DataFlower: Exploiting the Data-flow Paradigm for Serverless Workflow Orchestration
PROCEEDINGS OF THE 28TH ACM INTERNATIONAL CONFERENCE ON ARCHITECTURAL SUPPORT FOR PROGRAMMING LANGUAGES AND OPERATING SYSTEMS, ASPLOS 2023, VOL 4(2023)
摘要
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. Our investigation shows that the data-flow paradigm has the potential to resolve the above problems, with careful design and optimization. We propose DataFlower, a scheme that achieves the data-flow paradigm for serverless workflows. In DataFlower, a container is abstracted to be a function logic unit and a data logic unit. The function logic unit runs the functions, and the data logic unit handles the data transmission asynchronously. Moreover, a host-container collaborative communication mechanism is used to support efficient data transfer. Our experimental results show that compared to state-of-the-art serverless designs, DataFlower reduces the 99%-ile latency of the benchmarks by up to 35.4%, and improves the peak throughput by up to 3.8X.
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关键词
FaaS,Function-as-a-Service,serverless workflow,workflow orchestration,control-flow paradigm,data-flow paradigm
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