Performance analysis of cloud applications
Performance analysis of cloud applications Ardelean et al., NSDI’18
Today’s choice gives us an insight into how Google measure and analyse the performance of large user-facing services such as Gmail (from which most of the data in the paper is taken). It’s a paper in two halves. The first part of the paper demonstrates through an analysis of traffic and load patterns why the only real way to analyse production performance is using live production systems. The second part of the paper shares two techniques that Google use for doing so: coordinated bursty tracing and vertical context injection.
(Un)predictable load
Let’s start out just by consider Gmail requests explicitly generated by users (called ‘user visible requests,’ or UVRs, in the paper). These are requests generated by mail clients due to clicking on messages, sending messages, and background syncing (e.g., IMAP).
You can see a clear diurnal cycle here, with the highest QPS when both North America and Europe are active in the early morning, and lower QPS at weekends. (All charts are rescaled using some unknown factor, to protect Google information).

Request response sizes vary by about 20% over time. Two contributing factors are bulk mail senders, Continue reading
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