QCon San Francisco 2021 November 1-5, 2021 | | Continuous Optimization of Microservices Using ML

Abstract

Performance tuning of microservices in the data center is hard because of the multitude of available knobs, the large number of microservices and variation in work loads, all of which combine to make the problem combinatorially intractable. Maintaining optimal performance in the face of continuous upgrades to the service, and of the platform software and hardware, makes the problem even harder. As a result, lots of performance is typically left on the table, and data center resources wasted. We share our recent experiences in applying a technique from machine learning, called Bayesian optimization, to the performance tuning problem. We describe the implementation of a service for continuously optimizing microservices in the data center using this technique.

Speaker: Ramki Ramakrishna

Staff Software Engineer @Twitter

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. Ramki has worked with several generations of the JVM, at Sun and Oracle, before Twitter. He has been a committer and reviewer for the HotSpot group in OpenJDK. 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. Before joining industry, Ramki worked at SUNY Stony Brook, the Tata Institute of Fundamental Research in India, and Aalborg University in Denmark, dividing time between teaching and research into the formal verification of concurrent systems, using process algebras, temporal logics and automatic theorem-proving. Ramki holds a Ph.D. in Electrical and Computer Engineering from the University of California at Santa Barbara, and a B.Tech. in Electrical Engineering from IIT Kanpur in India.