Sandy Strong and Brian Martin, Twitter
The Twitter Ad Server is our revenue engine. It's designed to perform resiliently under high load and unpredictable spikes in demand. It accomplishes this by using concepts from control theory, which allows it to adapt its performance in response to changes in demand, based upon available system resources.
Services that drive revenue lend themselves to optimization projects that focus on how to make the most of our resources. The output of the system is revenue, and the cost of compute resource cuts into that.
We ask ourselves: Can we do the same amount of work with less resource, by appropriately tuning the software and the systems it runs on? If so, this reduces our operational costs.
In this talk we will start at the beginning, where we had a hunch that it was possible to reduce operational costs, and continue all the way through the experiments and unexpected outcomes that led us to settle on our final set of optimizations.
Sandy Strong, Twitter
Sandy is a Site Reliability Engineer at Twitter, and has been embedded with the Ads Serving Team for two years.
Brian Martin, Twitter
Brian Martin is a Site Reliability Engineer at Twitter, working on our Core Storage systems.
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author = {Sandy Strong and Brian Martin},
title = {Tune Your Way to Savings!},
year = {2017},
address = {San Francisco, CA},
publisher = {USENIX Association},
month = mar
}