Where the JVM is Entering the Era of AI Performance Tuning!
Conference (INTERMEDIATE level)
Developed by the JVM team at Oracle, Oracle Java Management Cloud Service (JMS) recently introduced a performance AI engine to analyze and generate tuning recommendations to improve the performance of Java applications.
- JMS collects JVM telemetry runtime data to analyze JVM performance statistics.
- Uses a tuning engine that leverages decades of in-depth knowledge of how the JVM works to recommend optimum JVM setup.
- The JVM has more than 500 arguments, many of which are obscure to most Java developers.
- By analyzing garbage collection logs, JVM logs, and JFR recording, along with an understanding of JVM internal heuristics, the recommendation engine identifies specific areas for optimization.
- Suggests optimized JVM arguments for better performance.
- Developers can identify performance bottlenecks and take appropriate measures to optimize the overall performance of their applications.
- Empowers enterprises to maximize performance, security, and efficiency of Java workloads.
- Evaluates the effort and feasibility of migrating Java applications to newer JDK versions.
- Identifies and reports potential vulnerabilities (CVE) associated with 3rd party Java libraries used by applications.
- Assesses the impact of Oracle JRE and JDK Cryptographic Roadmap on applications.
- Uses Java Flight Recorder to gather application runtime data.
- Manages the install and removal of older JDK versions to keep all systems secure from a centralized console.
Ana is a Java Champion Alumni, Developer Advocate for the Java Platform Group at Oracle, guest author of the book "DevOps tools for Java Developers", and a constant adopter of challenging technical scenarios involving Java-based frameworks and multiple cloud providers. She actively supports technical communities' growth through knowledge sharing and enjoys curating content for conferences as a program committee member. To learn more about/from her, follow her on Twitter @ammbra1508.