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Optimization of Java Virtual Machine Flags using Feature Model and Genetic Algorithm

Authors:

Felipe Canales,

Geoffrey Hecht,

Alexandre BergelAuthors Info & Claims

ICPE '21: Companion of the ACM/SPEC International Conference on Performance Engineering

Pages 183 - 186

https://doi.org/10.1145/3447545.3451177

Published: 19 April 2021 Publication History

Abstract

Optimizing the Java Virtual Machine (JVM) options in order to get the best performance out of a program for production is a challenging and time-consuming task. HotSpot, the Oracle's open-source Java VM implementation offers more than 500 options, called flags, that can be used to tune the JVM's compiler, garbage collector (GC), heap size and much more. In addition to being numerous, these flags are sometimes poorly documented and create a need of benchmarking to ensure that the flags and their associated values deliver the best performance and stability for a particular program to execute.

Auto-tuning approaches have already been proposed in order to mitigate this burden. However, in spite of increasingly sophisticated search techniques allowing for powerful optimizations, these approaches take little account of the underlying complexities of JVM flags. Indeed, dependencies and incompatibilities between flags are non-trivial to express, which if not taken into account may lead to invalid or spurious flag configurations that should not be considered by the auto-tuner.

In this paper, we propose a novel model, inspired by the feature model used in Software Product Line, which takes the complexity of JVM's flags into account. We then demonstrate the usefulness of this model, using it as an input of a Genetic Algorithm (GA) to optimize the execution times of DaCapo Benchmarks.

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Cited By

Baz AGligoric MShi A(2024)Impact of JVM Configurations on Test Runtime2024 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME58944.2024.00032(249-261)Online publication date: 6-Oct-2024
https://doi.org/10.1109/ICSME58944.2024.00032
Zhao YWang ZChen JLiu MWu MZhang YZhang LDwyer MDamian DZeller A(2022)History-driven test program synthesis for JVM testingProceedings of the 44th International Conference on Software Engineering10.1145/3510003.3510059(1133-1144)Online publication date: 21-May-2022
https://dl.acm.org/doi/10.1145/3510003.3510059

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Information & Contributors

Information

Published In

cover image ACM Conferences

ICPE '21: Companion of the ACM/SPEC International Conference on Performance Engineering

April 2021

198 pages

ISBN:9781450383318

DOI:10.1145/3447545

General Chairs:
Johann Bourcier
University of Rennes 1, France
,
Zhen Ming (Jack) Jiang
York University, Canada
,
Program Chairs:
Cor-Paul Bezemer
University of Alberta, Canada
,
Vittorio Cortellessa
University of L'Aquila, Italy

Copyright © 2021 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Publication History

Published: 19 April 2021

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Agencia Nacional de Investigación y Desarrollo
Lam Research

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ICPE '21

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ICPE '21: ACM/SPEC International Conference on Performance Engineering

April 19 - 23, 2021

Virtual Event, France

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Cited By

Baz AGligoric MShi A(2024)Impact of JVM Configurations on Test Runtime2024 IEEE International Conference on Software Maintenance and Evolution (ICSME)10.1109/ICSME58944.2024.00032(249-261)Online publication date: 6-Oct-2024
https://doi.org/10.1109/ICSME58944.2024.00032
Zhao YWang ZChen JLiu MWu MZhang YZhang LDwyer MDamian DZeller A(2022)History-driven test program synthesis for JVM testingProceedings of the 44th International Conference on Software Engineering10.1145/3510003.3510059(1133-1144)Online publication date: 21-May-2022
https://dl.acm.org/doi/10.1145/3510003.3510059

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