig(experimentId);
        this.experiments.set(experimentId, experiment);
        
        return experiment;
    }
    
    detectDevice(userAgent) {
        if (!userAgent) return 'unknown';
        
        // Use WASM for fast device detection
        const deviceScore = this.wasm.instance.exports.detect_device_type(
            this.stringToPtr(userAgent)
        );
        
        if (deviceScore < 30) return 'mobile';
        if (deviceScore < 70) return 'tablet';
        return 'desktop';
    }
}

// Usage in edge worker
export default {
    async fetch(request, env) {
        const experiments = new EdgeExperimentation(env.EXPERIMENT_WASM);
        const userId = request.headers.get('x-user-id') || 
                      generateAnonymousId(request);
        
        // Check for active experiments
        const { variant, experiment } = await experiments.getVariant(
            userId, 
            'homepage_layout', 
            request
        );
        
        // Modify response based on variant
        if (variant === 'new_design') {
            return fetch(request.url.replace('//', '//new.'));
        }
        
        // Add experiment headers for client-side tracking
        const response = await fetch(request);
        const modifiedResponse = new Response(response.body, response);
        
        modifiedResponse.headers.set('X-Experiment-Id', 'homepage_layout');
        modifiedResponse.headers.set('X-Variant', variant);
        
        return modifiedResponse;
    }
};

IoT and Embedded Systems

Sensor Data Processing

// IoT sensor processing with WASM
#![no_std]
#![no_main]

use embedded_hal::digital::v2::OutputPin;
use nb::block;

// Sensor data structure
#[repr(C)]
struct SensorReading {
    timestamp: u64,
    temperature: f32,
    humidity: f32,
    pressure: f32,
    light_level: u16,
    motion: bool,
}

// Processed sensor data
#[repr(C)]
struct ProcessedData {
    alert_level: u8,      // 0=normal, 1=warning, 2=critical
    predictions: [f32; 3], // Next 3 readings prediction
    anomaly_score: f32,
    action_required: bool,
}

#[no_mangle]
pub extern "C" fn process_sensor_data(
    readings: *const SensorReading,
    count: usize,
    historical: *const f32,
    historical_count: usize,
) -> ProcessedData {
    let readings_slice = unsafe {
        core::slice::from_raw_parts(readings, count)
    };
    
    let historical_slice = unsafe {
        core::slice::from_raw_parts(historical, historical_count)
    };
    
    // Real-time anomaly detection
    let anomaly_score = detect_anomalies(readings_slice, historical_slice);
    
    // Predictive analytics at the edge
    let predictions = predict_next_readings(readings_slice);
    
    // Determine alert level
    let alert_level = calculate_alert_level(readings_slice, anomaly_score);
    
    // Decide if immediate action is needed
    let action_required = alert_level > 1 || anomaly_score > 0.8;
    
    ProcessedData {
        alert_level,
        predictions,
        anomaly_score,
        action_required,
    }
}

fn detect_anomalies(readings: &[SensorReading], historical: &[f32]) -> f32 {
    if readings.is_empty() || historical.is_empty() {
        return 0.0;
    }
    
    let latest = &readings[readings.len() - 1];
    
    // Calculate statistical deviation
    let mean = historical.iter().sum::<f32>() / historical.len() as f32;
    let variance = historical.iter()
        .map(|x| (x - mean).powi(2))
        .sum::<f32>() / historical.len() as f32;
    let std_dev = libm::sqrtf(variance);
    
    // Z-score for temperature anomaly
    let z_score = libm::fabsf(latest.temperature - mean) / std_dev;
    
    // Normalize to 0-1 scale
    if z_score > 3.0 { 1.0 } else { z_score / 3.0 }
}

fn predict_next_readings(readings: &[SensorReading]) -> [f32; 3] {
    if readings.len() < 3 {
        return [0.0; 3];
    }
    
    // Simple linear regression for prediction
    let n = readings.len().min(10); // Use last 10 readings
    let recent = &readings[readings.len() - n..];
    
    // Calculate trend for temperature
    let mut sum_x = 0.0;
    let mut sum_y = 0.0;
    let mut sum_xy = 0.0;
    let mut sum_x2 = 0.0;
    
    for (i, reading) in recent.iter().enumerate() {
        let x = i as f32;
        let y = reading.temperature;
        
        sum_x += x;
        sum_y += y;
        sum_xy += x * y;
        sum_x2 += x * x;
    }
    
    let n_f = n as f32;
    let slope = (n_f * sum_xy - sum_x * sum_y) / (n_f * sum_x2 - sum_x * sum_x);
    let intercept = (sum_y - slope * sum_x) / n_f;
    
    // Predict next 3 values
    let last_x = (n - 1) as f32;
    [
        slope * (last_x + 1.0) + intercept,
        slope * (last_x + 2.0) + intercept,
        slope * (last_x + 3.0) + intercept,
    ]
}

fn calculate_alert_level(readings: &[SensorReading], anomaly_score: f32) -> u8 {
    if readings.is_empty() {
        return 0;
    }
    
    let latest = &readings[readings.len() - 1];
    
    // Critical conditions
    if latest.temperature > 50.0 || latest.temperature < -10.0 {
        return 2;
    }
    
    if anomaly_score > 0.8 {
        return 2;
    }
    
    // Warning conditions
    if latest.temperature > 40.0 || latest.temperature < 0.0 {
        return 1;
    }
    
    if anomaly_score > 0.5 {
        return 1;
    }
    
    0 // Normal
}

// Emergency response at the edge
#[no_mangle]
pub extern "C" fn handle_emergency(alert_level: u8) -> u8 {
    match alert_level {
        2 => {
            // Critical: immediate action
            trigger_alarm();
            shutdown_systems();
            1 // Action taken
        }
        1 => {
            // Warning: log and monitor
            log_warning();
            0 // Monitoring
        }
        _ => 0, // Normal operation
    }
}

fn trigger_alarm() {
    // Activate emergency protocols
    // This would interface with actual hardware
}

fn shutdown_systems() {
    // Safe shutdown of connected systems
    // This would interface with actual hardware
}

fn log_warning() {
    // Log warning for later analysis
    // This would write to local storage
}

#[panic_handler]
fn panic(_info: &core::panic::PanicInfo) -> ! {
    // Safe panic handling for embedded systems
    trigger_alarm();
    loop {}
}

Edge Device Management

// Device management and OTA updates with WASM
use serde::{Deserialize, Serialize};

#[derive(Serialize, Deserialize)]
struct DeviceStatus {
    device_id: String,
    firmware_version: String,
    uptime: u64,
    memory_usage: u32,
    cpu_usage: f32,
    temperature: f32,
    last_heartbeat: u64,
}

#[derive(Serialize, Deserialize)]
struct UpdatePackage {
    version: String,
    checksum: String,
    size: u32,
    critical: bool,
    rollback_version: Option<String>,
}

#[no_mangle]
pub extern "C" fn check_device_health(
    status_ptr: *const u8,
    status_len: usize,
) -> u8 {
    let status_data = unsafe {
        core::slice::from_raw_parts(status_ptr, status_len)
    };
    
    let status: DeviceStatus = match serde_json::from_slice(status_data) {
        Ok(s) => s,
        Err(_) => return 255, // Error
    };
    
    let mut health_score = 100u8;
    
    // Check memory usage
    if status.memory_usage > 90 {
        health_score = health_score.saturating_sub(30);
    } else if status.memory_usage > 75 {
        health_score = health_score.saturating_sub(15);
    }
    
    // Check CPU usage
    if status.cpu_usage > 95.0 {
        health_score = health_score.saturating_sub(25);
    } else if status.cpu_usage > 80.0 {
        health_score = health_score.saturating_sub(10);
    }
    
    // Check temperature
    if status.temperature > 70.0 {
        health_score = health_score.saturating_sub(20);
    } else if status.temperature > 60.0 {
        health_score = health_score.saturating_sub(10);
    }
    
    // Check uptime (too low might indicate crashes)
    if status.uptime < 3600 { // Less than 1 hour
        health_score = health_score.saturating_sub(15);
    }
    
    health_score
}

#[no_mangle]
pub extern "C" fn should_update_firmware(
    current_version_ptr: *const u8,
    current_version_len: usize,
    available_update_ptr: *const u8,
    available_update_len: usize,
    device_health: u8,
) -> u8 {
    let current_version = unsafe {
        core::str::from_utf8_unchecked(
            core::slice::from_raw_parts(current_version_ptr, current_version_len)
        )
    };
    
    let update_data = unsafe {
        core::slice::from_raw_parts(available_update_ptr, available_update_len)
    };
    
    let update: UpdatePackage = match serde_json::from_slice(update_data) {
        Ok(u) => u,
        Err(_) => return 0, // No update
    };
    
    // Critical updates should be applied regardless
    if update.critical {
        return 2; // Force update
    }
    
    // Don't update unhealthy devices unless critical
    if device_health < 70 {
        return 0; // Skip update
    }
    
    // Check version comparison
    if version_compare(current_version, &update.version) < 0 {
        return 1; // Normal update
    }
    
    0 // No update needed
}

fn version_compare(v1: &str, v2: &str) -> i8 {
    // Simple version comparison (major.minor.patch)
    let parts1: Vec<u32> = v1.split('.').filter_map(|s| s.parse().ok()).collect();
    let parts2: Vec<u32> = v2.split('.').filter_map(|s| s.parse().ok()).collect();
    
    for i in 0..3 {
        let p1 = parts1.get(i).unwrap_or(&0);
        let p2 = parts2.get(i).unwrap_or(&0);
        
        if p1 < p2 {
            return -1;
        } else if p1 > p2 {
            return 1;
        }
    }
    
    0
}

// Secure OTA update verification
#[no_mangle]
pub extern "C" fn verify_update_signature(
    update_data: *const u8,
    update_len: usize,
    signature: *const u8,
    signature_len: usize,
    public_key: *const u8,
    public_key_len: usize,
) -> bool {
    // This would use a cryptographic library to verify signatures
    // For embedded systems, ed25519 is often preferred for its small size
    
    // Simplified verification (in real implementation, use proper crypto)
    let data_slice = unsafe {
        core::slice::from_raw_parts(update_data, update_len)
    };
    
    let sig_slice = unsafe {
        core::slice::from_raw_parts(signature, signature_len)
    };
    
    // Calculate hash of update data
    let data_hash = simple_hash(data_slice);
    let sig_hash = simple_hash(sig_slice);
    
    // In a real implementation, this would be proper signature verification
    data_hash == sig_hash
}

fn simple_hash(data: &[u8]) -> u32 {
    let mut hash = 0u32;
    for &byte in data {
        hash = hash.wrapping_mul(31).wrapping_add(byte as u32);
    }
    hash
}

Edge Orchestration Strategies

K3s with WASM Support

# K3s cluster configuration for edge
apiVersion: v1
kind: ConfigMap
metadata:
  name: k3s-config
  namespace: kube-system
data:
  config.yaml: |
    # K3s configuration for edge deployments
    cluster-cidr: "10.42.0.0/16"
    service-cidr: "10.43.0.0/16"
    
    # Optimize for edge constraints
    kube-apiserver-arg:
      - "feature-gates=TTLAfterFinished=true"
      - "default-not-ready-toleration-seconds=30"
      - "default-unreachable-toleration-seconds=30"
    
    kube-controller-manager-arg:
      - "feature-gates=TTLAfterFinished=true"
      - "node-monitor-period=20s"
      - "node-monitor-grace-period=30s"
    
    kubelet-arg:
      - "feature-gates=TTLAfterFinished=true"
      - "image-gc-high-threshold=70"
      - "image-gc-low-threshold=50"
      - "eviction-hard=memory.available<100Mi"

---
# WASM runtime class for edge
apiVersion: node.k8s.io/v1
kind: RuntimeClass
metadata:
  name: wasmtime-edge
handler: wasmtime
overhead:
  podFixed:
    memory: "5Mi"
    cpu: "10m"
scheduling:
  nodeClassification:
    - name: "wasm-capable"
      value: "true"
  tolerations:
  - effect: NoSchedule
    key: edge-node
    operator: Equal
    value: "true"

---
# Edge workload deployment
apiVersion: apps/v1
kind: Deployment
metadata:
  name: edge-processor
  labels:
    app: edge-processor
    tier: edge
spec:
  replicas: 1
  selector:
    matchLabels:
      app: edge-processor
  template:
    metadata:
      labels:
        app: edge-processor
        tier: edge
    spec:
      runtimeClassName: wasmtime-edge
      nodeSelector:
        node-type: edge
        kubernetes.io/arch: wasm32
      tolerations:
      - key: edge-node
        operator: Equal
        value: "true"
        effect: NoSchedule
      containers:
      - name: processor
        image: registry.example.com/edge-processor:latest
        resources:
          requests:
            memory: "5Mi"
            cpu: "10m"
          limits:
            memory: "20Mi"
            cpu: "100m"
        env:
        - name: EDGE_LOCATION
          valueFrom:
            fieldRef:
              fieldPath: spec.nodeName
        - name: PROCESSING_MODE
          value: "realtime"
      affinity:
        nodeAffinity:
          preferredDuringSchedulingIgnoredDuringExecution:
          - weight: 100
            preference:
              matchExpressions:
              - key: node.kubernetes.io/instance-type
                operator: In
                values: ["edge", "iot"]

Edge Service Mesh

# Istio configuration for edge deployments
apiVersion: v1
kind: ConfigMap
metadata:
  name: istio-edge-config
  namespace: istio-system
data:
  mesh: |
    defaultConfig:
      # Optimize for edge constraints
      concurrency: 1
      proxyStatsMatcher:
        exclusionRegexps:
        - ".*_cx_.*"
        - ".*_rb_.*"
      # Reduce resource usage
      proxyMetadata:
        PILOT_ENABLE_WORKLOAD_ENTRY_AUTOREGISTRATION: true
        BOOTSTRAP_XDS_AGENT: true
    
    defaultProviders:
      metrics:
      - prometheus
      tracing:
      - jaeger
      accessLogging:
      - envoy
    
    # Edge-specific settings
    extensionProviders:
    - name: edge-telemetry
      prometheus:
        configOverride:
          metric_relabeling_configs:
          - source_labels: [__name__]
            regex: 'istio_.*'
            target_label: edge_location
            replacement: '${EDGE_LOCATION}'

---
# Gateway for edge traffic
apiVersion: networking.istio.io/v1beta1
kind: Gateway
metadata:
  name: edge-gateway
spec:
  selector:
    istio: edge-gateway
  servers:
  - port:
      number: 80
      name: http
      protocol: HTTP
    hosts:
    - "*.edge.example.com"
  - port:
      number: 443
      name: https
      protocol: HTTPS
    hosts:
    - "*.edge.example.com"
    tls:
      mode: SIMPLE
      credentialName: edge-tls-cert

---
# Virtual service for edge routing
apiVersion: networking.istio.io/v1beta1
kind: VirtualService
metadata:
  name: edge-routing
spec:
  hosts:
  - "api.edge.example.com"
  gateways:
  - edge-gateway
  http:
  - match:
    - uri:
        prefix: "/v1/process"
    route:
    - destination:
        host: edge-processor
        port:
          number: 8080
    timeout: 5s
    retries:
      attempts: 2
      perTryTimeout: 2s
  - match:
    - uri:
        prefix: "/v1/analytics"
    route:
    - destination:
        host: edge-analytics
        port:
          number: 8080
    fault:
      delay:
        percentage:
          value: 0.1
        fixedDelay: 100ms

Real-time Data Processing

Stream Processing at the Edge

// Real-time stream processing with WASM
use serde::{Deserialize, Serialize};
use std::collections::{HashMap, VecDeque};

#[derive(Deserialize, Clone)]
struct StreamEvent {
    timestamp: u64,
    source: String,
    event_type: String,
    data: serde_json::Value,
}

#[derive(Serialize)]
struct ProcessedEvent {
    timestamp: u64,
    source: String,
    aggregated_data: serde_json::Value,
    alert_level: u8,
    metadata: HashMap<String, String>,
}

struct EdgeStreamProcessor {
    window_size: usize,
    event_windows: HashMap<String, VecDeque<StreamEvent>>,
    aggregation_state: HashMap<String, AggregationState>,
}

#[derive(Default)]
struct AggregationState {
    count: u64,
    sum: f64,
    min: f64,
    max: f64,
    last_reset: u64,
}

impl EdgeStreamProcessor {
    fn new(window_size: usize) -> Self {
        Self {
            window_size,
            event_windows: HashMap::new(),
            aggregation_state: HashMap::new(),
        }
    }
    
    fn process_event(&mut self, event: StreamEvent) -> Option<ProcessedEvent> {
        let key = format!("{}:{}", event.source, event.event_type);
        
        // Add to sliding window
        let window = self.event_windows.entry(key.clone()).or_default();
        window.push_back(event.clone());
        
        // Maintain window size
        if window.len() > self.window_size {
            window.pop_front();
        }
        
        // Update aggregation state
        self.update_aggregation(&key, &event);
        
        // Check if we should emit a processed event
        if self.should_emit(&key) {
            Some(self.create_processed_event(&key, &event))
        } else {
            None
        }
    }
    
    fn update_aggregation(&mut self, key: &str, event: &StreamEvent) {
        let state = self.aggregation_state.entry(key.to_string()).or_default();
        
        if let Some(value) = event.data.as_f64() {
            state.count += 1;
            state.sum += value;
            
            if state.count == 1 {
                state.min = value;
                state.max = value;
            } else {
                state.min = state.min.min(value);
                state.max = state.max.max(value);
            }
        }
    }
    
    fn should_emit(&self, key: &str) -> bool {
        if let Some(window) = self.event_windows.get(key) {
            // Emit when window is full or on significant change
            window.len() >= self.window_size || self.detect_anomaly(key)
        } else {
            false
        }
    }
    
    fn detect_anomaly(&self, key: &str) -> bool {
        if let Some(state) = self.aggregation_state.get(key) {
            if state.count < 3 {
                return false;
            }
            
            let avg = state.sum / state.count as f64;
            let range = state.max - state.min;
            
            // Simple anomaly detection: large deviation from average
            range > avg * 2.0
        } else {
            false
        }
    }
    
    fn create_processed_event(&self, key: &str, latest: &StreamEvent) -> ProcessedEvent {
        let state = self.aggregation_state.get(key).unwrap();
        let avg = if state.count > 0 { state.sum / state.count as f64 } else { 0.0 };
        
        let mut aggregated_data = serde_json::Map::new();
        aggregated_data.insert("count".to_string(), state.count.into());
        aggregated_data.insert("average".to_string(), avg.into());
        aggregated_data.insert("min".to_string(), state.min.into());
        aggregated_data.insert("max".to_string(), state.max.into());
        
        let alert_level = if self.detect_anomaly(key) { 2 } else { 0 };
        
        let mut metadata = HashMap::new();
        metadata.insert("window_size".to_string(), self.window_size.to_string());
        metadata.insert("processing_location".to_string(), "edge".to_string());
        
        ProcessedEvent {
            timestamp: latest.timestamp,
            source: latest.source.clone(),
            aggregated_data: serde_json::Value::Object(aggregated_data),
            alert_level,
            metadata,
        }
    }
}

// WASM exports for edge stream processing
static mut PROCESSOR: Option<EdgeStreamProcessor> = None;

#[no_mangle]
pub extern "C" fn init_stream_processor(window_size: usize) {
    unsafe {
        PROCESSOR = Some(EdgeStreamProcessor::new(window_size));
    }
}

#[no_mangle]
pub extern "C" fn process_stream_event(
    event_ptr: *const u8,
    event_len: usize,
    output_ptr: *mut u8,
    output_len: *mut usize,
) -> i32 {
    let event_data = unsafe {
        std::slice::from_raw_parts(event_ptr, event_len)
    };
    
    let event: StreamEvent = match serde_json::from_slice(event_data) {
        Ok(e) => e,
        Err(_) => return -1,
    };
    
    let result = unsafe {
        match PROCESSOR.as_mut() {
            Some(processor) => processor.process_event(event),
            None => return -2,
        }
    };
    
    if let Some(processed) = result {
        let output_data = serde_json::to_vec(&processed).unwrap();
        let output_slice = unsafe {
            std::slice::from_raw_parts_mut(output_ptr, output_data.len())
        };
        
        output_slice.copy_from_slice(&output_data);
        unsafe {
            *output_len = output_data.len();
        }
        
        1 // Success with output
    } else {
        0 // Success without output
    }
}

Time Series Analysis at Edge

// Time series forecasting at the edge
use std::collections::VecDeque;

#[repr(C)]
struct TimeSeriesPoint {
    timestamp: u64,
    value: f64,
}

#[repr(C)]
struct ForecastResult {
    next_value: f64,
    confidence: f64,
    trend: f64,
    seasonality: f64,
}

struct EdgeTimeSeriesAnalyzer {
    history: VecDeque<TimeSeriesPoint>,
    max_history: usize,
    seasonal_period: usize,
}

impl EdgeTimeSeriesAnalyzer {
    fn new(max_history: usize, seasonal_period: usize) -> Self {
        Self {
            history: VecDeque::new(),
            max_history,
            seasonal_period,
        }
    }
    
    fn add_point(&mut self, point: TimeSeriesPoint) {
        self.history.push_back(point);
        if self.history.len() > self.max_history {
            self.history.pop_front();
        }
    }
    
    fn forecast_next(&self) -> ForecastResult {
        if self.history.len() < 3 {
            return ForecastResult {
                next_value: 0.0,
                confidence: 0.0,
                trend: 0.0,
                seasonality: 0.0,
            };
        }
        
        let trend = self.calculate_trend();
        let seasonality = self.calculate_seasonality();
        let base_value = self.history.back().unwrap().value;
        
        let next_value = base_value + trend + seasonality;
        let confidence = self.calculate_confidence();
        
        ForecastResult {
            next_value,
            confidence,
            trend,
            seasonality,
        }
    }
    
    fn calculate_trend(&self) -> f64 {
        if self.history.len() < 2 {
            return 0.0;
        }
        
        let n = self.history.len().min(10); // Use last 10 points for trend
        let start_idx = self.history.len() - n;
        
        let mut sum_x = 0.0;
        let mut sum_y = 0.0;
        let mut sum_xy = 0.0;
        let mut sum_x2 = 0.0;
        
        for (i, point) in self.history.iter().skip(start_idx).enumerate() {
            let x = i as f64;
            let y = point.value;
            
            sum_x += x;
            sum_y += y;
            sum_xy += x * y;
            sum_x2 += x * x;
        }
        
        let n_f = n as f64;
        if n_f * sum_x2 - sum_x * sum_x == 0.0 {
            return 0.0;
        }
        
        (n_f * sum_xy - sum_x * sum_y) / (n_f * sum_x2 - sum_x * sum_x)
    }
    
    fn calculate_seasonality(&self) -> f64 {
        if self.history.len() < self.seasonal_period * 2 {
            return 0.0;
        }
        
        let current_season_idx = (self.history.len() - 1) % self.seasonal_period;
        let mut seasonal_values = Vec::new();
        
        // Collect values from the same seasonal position
        for i in (current_season_idx..self.history.len()).step_by(self.seasonal_period) {
            if let Some(point) = self.history.get(i) {
                seasonal_values.push(point.value);
            }
        }
        
        if seasonal_values.is_empty() {
            return 0.0;
        }
        
        // Simple seasonal average
        let seasonal_avg = seasonal_values.iter().sum::<f64>() / seasonal_values.len() as f64;
        let overall_avg = self.history.iter().map(|p| p.value).sum::<f64>() / self.history.len() as f64;
        
        seasonal_avg - overall_avg
    }
    
    fn calculate_confidence(&self) -> f64 {
        if self.history.len() < 5 {
            return 0.0;
        }
        
        // Calculate mean absolute error of recent predictions
        let recent_errors: Vec<f64> = self.history
            .iter()
            .rev()
            .take(5)
            .collect::<Vec<_>>()
            .windows(2)
            .map(|window| {
                let predicted = window[1].value; // Simplified: use previous value as prediction
                let actual = window[0].value;
                (predicted - actual).abs()
            })
            .collect();
        
        if recent_errors.is_empty() {
            return 0.5;
        }
        
        let mae = recent_errors.iter().sum::<f64>() / recent_errors.len() as f64;
        let avg_value = self.history.iter().map(|p| p.value.abs()).sum::<f64>() / self.history.len() as f64;
        
        // Convert MAE to confidence (0-1 scale)
        if avg_value == 0.0 {
            0.5
        } else {
            (1.0 - (mae / avg_value)).max(0.0).min(1.0)
        }
    }
}

// WASM exports for time series analysis
static mut ANALYZER: Option<EdgeTimeSeriesAnalyzer> = None;

#[no_mangle]
pub extern "C" fn init_time_series_analyzer(max_history: usize, seasonal_period: usize) {
    unsafe {
        ANALYZER = Some(EdgeTimeSeriesAnalyzer::new(max_history, seasonal_period));
    }
}

#[no_mangle]
pub extern "C" fn add_time_series_point(timestamp: u64, value: f64) {
    let point = TimeSeriesPoint { timestamp, value };
    
    unsafe {
        if let Some(analyzer) = ANALYZER.as_mut() {
            analyzer.add_point(point);
        }
    }
}

#[no_mangle]
pub extern "C" fn forecast_next_value() -> ForecastResult {
    unsafe {
        match ANALYZER.as_ref() {
            Some(analyzer) => analyzer.forecast_next(),
            None => ForecastResult {
                next_value: 0.0,
                confidence: 0.0,
                trend: 0.0,
                seasonality: 0.0,
            },
        }
    }
}

Security at the Edge

Zero Trust Edge Security

// Zero Trust security model for edge WASM
use serde::{Deserialize, Serialize};
use std::collections::HashMap;

#[derive(Deserialize)]
struct SecurityContext {
    user_id: String,
    device_id: String,
    location: GeoLocation,
    network_info: NetworkInfo,
    request_metadata: HashMap<String, String>,
}

#[derive(Deserialize)]
struct GeoLocation {
    country: String,
    region: String,
    city: String,
    latitude: f64,
    longitude: f64,
}

#[derive(Deserialize)]
struct NetworkInfo {
    ip_address: String,
    user_agent: String,
    tls_version: String,
    cipher_suite: String,
}

#[derive(Serialize)]
struct SecurityDecision {
    allow: bool,
    confidence_score: f64,
    risk_factors: Vec<String>,
    required_actions: Vec<String>,
    expires_at: u64,
}

struct EdgeSecurityEngine {
    risk_rules: Vec<RiskRule>,
    geo_restrictions: HashMap<String, Vec<String>>,
    device_reputation: HashMap<String, f64>,
}

struct RiskRule {
    name: String,
    risk_score: f64,
    condition: fn(&SecurityContext) -> bool,
}

impl EdgeSecurityEngine {
    fn new() -> Self {
        let mut engine = Self {
            risk_rules: Vec::new(),
            geo_restrictions: HashMap::new(),
            device_reputation: HashMap::new(),
        };
        
        engine.initialize_rules();
        engine
    }
    
    fn initialize_rules(&mut self) {
        // Geolocation risk
        self.risk_rules.push(RiskRule {
            name: "high_risk_country".to_string(),
            risk_score: 0.8,
            condition: |ctx| {
                matches!(ctx.location.country.as_str(), "XX" | "YY" | "ZZ")
            },
        });
        
        // Velocity risk
        self.risk_rules.push(RiskRule {
            name: "impossible_travel".to_string(),
            risk_score: 0.9,
            condition: |_ctx| {
                // This would check against previous locations
                false // Simplified
            },
        });
        
        // Device risk
        self.risk_rules.push(RiskRule {
            name: "suspicious_user_agent".to_string(),
            risk_score: 0.6,
            condition: |ctx| {
                ctx.network_info.user_agent.is_empty() || 
                ctx.network_info.user_agent.len() < 10
            },
        });
        
        // Network risk
        self.risk_rules.push(RiskRule {
            name: "weak_tls".to_string(),
            risk_score: 0.7,
            condition: |ctx| {
                !ctx.network_info.tls_version.starts_with("TLSv1.3")
            },
        });
    }
    
    fn evaluate_request(&mut self, context: &SecurityContext) -> SecurityDecision {
        let mut total_risk = 0.0;
        let mut risk_factors = Vec::new();
        let mut required_actions = Vec::new();
        
        // Evaluate risk rules
        for rule in &self.risk_rules {
            if (rule.condition)(context) {
                total_risk += rule.risk_score;
                risk_factors.push(rule.name.clone());
            }
        }
        
        // Check device reputation
        if let Some(&reputation) = self.device_reputation.get(&context.device_id) {
            if reputation < 0.3 {
                total_risk += 0.5;
                risk_factors.push("low_device_reputation".to_string());
            }
        }
        
        // Normalize risk score
        let risk_score = (total_risk / self.risk_rules.len() as f64).min(1.0);
        let confidence_score = 1.0 - risk_score;
        
        // Determine actions based on risk
        if risk_score > 0.8 {
            required_actions.push("block_request".to_string());
        } else if risk_score > 0.6 {
            required_actions.push("require_mfa".to_string());
        } else if risk_score > 0.4 {
            required_actions.push("additional_verification".to_string());
        }
        
        // Determine if request should be allowed
        let allow = risk_score < 0.8 && !required_actions.contains(&"block_request".to_string());
        
        SecurityDecision {
            allow,
            confidence_score,
            risk_factors,
            required_actions,
            expires_at: current_timestamp() + 300, // 5 minutes
        }
    }
    
    fn update_device_reputation(&mut self, device_id: &str, reputation: f64) {
        self.device_reputation.insert(device_id.to_string(), reputation);
    }
}

// WASM exports for edge security
static mut SECURITY_ENGINE: Option<EdgeSecurityEngine> = None;

#[no_mangle]
pub extern "C" fn init_security_engine() {
    unsafe {
        SECURITY_ENGINE = Some(EdgeSecurityEngine::new());
    }
}

#[no_mangle]
pub extern "C" fn evaluate_security_context(
    context_ptr: *const u8,
    context_len: usize,
    output_ptr: *mut u8,
    output_len: *mut usize,
) -> i32 {
    let context_data = unsafe {
        std::slice::from_raw_parts(context_ptr, context_len)
    };
    
    let context: SecurityContext = match serde_json::from_slice(context_data) {
        Ok(c) => c,
        Err(_) => return -1,
    };
    
    let decision = unsafe {
        match SECURITY_ENGINE.as_mut() {
            Some(engine) => engine.evaluate_request(&context),
            None => return -2,
        }
    };
    
    let output_data = serde_json::to_vec(&decision).unwrap();
    let output_slice = unsafe {
        std::slice::from_raw_parts_mut(output_ptr, output_data.len())
    };
    
    output_slice.copy_from_slice(&output_data);
    unsafe {
        *output_len = output_data.len();
    }
    
    if decision.allow { 1 } else { 0 }
}

fn current_timestamp() -> u64 {
    // This would return actual timestamp
    1640995200 // Simplified
}

Performance Optimization

Edge-Specific Optimizations

// Edge performance optimization strategies
#![no_std]
#![no_main]

use core::mem;
use linked_list_allocator::LockedHeap;

#[global_allocator]
static ALLOCATOR: LockedHeap = LockedHeap::empty();

// Memory pool for edge deployments
const POOL_SIZE: usize = 64 * 1024; // 64KB pool
static mut MEMORY_POOL: [u8; POOL_SIZE] = [0; POOL_SIZE];

#[no_mangle]
pub extern "C" fn _start() {
    init_memory_pool();
    init_edge_optimizations();
}

fn init_memory_pool() {
    unsafe {
        ALLOCATOR.lock().init(MEMORY_POOL.as_mut_ptr(), POOL_SIZE);
    }
}

fn init_edge_optimizations() {
    // Pre-allocate common data structures
    pre_allocate_buffers();
    
    // Initialize lookup tables
    init_lookup_tables();
    
    // Configure CPU-specific optimizations
    configure_cpu_optimizations();
}

// Buffer pool for zero-allocation processing
static mut BUFFER_POOL: [Buffer; 16] = [Buffer::new(); 16];
static mut BUFFER_INDEX: usize = 0;

#[derive(Copy, Clone)]
struct Buffer {
    data: [u8; 1024],
    len: usize,
}

impl Buffer {
    const fn new() -> Self {
        Self {
            data: [0; 1024],
            len: 0,
        }
    }
}

fn pre_allocate_buffers() {
    // Buffers are already allocated statically
    // This just initializes the rotation index
    unsafe {
        BUFFER_INDEX = 0;
    }
}

#[no_mangle]
pub extern "C" fn get_buffer() -> *mut Buffer {
    unsafe {
        let buffer = &mut BUFFER_POOL[BUFFER_INDEX];
        BUFFER_INDEX = (BUFFER_INDEX + 1) % BUFFER_POOL.len();
        buffer.len = 0; // Reset buffer
        buffer
    }
}

// Lookup tables for fast computations
static CRC_TABLE: [u32; 256] = generate_crc_table();
static SIN_TABLE: [f32; 360] = generate_sin_table();

const fn generate_crc_table() -> [u32; 256] {
    let mut table = [0u32; 256];
    let mut i = 0;
    
    while i < 256 {
        let mut crc = i as u32;
        let mut j = 0;
        
        while j < 8 {
            if crc & 1 != 0 {
                crc = (crc >> 1) ^ 0xEDB88320;
            } else {
                crc >>= 1;
            }
            j += 1;
        }
        
        table[i] = crc;
        i += 1;
    }
    
    table
}

const fn generate_sin_table() -> [f32; 360] {
    let mut table = [0.0; 360];
    let mut i = 0;
    
    while i < 360 {
        // Simplified sine calculation for const context
        table[i] = 0.0; // Would calculate actual sine values
        i += 1;
    }
    
    table
}

fn init_lookup_tables() {
    // Tables are already initialized at compile time
}

fn configure_cpu_optimizations() {
    // Enable CPU features if available (simplified)
    // This would use CPU feature detection
}

// Fast CRC calculation using lookup table
#[no_mangle]
pub extern "C" fn fast_crc32(data: *const u8, len: usize) -> u32 {
    let mut crc = 0xFFFFFFFF;
    
    let slice = unsafe { core::slice::from_raw_parts(data, len) };
    
    for &byte in slice {
        let table_index = ((crc ^ byte as u32) & 0xFF) as usize;
        crc = (crc >> 8) ^ CRC_TABLE[table_index];
    }
    
    !crc
}

// SIMD-style operations for edge processing
#[no_mangle]
pub extern "C" fn vectorized_sum(data: *const f32, len: usize) -> f32 {
    let slice = unsafe { core::slice::from_raw_parts(data, len) };
    
    // Process in chunks of 4 for pseudo-SIMD
    let mut sum = 0.0;
    let chunks = slice.chunks_exact(4);
    let remainder = chunks.remainder();
    
    for chunk in chunks {
        // Simulate SIMD addition
        sum += chunk[0] + chunk[1] + chunk[2] + chunk[3];
    }
    
    // Handle remainder
    for &value in remainder {
        sum += value;
    }
    
    sum
}

// Cache-friendly data processing
#[no_mangle]
pub extern "C" fn cache_optimized_filter(
    input: *const u8,
    output: *mut u8,
    len: usize,
    threshold: u8,
) {
    let input_slice = unsafe { core::slice::from_raw_parts(input, len) };
    let output_slice = unsafe { core::slice::from_raw_parts_mut(output, len) };
    
    // Process in cache-line sized chunks (64 bytes)
    const CHUNK_SIZE: usize = 64;
    
    for (input_chunk, output_chunk) in input_slice
        .chunks(CHUNK_SIZE)
        .zip(output_slice.chunks_mut(CHUNK_SIZE))
    {
        for (i, o) in input_chunk.iter().zip(output_chunk.iter_mut()) {
            *o = if *i > threshold { *i } else { 0 };
        }
    }
}

// Branch-free algorithms for predictable performance
#[no_mangle]
pub extern "C" fn branchless_max(a: i32, b: i32) -> i32 {
    let diff = a - b;
    let mask = diff >> 31; // All 1s if a < b, all 0s if a >= b
    a ^ ((a ^ b) & mask)
}

#[no_mangle]
pub extern "C" fn branchless_clamp(value: i32, min: i32, max: i32) -> i32 {
    let clamped_min = branchless_max(value, min);
    let clamped_max = min + (((clamped_min - min) as u32) & 
                            (((max - clamped_min) >> 31) as u32)) as i32;
    clamped_max
}

// Memory-efficient string processing
#[no_mangle]
pub extern "C" fn in_place_string_reverse(data: *mut u8, len: usize) {
    if len <= 1 {
        return;
    }
    
    let slice = unsafe { core::slice::from_raw_parts_mut(data, len) };
    let mid = len / 2;
    
    for i in 0..mid {
        slice.swap(i, len - 1 - i);
    }
}

#[panic_handler]
fn panic(_info: &core::panic::PanicInfo) -> ! {
    loop {}
}

Conclusion

Edge computing with WebAssembly represents the future of distributed, low-latency applications. By leveraging WASM's unique characteristics - ultra-fast startup, minimal resource usage, and strong security - we can build sophisticated edge applications that were previously impossible.

Key Benefits Realized

• ✅ Sub-millisecond startup enables true serverless at the edge
• ✅ Minimal memory footprint allows massive scale deployment
• ✅ Universal portability simplifies multi-platform deployment
• ✅ Strong isolation provides security without performance cost
• ✅ Near-native performance enables complex edge processing

Edge Use Cases Mastered

1. CDN Edge Functions - Content optimization and routing
2. IoT Data Processing - Real-time sensor data analysis
3. Security at Edge - Zero-trust security decisions
4. Stream Processing - Real-time data transformation
5. Edge Analytics - Local insights generation

Production Considerations

• Choose the right platform based on your requirements
• Optimize for resource constraints at edge locations
• Implement proper monitoring and observability
• Design for intermittent connectivity
• Plan for edge-to-cloud data synchronization

Next Steps

• Explore advanced edge orchestration with K3s
• Implement multi-region edge deployments
• Build edge-native applications from scratch
• Contribute to the growing edge-WASM ecosystem

The combination of edge computing and WebAssembly is revolutionizing how we build distributed applications. Start experimenting with edge WASM today!

Resources

• Cloudflare Workers Documentation
• Fastly Compute@Edge
• WasmEdge for Edge Computing
• K3s Lightweight Kubernetes

Ready to secure your WASM applications? Check out our next article on WASM security and sandboxing! 🔒