Load Balancing Techniques for Optimal Performance

Load Balancing Techniques in 2024 1. Round Robin: The Classic Approach - Traditional: Sequentially routes requests to each server in turn - Weighted Version: Assigns more traffic to high-capacity servers - Best For: Systems with servers of similar capabilities - Real Example: Like a restaurant host seating customers at tables in rotation 2. Least Connections: The Traffic Manager - Basic Function: Routes to servers handling fewest current connections - Weighted Option: Considers server capacity alongside connection count - Key Advantage: Prevents server overload - Perfect For: Applications with varying request processing times 3. Least Response Time: The Speed Optimizer - Core Function: Selects servers with fastest response times - Monitors: Both active connections and response speed - Main Benefit: Enhanced user experience - Ideal For: Time-sensitive applications like trading platforms 4. Least Bandwidth: The Network Guardian - Primary Role: Directs traffic based on current bandwidth usage - Measures: Actual server load in terms of bandwidth - Key Feature: Prevents network saturation - Best Used: In bandwidth-intensive applications like video streaming 5. Least Packets: The Traffic Controller - Function: Routes based on packet count processing - Monitors: Network-level server load - Advantage: Fine-grained traffic control - Suited For: Network-intensive applications 6. IP Hash: The Consistency Keeper - How It Works: Maps client IPs to specific servers - Key Benefit: Session consistency without server-side storage - Perfect For: Applications needing user-server affinity - Real World Use: Content delivery networks (CDNs) 7. Sticky Sessions: The User Experience Guardian - Core Purpose: Maintains user-server relationships - Mechanism: Uses cookies or IP-based persistence - Critical For: E-commerce and banking applications - Benefit: Ensures transaction consistency 8. Layer 7 (Application Layer): The Smart Router - Intelligence Level: Content-aware routing - Capabilities: Routes based on URL, headers, or content type - Advanced Features: Can prioritize critical business transactions - Use Case: Microservices architectures 9. Geographical: The Global Optimizer - Strategy: Routes users to geographically closest servers - Benefits: Reduced latency, improved speed - Perfect For: Global applications 10. DNS-Based: The Internet-Scale Balancer - Operation: Resolves domain names to different server IPs - Advantage: Works at global scale - Best For: Distributed applications - Real Use: Global service providers 11. Transport Layer: The Protocol Specialist - Handles: Both TCP and UDP traffic - Distinction: Optimizes based on protocol needs - Key Feature: Protocol-specific optimization - Ideal For: Mixed protocol applications 12. AI-Powered: The Future of Load Balancing - Technology: Machine learning for traffic patterns - Capability: Real-time adaptation to changing conditions - Advanced Features: Predictive scaling, anomaly detection

Every day on YouTube, people upload 4 million videos and watch 5 billion videos. Handling this staggering traffic requires a vast fleet of servers. So when a new request comes in, where does it go? How do you balance load across so many servers for so many jobs? I love this paper because it proposes a clever load balancing system that works at the largest of scales. Let’s imagine a YouTube service is distributed across 1000 servers. Whenever a new query arrives, we send it to one of those servers to process. How do we choose which one? The obvious load balancing strategy is CPU-based, sending requests to whichever server has the lowest utilization. This works pretty well! But the problem with CPU-based load balancing is that CPU utilization is a lagging indicator that’s averaged over a measuring period and might not reflect the exact current state of a server. That means CPU-based load balancing can risk momentarily overloading servers and causing spikes in tail latency. How do we balance load better? The idea is simple and elegant: if your goal is to minimize query latency, why not send each query to the server with the lowest latency? This works through probing. When a new query comes in, the load balancer sends lightweight probes to a number of target servers. Each probe measures two instantaneous signals: estimated latency of the server (based on latency of recent queries) and the number of requests in flight. Usually, queries are routed to the server with the lowest estimated latency. The exception is if the servers have very high numbers of requests in flight, indicating high incoming load–then, requests are routed to the servers with the fewest requests in flight. What’s most impressive about this paper is that Prequal really works. The authors report that when they deployed it in production at YouTube, tail latency dropped by 2x, significantly reducing error and lag spikes for users. It’s not often that we see a systems paper produce results like that in the real world.

🚀 Nothing kills a 10M TPS load test faster than load imbalance. Production spikes? Even tougher. Load balancing might not be glamorous, but it’s a crucial component in scaling fleets efficiently. 1️⃣ The Power of Vertical Scale 10M TPS are easy to handle on a 1000 webserver fleet right? Afterall, that's only 10K TPS per server, assuming a perfectly balanced load. But in practice, the busiest 1% of servers (10 nodes in 1000) often get 10x the load, destroying tail latencies, & causing load based outages. The antidote to the law of large numbers is to pursue smaller numbers. Momento routing fleet handles 200K+ TPS on a single 16-core host. This took significant investment considering we had to terminate TLS, authenticate, route to the storage shards, and collect deep metrics, while maintaining our p999 SLO. The effort pays off by minimizing statistical imbalance in larger fleets. 10M TPS only requires 50 hosts. 2️⃣ Load Balancing in Spikes Demands Load Shedding Your fleet hums along at 400K TPS with two routers across two AZs. Suddenly, a *1M TPS spike** hits. Autoscaling? Too slow— server side connections are already established and getting hotter. Even if we are proactive about scaling, we still need to redistribute load to new routers. Enter load shedding: with well behaved clients (this is why we write our own SDKs), you can send an HTTP2 GoAway frame and expect your clients to establish connections, rebalancing load to the newly provisioned capacity. This magic trick minimizes over provisioning, reduces waste, & keeps us within our p999 SLOs with a leaner fleet. 3️⃣ Hot Keys? Use the Force, Luke: Cache the Cache! Hot keys are a nightmare. Millions of reads on the same key can crush storage shards. Adding replicas? Too slow and inefficient. Adding replicas proactively requires over-provisioning your entire storage fleet. Doing it on-demand for the hottest shard takes too long. Momento routers simply sample the hottest keys, caching them locally. By caching a hot key for just 10ms, we reduce storage load from 1M TPS to 500 TPS across 5 routers (each router handling 200K+ requests). This is incredibly efficient for a 10ms trade-off on consistency. For customers needing more consistent reads (e.g., nonces), a consistent read concerns allows them to trade off hot key protection for data that is 10ms fresher. I have yet to run across a customer that needs millions of consistent reads on a single key. These simple techniques—plus multi-tenancy—help us deliver robust systems ready for production spikes of 10M+ TPS. 💡 Want to nerd out with me on your high-volume workflows? Book time with me for Scaling Office Hours: 👉 https://lnkd.in/g6fdXJWV 💬 If you think there are techniques we’re missing or should reconsider, please add them in the comments. We are eager to learn more and continue improving the robustness of our systems every day!