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Speeding up AutoTuning of the Memory Management Options in Data Analytics

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Abstract

Many solutions used towards building autonomous (or, self-driving) data processing systems today are trying to leverage the “black box” algorithm of Bayesian Optimization (BO) both due to its wider applicability and the theoretical guarantees provided on the quality of results produced. The black-box approach, however, could be time and labor-intensive; or otherwise get stuck in a local minima. We study an important problem of auto-tuning the memory allocation for applications running on modern distributed data processing systems. A simple “white-box” model is developed which can quickly separate good configurations from bad ones. To combine the benefits of the two approaches to tuning, we build a framework called Guided Bayesian Optimization (GBO) that uses the white-box model as a guide during the Bayesian Optimization exploration process. An evaluation carried out on Apache Spark using industry-standard benchmark applications shows that GBO consistently provides performance speedups across the application workload with the magnitude of savings being close to 2x.

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Acknowledgements

The article was supported by National Science Foundation (Grant No. CNS-1423128). The author would like to thank Dr. Shivnath Babu for his expert feedback on the system design.

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    Mayuresh Kunjir

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Kunjir, M. Speeding up AutoTuning of the Memory Management Options in Data Analytics. Distrib Parallel Databases 38, 841–863 (2020). https://doi.org/10.1007/s10619-019-07281-y

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