Abstract
Data structure selection and tuning is laborious but can vastly improve application performance and memory footprint. In this paper, we demonstrate how artemis, a multiobjective, cloud-based optimisation framework can automatically find optimal, tuned data structures and how it is used for optimising the Guava library. From the proposed solutions that artemis found, \(27.45\%\) of them improve all measures (execution time, CPU usage, and memory consumption). More specifically, artemis managed to improve the memory consumption of Guava by up 13%, execution time by up to 9%, and 4% CPU usage.
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Basios, M., Li, L., Wu, F., Kanthan, L., Barr, E.T. (2017). Optimising Darwinian Data Structures on Google Guava. In: Menzies, T., Petke, J. (eds) Search Based Software Engineering. SSBSE 2017. Lecture Notes in Computer Science(), vol 10452. Springer, Cham. https://doi.org/10.1007/978-3-319-66299-2_14
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DOI: https://doi.org/10.1007/978-3-319-66299-2_14
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