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SAVE THE DATE FOR QCON SF 2018

Conference: Nov 5-7, 2018
Workshops: Nov 8–9, 2018

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.

Find Ramki Ramakrishna at

https://twitter.com/ysr1729
https://www.linkedin.com/in/ysramakrishna/

Talk: Continuous Optimization of Microservices Using ML

Track: Evolving Java
Location: Seacliff ABC
Day of week: Tuesday
Duration: 11:50am - 12:40pm

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.

Other talks from track Evolving Java

Java 9: Tips on Migration and Upgradability
Head of the Java Platform Development Team & VP @Oracle
Bernard Traversat
Lessons Debugging Serverless JVM Functions
Co-founder and CEO @OverOps
Tal Weiss
Servlet vs Reactive: Choosing the Right Stack
Spring Framework Committer @Pivotal
Rossen Stoyanchev
Fresh Async With Kotlin
Software Engineer Developing Kotlin @JetBrains
Roman Elizarov

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Tracks

  • Architectures You've Always Wondered About

    Architectural practices from the world's most well-known properties, featuring startups, massive scale, evolving architectures, and software tools used by nearly all of us.

  • Going Serverless

    Learn about the state of Serverless & how to successfully leverage it! Lessons learned in the track hit on security, scalability, IoT, and offer warnings to watch out for.

  • Microservices: Patterns and Practices

    Stories of success and failure building modern Microservices, including event sourcing, reactive, decomposition, & more.

  • DevOps: You Build It, You Run It

    Pushing DevOps beyond adoption into cultural change. Hear about designing resilience, managing alerting, CI/CD lessons, & security. Features lessons from open source, Linkedin, Netflix, Financial Times, & more. 

  • The Art of Chaos Engineering

    Failure is going to happen - Are you ready? Chaos engineering is an emerging discipline - What is the state of the art?

  • The Whole Engineer

    Success as an engineer is more than writing code. Hear inward looking thoughts on inclusion, attitude, leadership, remote working, and not becoming the brilliant jerk.

  • Evolving Java

    Java continues to evolve & change. Track covers Spring 5, async, Kotlin, serverless, the 6-month cadence plans, & AI/ML use cases.

  • Security: Attacking and Defending

    Offense and defensive security evolution that application developers should know about including SGX Enclaves, effects of AI, software exploitation techniques, & crowd defense

  • The Practice & Frontiers of AI

    Learn about machine learning in practice and on the horizon. Learn about ML at Quora, Uber's Michelangelo, ML workflow with Netflix Meson and topics on Bots, Conversational interfaces, automation, and deployment practices in the space.

  • 21st Century Languages

    Compile to Native, Microservices, Machine learning... tailor-made languages solving modern challenges, featuring use cases around Go, Rust, C#, and Elm.

  • Modern CS in the Real World

    Applied trends in Computer Science that are likely to affect Software Engineers today. Topics include category theory, crypto, CRDT's, logic-based automated reasoning, and more.

  • Stream Processing In The Modern Age

    Compelling applications of stream processing using Flink, Beam, Spark, Strymon & recent advances in the field, including Custom Windowing, Stateful Streaming, SQL over Streams.  

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