一句话：提出 DataFlower，用数据流范式编排 Serverless 工作流，以降低控制流范式导致的高响应延迟。
在云原生场景中，Serverless 函数常被连接成工作流以实现复杂功能。现有编排采用控制流范式，但由于数据持久化开销大、资源使用串行以及函数触发滞后，导致响应延迟较长。本文提出 DataFlower，转向数据流范式来编排 Serverless 工作流，通过以数据流动驱动函数执行与资源调度，避免控制流范式的固有瓶颈，从而缩短响应延迟。摘要未给出具体实现机制、实验设置与量化结果。
Serverless computing that runs functions with auto-scaling is a popular task execution pattern in the cloud-native era. By connecting serverless functions into workflows, tenants can achieve complex functionality. Prior research adopts the control-flow paradigm to orchestrate a serverless workflow. However, the control-flow paradigm inherently results in long response latency, due to the heavy data persistence overhead, sequential resource usage, and late function triggering.
@inproceedings{paperbot10119,
title = {DataFlower: Exploiting the Data-flow Paradigm for Serverless Workflow Orchestration},
author = {Zijun Li and Chuhao Xu and Quan Chen and Jieru Zhao and Chen Chen and Minyi Guo},
booktitle = {ASPLOS},
year = {2023},
doi = {10.1145/3623278.3624755}
}