If you manage web applications, prepare for your traffic to grow exponentially overnight. Viral social posts can cripple websites not designed to scale on demand. Even without surges, you need to maximize uptime to avoid losing customers.
That‘s why IT architects have gravitated towards load balancers – distributing requests across backend servers to improve performance and reliability. And with microservices and serverless apps continuing to gain adoption, the need for intelligent routing and orchestration provided by modern load balancers keeps increasing.
This guide will walk through load balancing concepts and impementation on Google Cloud Platform (GCP) specifically. I‘ll share recommendations based on hard-learned lessons from web-scale architectures I‘ve designed and run directly.
By the end, you‘ll understand best practices for building highly available web and API backends ready for traffic bursts and real-world failure scenarios.
A Brief History of Load Balancing
Load balancers have been around since the early days of the internet. The first dedicated hardware load balancer products hit the market over 25 years ago!
They were simple Layer 4 devices that routed TCP/IP requests based on factors like server availability and basic round-robin algorithms…
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Conclusion: Application Reliability Starts with Load Balancing
Hopefully this post gave you a structured way to approach load balancing on Google Cloud. With compute resources instantly available, scale and flexibility are hallmarks of the public cloud. Load balancing sits on top to direct requests for performance and reliability.
I aimed to provide a solid foundation of concepts andconfiguration guidance based on real-world experience. Feel free to reach out on Twitter @expert_cloudarch if you have any other questions!
Now you have the understanding to implement resilient web application architectures on Google Cloud that withstand demanding customer workloads while keeping costs under control.
