Energy Profiling of Data-Sharing Pipelines: Modeling, Estimation, and Reuse Strategies
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
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| _version_ | 1866915653071405056 |
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| author | Masoudi, Sepideh Werner, Sebastian Plebani, Pierluigi Tai, Stefan |
| author_facet | Masoudi, Sepideh Werner, Sebastian Plebani, Pierluigi Tai, Stefan |
| contents | Data-sharing pipelines involve a series of stages that apply policy-based data transformations to enable secure and effective data exchange among organizations. Although numerous tools and platforms exist to manage governance and enforcement in these pipelines, energy efficiency in data exchange has received limited attention. This paper introduces a novel method to model and estimate the energy consumption of different execution configurations in data-sharing pipelines. Additionally, this method identifies reuse potential in shared stages across pipelines that hold the key to reducing energy in large data-sharing federations. We validate this method through simulation experiments, revealing promising potential for cross-organizational pipeline optimization and laying a foundation for energy-conscious execution strategies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_04086 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Energy Profiling of Data-Sharing Pipelines: Modeling, Estimation, and Reuse Strategies Masoudi, Sepideh Werner, Sebastian Plebani, Pierluigi Tai, Stefan Databases Distributed, Parallel, and Cluster Computing Data-sharing pipelines involve a series of stages that apply policy-based data transformations to enable secure and effective data exchange among organizations. Although numerous tools and platforms exist to manage governance and enforcement in these pipelines, energy efficiency in data exchange has received limited attention. This paper introduces a novel method to model and estimate the energy consumption of different execution configurations in data-sharing pipelines. Additionally, this method identifies reuse potential in shared stages across pipelines that hold the key to reducing energy in large data-sharing federations. We validate this method through simulation experiments, revealing promising potential for cross-organizational pipeline optimization and laying a foundation for energy-conscious execution strategies. |
| title | Energy Profiling of Data-Sharing Pipelines: Modeling, Estimation, and Reuse Strategies |
| topic | Databases Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2512.04086 |