Christophe Restif
Christophe Restif is a Software Engineer at Google in New York, where he is involved in the development of smart tools to assist the infrastructure Google-wide.
Before joining Google, he was an Assistant Professor at Rutgers, the State University of New Jersey, developing computer vision software for the biology of animal behavior. He received his PhD in computer science from Oxford Brookes University, UK, and his masters from the University of Cambridge, UK, and from Ecole Centrale Paris, France.
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End-to-end latency of serving jobs in distributed and shared environments, such as a Cloud, is an important metric for jobs' owners and infrastructure providers. Yet it is notoriously challenging to model precisely, since it is affected by a large collection of unrelated moving pieces, from the software design to the job schedulers strategies. In this work we present a novel approach to modeling latency, by tracking how it varies with CPU usage. We train a classifier to automatically assign the latency behavior of methods in three classes: constant latency regardless of CPU, uncorrelated latency and CPU, and predictable latency as a function of CPU. We use our model on a random sample of serving jobs running on the Google infrastructure. We illustrate unexpected and insightful patterns of latency variations with CPU. The visualization of latency-CPU variations and the corresponding class may be used by both jobs' owners and infrastructure providers, for a variety of applications, such as smarter latency alerting, latency-aware configuration of jobs, and automated detection of changes in behavior, either over time, during pre-release testing, or across data centers.
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