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Load Balancing Techniques

Explore various load balancing techniques that distribute incoming traffic across backend servers to prevent overload and improve system reliability. Understand the differences between hardware, software, and cloud load balancers, alongside static and dynamic algorithms like round robin, least connections, and consistent hashing. Learn how to implement load balancers within system architectures, perform health checks, and maintain high availability to ensure continuous service operation.

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A system that survives rate limiting can still collapse. Picture an e-commerce platform that correctly throttles excess traffic during a flash sale, keeping total request volume within safe bounds. Yet every surviving request lands on a single backend instance because the routing layer has no distribution logic. That instance saturates its CPU, response times spike, and the entire checkout flow fails. Protecting capacity through rate limiting is only half the reliability equation. The other half is distributing that protected capacity efficiently across available infrastructure.

Working of a load balancer
Working of a load balancer

Load balancing solves this by routing incoming requests across multiple backend instances, preventing hotspots, improving availability, and maximizing throughput. A load balancer sits between clients and server pools as a traffic routing component, deciding which backend handles each request. This lesson covers the types of load balancers, the algorithms they use, where they fit in system architectures, and how to monitor them.

Forms of load balancers

Load balancers come in three fundamental forms, and the distinction shapes cost, flexibility, and operational complexity.

  • Hardware load balancers: Hardware LBs are dedicated physical appliances. They use custom ASICsApplication-Specific Integrated Circuits, specialized hardware chips designed for a specific task (e.g., fast packet processing). for wire-speed packet processing, delivering deterministic latency measured in microseconds. While they offer high performance and handle many concurrent users, they are expensive and difficult to configure. High availability is costly, as it requires redundant hardware for failover. Additionally, they often suffer from vendor lock-in and limited flexibility.

  • Software load balancers: Software LBs run on commodity hardware, making them flexible, programmable, and cost-effective. They scale easily as requirements grow. High availability is easier to achieve, as shadow load balancers can be deployed on commodity hardware with minimal cost. They also support predictive analysis for traffic planning. ...