Efficient Fault Tolerance for Pipelined Query Engines via Write-ahead Lineage

Modern distributed pipelined query engines either do not support intra-query fault tolerance or employ high-overhead approaches such as persisting intermediate outputs or checkpointing state. In this work, we present write-ahead lineage, a novel fault recovery technique that combines Spark's lineage-based replay and write-ahead logging. Unlike Spark, where the lineage is determined before query execution, write-ahead lineage persistently logs lineage at runtime to support dynamic task dependencies in pipelined query engines. Since only KB-sized lineages are persisted instead of MB-sized intermediate outputs, the normal execution overhead is minimal compared to spooling or checkpointing based approaches. To ensure fast fault recovery times, tasks only consume intermediate outputs with persisted lineage, preventing global rollbacks upon failure. In addition, lost tasks from different stages can be recovered in a pipelined parallel manner. We implement write-ahead lineage in a distributed pipelined query engine called Quokka. We show that Quokka is around 2x faster than SparkSQL on the TPC-H benchmark with similar fault recovery performance.Comment: ICDE 2024 (copyright IEEE

SATR-DL: Improving Surgical Skill Assessment and Task Recognition in Robot-assisted Surgery with Deep Neural Networks

Purpose: This paper focuses on an automated analysis of surgical motion profiles for objective skill assessment and task recognition in robot-assisted surgery. Existing techniques heavily rely on conventional statistic measures or shallow modelings based on hand-engineered features and gesture segmentation. Such developments require significant expert knowledge, are prone to errors, and are less efficient in online adaptive training systems. Methods: In this work, we present an efficient analytic framework with a parallel deep learning architecture, SATR-DL, to assess trainee expertise and recognize surgical training activity. Through an end-to-end learning technique, abstract information of spatial representations and temporal dynamics is jointly obtained directly from raw motion sequences. Results: By leveraging a shared high-level representation learning, the resulting model is successful in the recognition of trainee skills and surgical tasks, suturing, needle-passing, and knot-tying. Meanwhile, we explore the use of ensemble in classification at the trial level, where the SATR-DL outperforms state-of-the-art performance by achieving accuracies of 0.960 and 1.000 in skill assessment and task recognition, respectively. Conclusion: This study highlights the potential of SATR-DL to provide improvements for an efficient data-driven assessment in intelligent robotic surgery

DPA Load Balancer: Load balancing for Data Parallel Actor-based systems

In this project we explore ways to dynamically load balance actors in a streaming framework. This is used to address input data skew that might lead to stragglers. We continuously monitor actors' input queue lengths for load, and redistribute inputs among reducers using consistent hashing if we detect stragglers. To ensure consistent processing post-redistribution, we adopt an approach that uses input forwarding combined with a state merge step at the end of the processing. We show that this approach can greatly alleviate stragglers for skewed data.Comment: 7 page