Swift: Fast Performance Tuning with GAN-Generated Configurations

This paper proposes Swift, a novel Bayesian Optimization (BO) based parameter configuration tuning approach for big data systems. The key idea is to leverage a generative AI approach, generative adversarial network (GAN) , to generate high quality configurations based on the evaluated configuration with the highest performance. Mixing these configurations with randomly generated ones has the effect of skewing search space toward the optimal configuration, leading to faster convergence and less optimization time. Our substantial experimental results on Apache Flink, Spark programs, and an industrial setting show that Swift significantly improves the performance of data analytics over state-of-the-art approaches in dramatically shorter time.