Pheromone: Restructuring Serverless Computing With Data-Centric Function Orchestration

Serverless applications are typically composed of function workflows in which multiple short-lived functions are triggered to exchange data in response to events or state changes. Current serverless platforms coordinate and trigger functions by following high-level invocation dependencies but are oblivious to the underlying data exchanges between functions. This design is neither efficient nor easy to use in orchestrating complex workflows – developers often have to manage complex function interactions by themselves, with customized implementation and unsatisfactory performance. Therefore, we argue that function orchestration should follow a data-centric approach. In our design, the platform provides a data bucket abstraction to hold the intermediate data generated by functions. Developers can use a rich set of data trigger primitives to control when and how the output of each function should be passed to the next functions in a workflow. By making data consumption explicit and allowing it to trigger functions and drive the workflow, complex function interactions can be easily and efficiently supported. We present <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pheromone</monospace> – a scalable, low-latency serverless platform following this data-centric design. Compared to well-established commercial and open-source platforms, <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Pheromone</monospace> cuts the latencies of function interactions and data exchanges by orders of magnitude, scales to large workflows, and enables easy implementation of complex applications.