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Continuous Optimization of Microservices Using ML
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| by Ramki Ramakrishna Follow 1 Followers on Feb 06, 2018 | NOTICE: The next QCon is in London, Mar 4 - 6, 2019. Join us!

Ramki Ramakrishna shares Twitter’s recent experience in applying a technique from machine learning, called Bayesian optimization, to the performance tuning problem. He describes the implementation of a service for continuously optimizing microservices in the data center using this technique.


Ramki Ramakrishna is a staff software engineer in the Infrastructure Engineering Division of Twitter. He is a member of the JVM team and of the Twitter Architecture Group. His principal contributions have been in the areas of performance analysis, tuning and adaptive optimization, parallel and concurrent garbage collection, and the synchronization infrastructure within the JVM.

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