Abstract:
"This research tackles two critical challenges in the realm of Java application optimization and Java Virtual Machine (JVM) startup time reduction. The first challenge revolves around code optimization, which is vital for improving software performance. By addressing issues such as inefficient algorithmic implementations and poor memory management, the study aims to resolve common inefficiencies that often lead to resource contention, increased operational costs, and reduced responsiveness. These inefficiencies significantly hamper the overall performance and scalability of Java applications. The second challenge focuses on JVM initialization, a notorious bottleneck in the startup process, especially during the warm-up phase. This delay not only impacts application scalability but also negatively affects the user experience, as prolonged startup times are often perceived as a sign of sluggish performance.
To overcome these hurdles, this research introduces a hybrid profiling approach designed to optimize Java applications more effectively. This approach combines both static and dynamic profiling techniques, allowing for greater accuracy in measuring CPU time while minimizing memory overhead. By utilizing artificial intelligence, the approach includes a sophisticated code optimizer that analyzes the application’s code structure and recommends targeted optimizations. This AI-driven optimizer is adaptable, capable of evolving with the latest coding practices and development trends. Furthermore, the study investigates the use of GraalVM as a tool for code conversion. GraalVM promises to significantly enhance startup times, reduce memory consumption, and improve overall application responsiveness. Together, these strategies form a comprehensive solution aimed at optimizing both the code execution and the initialization process in Java applications, ultimately leading to better performance, faster startup times, and an enhanced user experience."
