<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Load Balancing on AI VOID</title><link>https://ai-blog.noorshomelab.dev/tags/load-balancing/</link><description>Recent content in Load Balancing on AI VOID</description><generator>Hugo</generator><language>en</language><lastBuildDate>Fri, 15 May 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://ai-blog.noorshomelab.dev/tags/load-balancing/index.xml" rel="self" type="application/rss+xml"/><item><title>Scaling with Reverse Proxies and API Gateways</title><link>https://ai-blog.noorshomelab.dev/systems-engineering-2026/reverse-proxies-api-gateways/</link><pubDate>Fri, 15 May 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/systems-engineering-2026/reverse-proxies-api-gateways/</guid><description>&lt;p&gt;Imagine your application starts small, a single server humming along, directly serving every user request. What happens when users multiply by thousands, or even millions? Direct access quickly becomes a bottleneck, a security risk, and a nightmare to manage. This is where reverse proxies and API gateways step in, transforming a fragile single point into a robust, scalable entry for your entire system.&lt;/p&gt;
&lt;p&gt;In this chapter, we&amp;rsquo;ll peel back the layers of how modern systems handle inbound traffic, learning the timeless engineering principles behind reverse proxies and API gateways. You&amp;rsquo;ll understand not just &lt;em&gt;what&lt;/em&gt; these components are, but &lt;em&gt;why&lt;/em&gt; they are indispensable for building scalable, resilient, and secure architectures, especially in the context of distributed systems and emerging AI agent workflows. We&amp;rsquo;ll explore their core functionalities, their evolution, and how to think about integrating them into your designs without falling into the trap of over-engineering.&lt;/p&gt;</description></item><item><title>Scaling Netflix: Elasticity, Load Balancing, and Autoscaling</title><link>https://ai-blog.noorshomelab.dev/netflix-internals-guide-2026-03-19/scaling-elasticity-autoscaling/</link><pubDate>Thu, 19 Mar 2026 00:00:00 +0000</pubDate><guid>https://ai-blog.noorshomelab.dev/netflix-internals-guide-2026-03-19/scaling-elasticity-autoscaling/</guid><description>&lt;h2 id="introduction"&gt;Introduction&lt;/h2&gt;
&lt;p&gt;Welcome to Chapter 9 of our deep dive into &amp;ldquo;How Netflix Works Internally.&amp;rdquo; In previous chapters, we laid the groundwork by discussing Netflix&amp;rsquo;s microservices architecture and principles of fault tolerance. Now, we confront a fundamental challenge for any global streaming service: how to handle massive, fluctuating user demand while maintaining high performance and availability. This is where the concepts of elasticity, load balancing, and autoscaling become paramount.&lt;/p&gt;
&lt;p&gt;In this chapter, we will explore the core strategies Netflix employs to scale its infrastructure. You&amp;rsquo;ll learn how Netflix leverages cloud elasticity to dynamically adjust resources, distributes incoming traffic efficiently using various load balancing mechanisms, and automates resource provisioning and de-provisioning through sophisticated autoscaling solutions. Understanding these mechanisms is crucial for appreciating how Netflix can serve millions of concurrent users worldwide without skipping a beat.&lt;/p&gt;</description></item></channel></rss>