Sep 2026· International Journal of Research Publication and Reviews· Vol 7, pp. 1863-1866· 0 citations
TL;DR
This paper investigates strategies for optimizing memory management and minimizing GC overhead in cloud-native Java applications, and analyzes modern garbage collection algorithms, evaluate native compilation via GraalVM, explore the memory impact of Virtual Threads, and formulate container-aware tuning strategies for modern cloud environments.
Abstract
The transition of Java applications from monolithic, bare-metal enterprise deployments to cloud-native, containerized environments (e.g., Kubernetes) has introduced severe memory management challenges. Traditional Java Virtual Machine (JVM) heuristics—designed for high-throughput, multi-gigabyte monolithic runtimes—often conflict with container memory limits, leading to out-of-memory (OOM) kills, CPU throttling, and excessive garbage collection (GC) latency spikes. This paper investigates strategies for optimizing memory management and minimizing GC overhead in cloud-native Java applications. We analyze modern garbage collection algorithms (G1GC, Generational ZGC, and Shenandoah), evaluate native compilation via GraalVM, explore the memory impact of Virtual Threads (Project Loom), and formulate container-aware tuning strategies for modern cloud environments.
This work develops a novel low-overhead methodology for measuring the cost of GCs, by combining isolated thread monitoring using a combination of real hardware and calibrated cycle-accurate simulation, which allows for fine-grained analysis of modern GC overheads.
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