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Serverless applications are typically composed of function workflows in whichmultiple short-lived functions are triggered to exchange data in response toevents or state changes. Current serverless platforms coordinate and triggerfunctions by following high-level invocation dependencies but are oblivious tothe underlying data exchanges between functions. This design is neitherefficient nor easy to use in orchestrating complex workflows -- developersoften have to manage complex function interactions by themselves, withcustomized implementation and unsatisfactory performance. In this paper, weargue that function orchestration should follow a data-centric approach. In ourdesign, the platform provides a data bucket abstraction to hold theintermediate data generated by functions. Developers can use a rich set of datatrigger primitives to control when and how the output of each function shouldbe passed to the next functions in a workflow. By making data consumptionexplicit and allowing it to trigger functions and drive the workflow, complexfunction interactions can be easily and efficiently supported. We presentPheromone -- a scalable, low-latency serverless platform following thisdata-centric design. Compared to well-established commercial and open-sourceplatforms, Pheromone cuts the latencies of function interactions and dataexchanges by orders of magnitude, scales to large workflows, and enables easyimplementation of complex applications.

Following the Data, Not the Function: Rethinking Function Orchestration in Serverless Computing