Dads: Dynamic Slicing Continuously-Running Distributed Programs with Budge... (Video, ESEC/FSE 2020)
"Dads: Dynamic Slicing Continuously-Running Distributed Programs with Budget Constraints (Video, ESEC/FSE 2020)
Xiaoqin Fu, Haipeng Cai, and Li Li
(Washington State University, USA; Washington State University, USA; Monash University, Australia)
Abstract: We present Dads, the first distributed, online, scalable, and cost-effective dynamic slicer for continuously-running distributed programs with respect to user-specified budget constraints. Dads is distributed by design to exploit distributed and parallel computing resources. With an online analysis, it avoids tracing hence the associated time and space costs. Most importantly, Dads achieves and maintains practical scalability and cost-effectiveness tradeoffs according to a given budget on analysis time by continually and automatically adjusting the configuration of its analysis algorithm on the fly via reinforcement learning. Against eight real-world Java distributed systems, we empirically demonstrated the scalability and cost-effectiveness merits of Dads.
The open-source tool package is at https://bitbucket.org/wsucailab/seads
Article: https://doi.org/10.1145/3368089.3417920
Supplementary web page: https://bitbucket.org/wsucailab/seads
ORCID: https://orcid.org/0000-0002-5224-9970
Submitted to the conference by Xiaoqin Fu on 2020-10-31, updated 2022-03-27
Video Tags: Distributed system, dynamic slicing, reinforcement learning, fse20demo-p3-p, DOI: 10.1145/3368089.3417920, ORCID: 0000-0002-5224-9970
Presentation at the ESEC/FSE 2020 conference, November 8–13, 2020, https://2020.esec-fse.org/
Sponsored by ACM SIGSOFT, https://www.sigsoft.org/
Twitter: / fseconf
Reddit: / esecfse "
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