Historical Evolution and Future Trends in Garbage Collection

Authors: Pradeep Kumar

DOI: https://doi.org/10.5281/zenodo.14684772

Short DOI: https://doi.org/g82ccm

Country: USA

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Abstract: Garbage Collection (GC) is a critical mechanism for automated memory management, addressing challenges like memory leaks and dangling pointers. Early algorithms such as Mark-Sweep and Copying GC provided foundational solutions but introduced significant CPU overhead and pause times. Generational GC optimized performance by segregating short-lived and long-lived objects, reducing collection frequency for the old generation and lowering computational costs.
Modern advancements, including G1, ZGC, and Shenandoah, prioritize minimizing pause times and improving scalability for real-time and cloud-native applications. However, these collectors require additional computational resources, increasing CPU usage during concurrent operations. Emerging trends such as AI-driven GC optimization leverage machine learning to predict allocation patterns, adapt GC strategies dynamically, and balance throughput with reduced computational overhead. Additionally, energy-efficient designs aim to reduce power consumption, critical for large-scale systems such as data centers.
Despite these innovations, challenges like memory fragmentation, hybrid workloads, and the CPU costs of concurrent GCs persist as critical research areas. Future directions include developing adaptive GC algorithms capable of efficiently handling diverse workloads while optimizing performance and energy efficiency. This paper synthesizes the evolution, modern techniques, and emerging trends, providing a comprehensive roadmap for improving GC in heterogeneous computing environments.

Keywords: Garbage Collection, Memory Management, Mark-Sweep, Generational GC, ZGC, G1, Shenandoah

Paper Id: 232039

Published On: 2020-03-12

Published In: Volume 8, Issue 2, March-April 2020

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