DataFlower: Exploiting the Data-flow Paradigm for Serverless Workflow Orchestration

Abstract

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.