一句话：提出首个面向持续运行分布式程序、支持预算约束的分布式在线动态切片器 Dads。
论文针对持续运行的分布式程序，提出动态切片器 Dads。现有动态切片在分布式与长期运行场景下难以兼顾可扩展性、在线分析与成本效益。Dads 按分布式架构设计以利用分布式并行资源，并通过在线分析避免记录执行轨迹，从而节省时间与空间开销。其关键机制是利用强化学习，根据用户给定的分析时间预算持续自动调整分析算法配置，以维持实用的可扩展性与成本效益折衷。作者在八个真实 Java 分布式系统上开展实证评估，展示了 Dads 的可扩展性与成本效益优势。该工具已开源并提供演示视频。
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.