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Conference Papers Year : 2022

Analysing and Predicting Energy Consumption of Garbage Collectors in OpenJDK

Marina Shimchenko
Tobias Wrigstad

Abstract

Sustainable computing needs energy-efficient software. This paper explores the potential of leveraging the nature of software written in managed languages: increasing energy efficiency by changing a program’s memory management strategy without altering a single line of code. To this end, we perform comprehensive energy profiling of 35 Java applications across four benchmarks. In many cases, we find that it is possible to save energy by replacing the default G1 collector with another without sacrificing performance. Furthermore, potential energy savings can be even higher if performance regressions are permitted. Inspired by these results, we study what the most energy-efficient GCs are to help developers prune the search space for energy profiling at a low cost. Finally, we show that machine learning can be successfully applied to the problem of finding an energy-efficient GC configuration for an application, reducing the cost even further.

Dates and versions

hal-03884425 , version 1 (05-12-2022)

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Cite

Marina Shimchenko, Mihail Popov, Tobias Wrigstad. Analysing and Predicting Energy Consumption of Garbage Collectors in OpenJDK. MPLR 2022 - 19th International Conference on Managed Programming Languages and Runtimes, Sep 2022, Brussels, Belgium. pp.3-15, ⟨10.1145/3546918.3546925⟩. ⟨hal-03884425⟩

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