<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Tawhid Bhuiyan on Blog | Chameleon</title><link>https://blog.chameleoncloud.org/authors/tawhid-bhuiyan/</link><description>Recent content in Tawhid Bhuiyan on Blog | Chameleon</description><generator>Hugo</generator><language>en-us</language><lastBuildDate>Mon, 30 Mar 2026 00:00:00 +0000</lastBuildDate><atom:link href="https://blog.chameleoncloud.org/authors/tawhid-bhuiyan/index.xml" rel="self" type="application/rss+xml"/><item><title>Wax: Making Stale Profiles Work for Data Center Optimization</title><link>https://blog.chameleoncloud.org/posts/wax-optimizing-data-center-applications-with-stale-profile/</link><pubDate>Mon, 30 Mar 2026 00:00:00 +0000</pubDate><guid>https://blog.chameleoncloud.org/posts/wax-optimizing-data-center-applications-with-stale-profile/</guid><description>&lt;p&gt;Every time you run a search query, stream a video, or send a message, a data center somewhere is burning energy to make it happen. Keeping those data centers fast and efficient is a constant engineering challenge—and one of the most powerful tools available is &lt;em&gt;profile-guided optimization&lt;/em&gt; (PGO): using data about how an application actually runs to reorganize its binary code for better performance.&lt;/p&gt;
&lt;p&gt;The catch? Profiling takes time, and software doesn't stand still. By the time engineers have collected a useful profile and applied it to their code, they're often already shipping the next version of the software. According to recent work from Google and Meta, &lt;strong&gt;70–92% of profile samples can become stale within a week&lt;/strong&gt; of a new release—meaning they no longer accurately map to the code they were meant to optimize. The result is a significant, persistent performance gap in some of the most critical software on the planet.&lt;/p&gt;</description></item></channel></rss>