Tuning big data frameworks is a very important task to get the best performance for a given application. However, these frameworks are rarely used individually, they generally constitute a pipeline, each having a different role. This makes tuning big data pipelines an important yet difficult task given the size of the search space. Moreover, we have to consider the interaction between these frameworks when tuning the configuration parameters of the big data pipeline. A trade-off is then required to achieve the best end-to-end performance.
Machine learning-based methods have shown great success in automatic tuning systems, but they rely on a large number of high-quality learning examples that are rather difficult to obtain. In this context, we propose to use a deep reinforcement learning algorithm, namely Twin Delayed Deep Deterministic Policy Gradient, TD3, to tune a fraud detection big data pipeline.
Houssem Sagaama, Nourchene Ben Slimane, Maher Marwani, Sabri Skhiri, Automatic Parameter Tuning for Big Data Pipelines, In Proc. of The 26th IEEE Symposium on Computers and Communications (ISCC 2021), September 2021.
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