How do you implement edge computing and CDN strategies for global performance?

Edge computing processes data closer to users for reduced latency and improved performance. CDN (Content Delivery Network): cache static assets (images, CSS, JS, videos) at edge locations worldwide. Implementation: configure cache headers (Cache-Control: public, max-age=31536000 for versioned assets), implement cache invalidation via purge APIs or versioned file names, and use stale-while-revalidate for always-fast responses. CDN providers: Cloudflare (global network, Workers for edge compute), CloudFront (AWS integration, Lambda@Edge), and Fastly (real-time purging, Compute@Edge). Edge compute: run application logic at edge locations for lower latency. Use cases: personalization (customize content based on geo-location, device, user segment), A/B testing (route at the edge without origin round-trip), API routing and authentication (validate tokens at the edge), image optimization (resize and format conversion at the edge), and bot detection (filter bad traffic before it reaches origin). Implementation: Cloudflare Workers (V8 isolates, sub-millisecond cold starts, 200+ edge locations), Deno Deploy (TypeScript at the edge, globally distributed), and Vercel Edge Functions (integrated with Next.js). Architecture patterns: edge-first (serve from edge when possible, fall back to origin), edge caching with edge compute for dynamic content (personalized cached pages), and edge orchestration (edge function coordinates calls to multiple origin services). Performance optimization: implement edge-level caching for API responses (short TTL with stale-while-revalidate), preconnect and prefetch hints, HTTP/3 (QUIC) for reduced connection setup time, and Brotli compression at the edge. Monitoring: track cache hit ratios per edge location, measure origin load reduction, monitor edge function execution time and error rates, and implement real user monitoring (RUM) for client-side performance measurement.