Advanced Garbage Collection Strategies for Java Performance

Abstract: Garbage collection (GC) is an essential aspect of memory management in Java that helps automate the process of reclaiming unused objects, thereby reducing the chances of memory leaks and improving overall system performance. Although Java provides default GC settings, these may not always be optimal for high-performance or large-scale systems. This paper explores the inner workings of Java's garbage collectors, including Serial, Parallel, Concurrent Mark-Sweep (CMS), and G1, and provides guidance on selecting the most suitable one based on specific application needs. It also covers advanced tuning techniques involving heap size configuration, garbage collection logs, and monitoring tools that assist in identifying memory bottlenecks. Additionally, it discusses best practices and common mistakes developers encounter when fine-tuning GC settings, with a focus on balancing latency and throughput. By understanding and applying these optimization strategies, Java developers can significantly enhance application responsiveness and minimize downtime.

Java garbage collection, JVM tuning, memory management, heap management, G1 garbage collector

1. Mäkinen, S. (2021). Designing an open-source cloud-native M L Ops pipeline. University of Helsinki. [Google Scholar] [Crossref]

2. Bhatia, M. (2022). Cloud Adoption: A Foundational Engine. In Banking 4.0: The Industrialised Bank of Tomorrow (pp. 129-146). Singapore: Springer Nature Singapore. [Google Scholar] [Crossref]

3. Agarwal, G. (2021). Modern DevOps Practices: Implement and secure DevOps in the public cloud with cutting-edge tools, tips, tricks, and techniques. Packt Publishing Ltd. [Google Scholar] [Crossref]

4. Agarwal, G. (2021). Modern DevOps Practices: Implement and secure DevOps in the public cloud with cutting-edge tools, tips, tricks, and techniques. Packt Publishing Ltd. [Google Scholar] [Crossref]

5. Kumari, S. (2022). Agile Cloud Transformation in Enterprise Systems: Integrating AI for Continuous Improvement, Risk Management, and Scalability. Australian Journal of Machine Learning Research & Applications, 2(1), 416-440. [Google Scholar] [Crossref]

6. Manchana, R. (2019). Exploring Creational Design Patterns: Building Flexible and Reusable Software Solutions. International Journal of Science Engineering and Technology, 7, 1-10. https://doi.org/10.61463/ijset.vol.7.issue1.104. [Google Scholar] [Crossref]

7. Manchana, R. (2019). Structural Design Patterns: Composing Efficient and Scalable Software Architectures. International Journal of Scientific Research and Engineering Trends, 5, 1483-1491. https://doi.org/10.61137/ijsret.vol.5.issue3.371. [Google Scholar] [Crossref]

8. Manchana, R. (2019). Behavioral Design Patterns: Enhancing Software Interaction and Communication. International Journal of Science Engineering and Technology, 7, 1-18. https://doi.org/10.61463/ijset.vol.7.issue6.243. [Google Scholar] [Crossref]

9. Manchana, R. (2020). The Collaborative Commons: Catalyst for Cross-Functional Collaboration and Accelerated Development. International Journal of Science and Research (IJSR), 9, 1951-1958. https://doi.org/10.21275/SR24820051747. [Google Scholar] [Crossref]

10. Manchana, R. (2020). Cloud-Agnostic Solution for Large-Scale High-Performance Computer and Data Partitioning. https://doi.org/10.5281/zenodo.1392354 [Google Scholar] [Crossref]

• Topology Optimization for Low-Power-Wide-Area Networks (LPWANs) within Internet of Things (IOT)
• Detecting Misinformation Using Multimodal AI Models on Social Media Platforms
• Enhanced Face Detection Using Haar Cascade with Histogram Equalization, Sharpening, and Denoising for Real-Time Applications
• The Evolution of Data Analytics and Its Future Implication
• Utilizing AI Approaches for Generating Code Automatically