The Square Kilometre Array (SKA) will generate unprecedented data volumes, making efficient data processing a critical challenge. Within this context, the SKA Regional Centres Network (SRCNet) must operate in a near-exascale environment where traditional data-centric computing models based on moving large datasets to centralised resources are no longer viable due to network and storage bottlenecks. To address this limitation, this work proposes a shift towards distributed and in-situ computing, where computation is moved closer to the data. We explore the integration of Function-as-a-Service (FaaS) with an intelligent decision-making entity based on Evolutionary Algorithms (EAs) to optimise data-intensive workflows within SRCNet. FaaS enables lightweight and modular function execution near data sources while abstracting infrastructure management. The proposed decision-making entity employs Multi-Objective Evolutionary Algorithms (MOEAs) to explore near-optimal execution plans considering execution time and energy consumption, together with constraints related to data location and transfer costs. This work establishes a baseline framework for efficient and cost-aware computation-to-data strategies within the SRCNet architecture.

翻译：平方公里阵列（SKA）将产生前所未有的数据量，使得高效的数据处理成为一项关键挑战。在此背景下，SKA区域中心网络（SRCNet）必须在接近百亿亿次计算规模的环境中运行，而传统的以数据为中心的计算模型——即将大型数据集迁移至集中式资源进行处理——由于网络和存储瓶颈已不再可行。为应对这一局限，本研究提出转向分布式与原位计算模式，使计算更贴近数据。我们探索了将函数即服务（FaaS）与基于进化算法（EAs）的智能决策实体相结合，以优化SRCNet内的数据密集型工作流。FaaS支持在数据源附近执行轻量级、模块化的函数，同时抽象化基础设施管理。所提出的决策实体采用多目标进化算法（MOEAs）来探索近似最优的执行方案，综合考虑执行时间和能耗，以及数据位置与传输成本相关的约束条件。本研究为SRCNet架构内高效且成本敏感的计算向数据策略建立了基础框架。