Where the JVM is Entering the Era of AI Performance Tuning! By Ana Maria Mihalceanu

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. Core Features: 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. JVM Tuning: 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. Benefits for Developers: Developers can identify performance bottlenecks and take appropriate measures to optimize the overall performance of their applications. Enterprise-Level Features: 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. Additional Tools: 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-MARIA MIHALCEANU 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.