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@article{182978,
        author = {Sai Adarsh S and Prasanna Venkateshan and Prithvi Alva Suresh},
        title = {A V8 Memory Management Technique for Running Node.js Applications in Low-Memory Environments},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {2},
        pages = {3863-3870},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=182978},
        abstract = {Node.js applications running on Google’s V8 JavaScript engine often encounter significant memory management challenges in resource-constrained cloud environments (e.g., Heroku dynos). V8’s default garbage collection heuristics, tuned for servers with expansive memory, can result in excessive memory consumption under tight memory limits, leading to application instability and premature termination. This paper provides an in-depth analysis of these issues and proposes a practical solution: a dynamic V8 memory configuration utility. By dynamically adjusting V8’s heap size parameters, specifically the –max-old-space size-flag and the now-legacy –max-new-space-size (renamed to –max-semi-space-size), to align with the host environment’s memory limits, this utility mitigates memory spikes and prevents application crashes. We delineate V8’s generational garbage collection model and the impact of key V8 flags on memory usage, examine the specific memory issues observed with Node.js on Heroku, and detail the design and operation of the proposed utility. Our results demonstrate how proactive heap tuning ensures robust Node.js performance in low-memory environments while highlighting the need for complementary best practices in application-level memory management.},
        keywords = {Node.js, V8, Memory Management, Garbage Collection, Heroku, Cloud Computing, Resource Constraints},
        month = {July},
        }