Function-as-a-Service (FaaS) is a type of serverless computing that allows developers to write and deploy code as individual functions, which can be triggered by specific events or requests. FaaS platforms automatically manage the underlying infrastructure, scaling it up or down as needed, being highly scalable, cost-effective and offering a high level of abstraction. Prototypes being developed within the SKA Regional Center Network (SRCNet) are exploring models for data distribution, software delivery and distributed computing with the goal of moving and executing computation to where the data is. Since SKA will be the largest data producer on the planet, it will be necessary to distribute this massive volume of data to the SRCNet nodes that will serve as a hub for computing and analysis operations on the closest data. Within this context, in this work we want to validate the feasibility of designing and deploying functions and applications commonly used in radio interferometry workflows within a FaaS platform to demonstrate the value of this computing model as an alternative to explore for data processing in the distributed nodes of the SRCNet. We have analyzed several FaaS platforms and successfully deployed one of them, where we have imported several functions using two different methods: microfunctions from the CASA framework, which are written in Python code, and highly specific native applications like wsclean. Therefore, we have designed a simple catalogue that can be easily scaled to provide all the key features of FaaS in highly distributed environments using orchestrators, as well as having the ability to integrate them with workflows or APIs. This paper contributes to the ongoing discussion of the potential of FaaS models for scientific data processing, particularly in the context of large-scale, distributed projects such as SKA.

翻译：函数即服务（FaaS）是一种无服务器计算类型，允许开发者将代码编写并部署为独立函数，这些函数可通过特定事件或请求触发。FaaS平台自动管理底层基础设施，根据需求动态扩展或缩减资源，具有高度可扩展性、成本效益高且提供高级抽象能力。在平方公里阵列区域中心网络（SRCNet）框架内开发的多个原型正在探索数据分发、软件交付及分布式计算的模型，其目标是将计算任务迁移到数据所在位置执行。由于SKA将成为全球最大的数据生产者，有必要将海量数据分发至SRCNet节点——这些节点将作为数据邻近区域的计算与分析中枢。在此背景下，本研究旨在验证在FaaS平台中设计并部署射电干涉测量工作流常用函数与应用程序的可行性，从而论证该计算模型作为SRCNet分布式节点数据处理备选方案的价值。我们分析了多个FaaS平台并成功部署其中之一，通过两种方法导入了多种函数：基于Python编写的CASA框架微函数，以及高度特化的原生应用（如wsclean）。因此，我们设计了一个可轻松扩展的简易目录，借助编排器在高度分布式环境中提供FaaS全部核心功能，并具备将其与工作流或API集成的能力。本文为FaaS模型在科学数据处理中的潜力（尤其针对SKA等大规模分布式项目）的持续讨论作出贡献。