A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is application/pdf
.
ATCS: Auto-Tuning Configurations of Big Data Frameworks Based on Generative Adversarial Nets
2020
IEEE Access
Big data processing frameworks (e.g., Spark, Storm) have been extensively used for massive data processing in the industry. To improve the performance and robustness of these frameworks, developers provide users with highly-configurable parameters. Due to the high-dimensional parameter space and complicated interactions of parameters, manual tuning of parameters is time-consuming and ineffective. Building performance-predicting models for big data frameworks is challenging for several reasons:
doi:10.1109/access.2020.2979812
fatcat:aownx2kmxvcjlp5gx5otahigz4