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

arXiv: https://arxiv.org/abs/2109.13492 · HTML: https://mlsanity.com/paper/2109.13492/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2021.

## Built on by (most cited first, 7 of 7)

- [ServerlessLLM: Low-Latency Serverless Inference for Large Language Models](https://mlsanity.com/paper/2401.14351/) (2024)
- [How Does It Function? Characterizing Long-term Trends in Production Serverless Workloads](https://mlsanity.com/paper/2312.10127/) (2023)
- [Trusted AI Agents in the Cloud](https://mlsanity.com/paper/2512.05951/) (2025)
- [SALINA: Towards Sustainable Live Sonar Analytics in Wild Ecosystems](https://mlsanity.com/paper/2410.19742/) (2024)
- [LIFL: A Lightweight, Event-driven Serverless Platform for Federated Learning](https://mlsanity.com/paper/2405.10968/) (2024)
- [Keep Your Friends Close: Leveraging Affinity Groups to Accelerate AI Inference Workflows](https://mlsanity.com/paper/2312.11488/) (2023)
- [LegoDiffusion: Micro-Serving Text-to-Image Diffusion Workflows](https://mlsanity.com/paper/2604.08123/) (2026)

## Abstract (arXiv)

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. In this paper, 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 Pheromone -- a scalable, low-latency serverless platform following this data-centric design. Compared to well-established commercial and open-source platforms, Pheromone cuts the latencies of function interactions and data exchanges by orders of magnitude, scales to large workflows, and enables easy implementation of complex applications.