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Serverless computing that runs functions with auto-scaling is a popular taskexecution pattern in the cloud-native era. By connecting serverless functionsinto workflows, tenants can achieve complex functionality. Prior researchesadopt the control-flow paradigm to orchestrate a serverless workflow. However,the control-flow paradigm inherently results in long response latency, due tothe heavy data persistence overhead, sequential resource usage, and latefunction triggering. Our investigation shows that the data-flow paradigm hasthe potential to resolve the above problems, with careful design andoptimization. We propose DataFlower, a scheme that achieves the data-flowparadigm for serverless workflows. In DataFlower, a container is abstracted tobe a function logic unit and a data logic unit. The function logic unit runsthe functions, and the data logic unit handles the data transmissionasynchronously. Moreover, a host-container collaborative communicationmechanism is used to support efficient data transfer. Our experimental resultsshow that compared to state-of-the-art serverless designs, DataFlower reducesthe 99\%-ile latency of the benchmarks by up to 35.4\%, and improves the peakthroughput by up to 3.8X.

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