Dads: dynamic slicing continuously-running distributed programs with budget constraints

Xiaoqin Fu, Haipeng Cai, Li Li

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review

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 of Dads with a demo video is publicly available.

Original languageEnglish
Title of host publicationESEC/FSE'20 - Proceedings of the 28th ACM Joint Meeting European Software Engineering Conference and Symposium on the Foundations of Software Engineering
EditorsPrem Devanbu, Myra Cohen, Thomas Zimmermann
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages1566-1570
Number of pages5
ISBN (Electronic)9781450370431
DOIs
Publication statusPublished - 2020
EventJoint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering 2020 - Virtual, United States of America
Duration: 8 Nov 202013 Nov 2020
Conference number: 28th
https://dl.acm.org/doi/proceedings/10.1145/3368089 (Proceedings)
https://2020.esec-fse.org (Website)

Conference

ConferenceJoint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering 2020
Abbreviated titleESEC/FSE 2020
CountryUnited States of America
CityVirtual
Period8/11/2013/11/20
Internet address

Keywords

  • Distributed system
  • Dynamic slicing
  • Reinforcement learning

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