Evaluating Function-as-a-Service (FaaS) frameworks for the Accelerator Control System

TL;DR

This paper assesses the suitability of Function-as-a-Service frameworks for particle accelerator control systems, focusing on performance, scalability, and integration for real-time, trigger-based workloads.

Contribution

It provides a comprehensive evaluation of open-source FaaS platforms in the context of accelerator control, highlighting their advantages and limitations for real-time scientific applications.

Findings

FaaS platforms can meet real-time control requirements with proper deployment.

Self-hosted FaaS offers better control and integration with legacy systems.

Performance varies based on workload and network conditions.

Abstract

As particle accelerator control systems evolve in complexity and scale, the need for responsive, scalable, and cost-effective computational infrastructure becomes increasingly critical. Function-as-a-Service (FaaS) offers an alternative to traditional monolithic architecture by enabling event-driven execution, automatic scaling, and fine-grained resource utilization. This paper explores the applicability and performance of FaaS frameworks in the context of a modern particle accelerator control system, with the objective of evaluating their suitability for short lived and triggered workloads. In this paper, we evaluate prominent open-source FaaS platforms in executing functional logic, triggers, and diagnostics routines. Evaluation metrics consist of cold-start latency, scalability, performance, integration with other open-source tools like Kafka. Experimental workloads were designed to simulate real-world control tasks when implemented as stateless FaaS functions. These workloads were benchmarked under various invocation loads and network conditions. Self-hosted FaaS platforms, when deployed within accelerator networks, offer greater control over execution environment, better integration with legacy systems, and support for real-time guarantees when paired with message queues. Based on lessons learned and evaluation metrics, this paper describes reliability of the FaaS framework for the Accelerator Control Systems (ACS).