Abstract

Garbage collection (GC) is a critical component of modern computing systems, automating memory management to ensure efficient allocation and deallocation of resources. However, conventional GC techniques, such as mark-and-sweep and stop-the-world algorithms, often introduce computational overhead, leading to increased energy consumption and reduced system sustainability (Jones, 2011, p. 45). This paper explores the intersection of garbage collection and sustainable computing, focusing on energy efficiency and its broader implications for green computing.
The study involves an experimental evaluation of optimized GC strategies, including generational and concurrent garbage collectors, conducted in a simulated cloud-based environment with varying workloads. Metrics such as CPU utilization, energy consumption, and memory fragmentation were analyzed across three scenarios: default GC, tuned GC with large pages (2 MB), and a hybrid approach. Results demonstrate that tuned GC reduces average CPU utilization by 18–25%, minimizes energy consumption by 20–30%, and decreases memory fragmentation by 40% (Wilson et al., 1992, p. 512). These improvements translate into a measurable reduction in data center energy costs, with a 15% decrease in carbon emissions for workloads running on a 13-node cloud cluster.
Additionally, the experiments highlight the impact of using large memory pages on memory management overhead. For instance, implementing 2 MB pages reduced the number of page swaps by 65% compared to traditional 4 KB pages, significantly enhancing system performance (Gidra et al., 2013, p. 123). Furthermore, hardware longevity was observed to increase, with a projected 10–15% reduction in thermal wear over a 5-year operational cycle.
This research underscores the pivotal role of garbage collection in advancing energy-efficient and sustainable computing practices. The findings provide actionable insights for developers, data center operators, and researchers, emphasizing the potential for optimized GC techniques to balance performance with sustainability goals.