<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>BIC on Mi&amp;Bee Blog</title><link>https://blog.mickeyzzc.tech/en/tags/bic/</link><description>Recent content in BIC on Mi&amp;Bee Blog</description><generator>Hugo -- gohugo.io</generator><language>en</language><managingEditor>蓝宝石的傻话</managingEditor><lastBuildDate>Fri, 15 Aug 2025 00:00:00 +0000</lastBuildDate><atom:link href="https://blog.mickeyzzc.tech/en/tags/bic/rss.xml" rel="self" type="application/rss+xml"/><item><title>TCP Congestion Control Algorithm Evolution: From Tahoe to BBRv3 — Principles, Performance, and Linux Practice</title><link>https://blog.mickeyzzc.tech/en/posts/network/tcp-congestion-control-evolution/</link><pubDate>Fri, 15 Aug 2025 00:00:00 +0000</pubDate><guid>https://blog.mickeyzzc.tech/en/posts/network/tcp-congestion-control-evolution/</guid><description>&lt;p&gt;TCP congestion control is a critical determinant of network transport performance and a cornerstone of internet stability. Whether it&amp;rsquo;s webpage loading speed in web services, smoothness of live video streaming, inter-container communication in cloud-native applications, or download efficiency in P2P transfers, all rely on TCP congestion control to coordinate bandwidth allocation. A single BitTorrent node, for example, may maintain hundreds of concurrent TCP connections, and choosing the wrong algorithm can severely degrade bandwidth utilization. Since Van Jacobson&amp;rsquo;s seminal 1988 paper at SIGCOMM, congestion control algorithms have evolved over nearly four decades — from heuristic loss-based methods to precise model-based measurement. This article covers 12 major congestion control algorithms, explaining their core ideas, strengths, weaknesses, and applicable scenarios, from Tahoe and Reno to CUBIC, BBR, and Copa.&lt;/p&gt;</description></item></channel></rss>