Bringing computation to the data: A MOEA-driven approach for optimising data processing in the context of the SKA and SRCNet
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866912802519646208 |
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| author | Parra-Royón, Manuel Rodríguez-Gallardo, Álvaro Sánchez-Expósito, Susana Darriba-Pol, Laura Sánchez-Castañeda, Jesús Mendoza, M. Ángeles Garrido, Julián Moldón, Javier Verdes-Montenegro, Lourdes |
| author_facet | Parra-Royón, Manuel Rodríguez-Gallardo, Álvaro Sánchez-Expósito, Susana Darriba-Pol, Laura Sánchez-Castañeda, Jesús Mendoza, M. Ángeles Garrido, Julián Moldón, Javier Verdes-Montenegro, Lourdes |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_01980 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Bringing computation to the data: A MOEA-driven approach for optimising data processing in the context of the SKA and SRCNet Parra-Royón, Manuel Rodríguez-Gallardo, Álvaro Sánchez-Expósito, Susana Darriba-Pol, Laura Sánchez-Castañeda, Jesús Mendoza, M. Ángeles Garrido, Julián Moldón, Javier Verdes-Montenegro, Lourdes Distributed, Parallel, and Cluster Computing 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. |
| title | Bringing computation to the data: A MOEA-driven approach for optimising data processing in the context of the SKA and SRCNet |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2601.01980 |