ACTGAN: Automatic Configuration Tuning for Software Systems with Generative Adversarial Networks
Complex software systems often provide a large number of parameters so that users can configure them for their specific application scenarios. However, configuration tuning requires a deep understanding of the software system, far beyond the abilities of typical system users. To address this issue, many existing approaches focus on exploring and learning good performance estimation models. The accuracy of such models often suffers when the number of available samples is small, a thorny challenge under a given tuning-time constraint. By contrast, we hypothesize that good configurations often share certain hidden structures. Therefore, instead of trying to improve the performance estimation of a given configuration, we focus on capturing the hidden structures of good configurations and utilizing such learned structure to generate potentially better configurations. We propose ACTGAN to achieve this goal. We have implemented and evaluated ACTGAN using 17 workloads with eight different software systems. Experimental results show that ACTGAN outperforms default configurations by 76.22% on average, and six state-of-the-art configuration tuning algorithms by 6.58%-64.56%. Furthermore, the ACTGAN-generated configurations are often better than those used in training and show certain features consisting with domain knowledge, both of which supports our hypothesis.
Wed 13 Nov
13:40 - 15:20: Papers - Configurations and Variability at Hillcrest Chair(s): Shin Hwei TanSouthern University of Science and Technology | ||||||||||||||||||||||||||||||||||||||||||
13:40 - 14:00 Talk | ACTGAN: Automatic Configuration Tuning for Software Systems with Generative Adversarial Networks Liang BaoSchool of Computer Science and Technology, XiDian University, Xin LiuDepartment of Computer Science, University of California, Davis, Fangzheng WangSchool of Computer Science and Technology, XiDian University, Baoyin FangSchool of Computer Science and Technology, XiDian University | |||||||||||||||||||||||||||||||||||||||||
14:00 - 14:20 Talk | Automated N-way Program Merging for Facilitating Family-Based Analyses of Variant-Rich Software Dennis ReulingSoftware Engineering Group, University of Siegen, Udo KelterSoftware Engineering Group, University of Siegen, Johannes BürdekTU Darmstadt, Real-time Systems Lab, Malte LochauTU Darmstadt Link to publication DOI | |||||||||||||||||||||||||||||||||||||||||
14:20 - 14:40 Talk | V2: Fast Detection of Configuration Drift in Python Pre-print | |||||||||||||||||||||||||||||||||||||||||
14:40 - 15:00 Talk | Feature-Interaction Aware Configuration Prioritization for Configurable Code Son NguyenThe University of Texas at Dallas, Hoan Anh NguyenAmazon, Ngoc TranUniversity of Texas at Dallas, Hieu TranThe University of Texas at Dallas, Tien N. NguyenUniversity of Texas at Dallas | |||||||||||||||||||||||||||||||||||||||||
15:00 - 15:20 Talk | Search-based test case implantation for testing untested configurations Dipesh PradhanSimula Research Laboratory, Norway, Shuai WangHong Kong University of Science and Technology, Tao YueNanjing University of Aeronautics and Astronautics & Simula Research Laboratory, Shaukat AliSimula Research Lab, Marius LiaaenCisco Systems Link to publication |