Serverless computing is a paradigm in which the underlying infrastructure is fully managed by the provider, enabling applications and services to be executed with elastic resource provisioning and minimal operational overhead. A core model within this paradigm is Function-as-a-Service (FaaS), where lightweight functions are deployed and triggered on demand, scaling seamlessly with workload. FaaS offers flexibility, cost-effectiveness, and fine-grained scalability, qualities particularly relevant for large-scale scientific infrastructures where data volumes are too large to centralise and computation must increasingly occur close to the data. The Square Kilometre Array Observatory (SKAO) exemplifies this challenge. Once operational, it will generate about 700~PB of data products annually, distributed across the SKA Regional Centre Network (SRCNet), a federation of international centres providing storage, computing, and analysis services. In such a context, FaaS offers a mechanism to bring computation to the data. We studied the principles of serverless and FaaS computing and explored their application to radio astronomy workflows. Representative functions for astrophysical data analysis were developed and deployed, including micro-functions derived from existing libraries and wrappers around domain-specific applications. In particular, a Gaussian convolution function was implemented and integrated within the SRCNet ecosystem. The use case demonstrates that FaaS can be embedded into the existing SRCNet ecosystem of services, allowing functions to run directly at sites where data replicas are stored. This reduces latency, minimises transfers, and improves efficiency, aligning with federated, data-proximate computation. The results show that serverless models provide a scalable and efficient pathway to address the data volumes of the SKA era.

翻译：无服务器计算是一种底层基础设施完全由提供商管理的范式，使得应用和服务能够以弹性资源供给和最小运维开销的方式执行。该范式中的核心模型是函数即服务（FaaS），其中轻量级函数按需部署和触发，并能随工作负载无缝扩展。FaaS提供了灵活性、成本效益和细粒度可扩展性，这些特性对于大规模科学基础设施尤为重要，因为此类设施的数据量过大而无法集中存储，且计算必须日益靠近数据所在位置。平方公里阵列天文台（SKAO）正是这一挑战的典型代表。其投入运行后，每年将产生约700PB的数据产品，这些数据分布在SKA区域中心网络（SRCNet）中——这是一个提供存储、计算和分析服务的国际中心联盟。在此背景下，FaaS提供了一种将计算带至数据的机制。我们研究了无服务器和FaaS计算的原理，并探索了其在射电天文工作流中的应用。我们开发并部署了用于天体物理数据分析的代表性函数，包括从现有库派生的微函数以及对领域特定应用的封装函数。特别地，我们实现了一个高斯卷积函数并将其集成到SRCNet生态系统中。该用例表明，FaaS可以嵌入到现有的SRCNet服务生态系统中，使函数能够在存储数据副本的站点直接运行。这降低了延迟、减少了数据传输并提高了效率，符合联邦化就近计算的原则。结果表明，无服务器模型为应对SKA时代的数据规模提供了一条可扩展且高效的途径。