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The Role of Machine Learning in Optimizing Garbage Collection

Author(s) Pradeep Kumar
Country United States
Abstract Garbage collection (GC) has been fundamental to memory management in managed runtime environments since its inception. However, traditional GC techniques, such as generational and concurrent collectors, struggle with issues like unpredictable pause times, scalability limitations, and excessive computational overhead. Machine learning (ML) offers innovative solutions to these challenges by enabling predictive optimization, anomaly detection, and dynamic tuning. Predictive models can forecast application workload patterns and optimize heap allocation, leading to reduced latency and improved throughput (McCarthy, 1960, p. 185). Reinforcement learning has been applied to dynamically switch between GC strategies at runtime, resulting in significant performance improvements by minimizing user-perceived delays (Ghosh et al., 2009, p. 325). Furthermore, supervised learning algorithms have demonstrated their utility in detecting anomalies, such as memory leaks and excessive fragmentation, thus preventing crashes and improving system reliability (Jones, 1996, p. 212). Despite these advancements, challenges remain in deploying ML-driven GC systems due to data collection overhead, real-time computational constraints, and the difficulty of generalizing models across diverse workloads. This review explores the intersection of ML and GC, highlighting existing frameworks, their benefits, and the future potential for adaptive, ML-driven memory management systems.
Keywords Garbage Collection (GC), Machine Learning (ML), Memory Management, Reinforcement Learning, Predictive Modeling, Runtime Optimization, Heap Space Optimization, Dynamic Tuning, Adaptive Systems, Performance Metrics, Scalability.
Field Engineering
Published In Volume 11, Issue 2, April-June 2020
Published On 2020-05-05
Cite This The Role of Machine Learning in Optimizing Garbage Collection - Pradeep Kumar - IJSAT Volume 11, Issue 2, April-June 2020. DOI 10.71097/IJSAT.v11.i2.1642
DOI https://doi.org/10.71097/IJSAT.v11.i2.1642
Short DOI https://doi.org/g83jfd
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