InfoQ Homepage application performance management Content on InfoQ
-
Fast and Dynamic
Maxime Chevalier-Boisvert discusses making dynamic languages faster providing various examples of optimizations: SmallTalk, LISP machine, Google V8 and others.
/presentations/dynamic-performance/en/mediumimage/Maxbig.jpg)
-
Why Ruby Isn't Slow
Alex Gaynor explains how he solved the usual Ruby VM speed problems with Topaz, a high performance VM built on the same technologies that power PyPy.
/presentations/ruby-performance/en/mediumimage/Alexbig.jpg)
-
Visual Management: Leading with What You Can See
Craig Smith, Renee Troughton discuss improving visual management: different types of story walls, ways to visualize the product backlog, the important of queue columns and WIP limitation, etc.
/presentations/visual-management/en/mediumimage/Craigbig.jpg)
-
The Unreasonable Effectiveness of Tuning
Keith Adams shares HHVM insights showing how a system can become very performant if it is well tuned.
/presentations/facebook-tuning/en/mediumimage/Keithbig.jpg)
-
Resiliency through Failure - Netflix's Approach to Extreme Availability in the Cloud
Ariel Tseitlin discusses Netflix' failure-based suite of tools, collectively called the Simian Army, used to improve resiliency and maintain the cloud environment.
/presentations/netflix-resiliency-failure-cloud/en/mediumimage/Aribig.jpg)
-
Robust Software - Dotting the I's and Crossing the T's
Chris Oldwood discusses what it takes to create robust software: correct error detection and recovery, testing systemic effects, app monitoring and configuration.
/presentations/robust-software/en/mediumimage/Chrisbig.jpg)
-
Code Execution as Data
Zach Tellman discusses instrumenting and analyzing running systems using real world examples from Factual's production systems.
/presentations/analyze-running-system/en/mediumimage/Zacbig.jpg)
-
Leveraging Your Hadoop Cluster Better - Running Performant Code at Scale
Michael Kopp explains how to run performance code at scale with Hadoop and how to analyze and optimize Hadoop jobs.
/presentations/optimize-hadoop-jobs/en/mediumimage/Micbig.jpg)
-
Deep Dive into G1 Garbage Collector
Charlie Hunt and Monica Beckwith describe the operational basics of G1 and how to tune it, along with tips on what to expect when migrating from Parallel GC or CMS to G1 GC.
/presentations/java-g1/en/mediumimage/doibig.jpg)
-
The Fundamentals of JVM Tuning
Charlie Hunt presents the fundamentals of JVM tuning and provides advice for developers on writing a Java application that performs well at runtime.
/presentations/jvm-tuning/en/mediumimage/Charliebig.jpg)
-
Dumb and Dumber: How smart is your monitoring data
Big Data is all the rage right now. Everyone from a social media company to your grandmother's online knitting store is suddenly a big data shop.
/presentations/dumb-and-dumber/en/mediumimage/Hirsbig.jpg)
-
JVM Mechanics – A Peek Under the Hood
Gil Tene discusses JVM observation-based runtime optimizations, ordering and memory model rules, basics GC functions, memory management, and JVM mechanics.
/presentations/JVM-Mechanics/en/mediumimage/Gilbig.jpg)