Large scale A/B experimentation on data center infrastructure
When most people think of A/B experimentation, they think of button colors, landing page layouts, or checkout flows. At Google, many fundamental infrastructure improvements also need the rigor of A/B experimentation. Optimizing a memory allocator or a kernel scheduler can unlock massive savings in compute resources and slash latency for millions of users. But experimenting with such critical changes is inherently risky; a buggy kernel update doesn't just result in an unhappy user, it can take down large swaths of machines. To innovate safely and at scale, you must perform A/B experimentation on the infrastructure itself.
In this blog post, we summarize the key lessons from Google's A/B experimentation methodology that we’ve refined over multiple years. This blog post is meant as a resource of best practices to follow and initiate a broader discussion around this topic (similar to our performance tips series). Specifically, we highlight four pillars of...
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