Optimization Opportunities for Cloud-Based Data Pipeline Infrastructures
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910097269063680 |
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| author | Jablonski, Johannes Schwarz, Georg-Daniel Heltweg, Philip Riehle, Dirk |
| author_facet | Jablonski, Johannes Schwarz, Georg-Daniel Heltweg, Philip Riehle, Dirk |
| contents | Cloud infrastructure supports the efficient operation of data pipelines regarding requirements like cost, speed, and resource utilization. We present an integrated view of optimization opportunities for cloud-based data pipelines by conducting a systematic review of existing literature on optimization approaches to cloud infrastructure performance for data pipelines. Our study contributes a theory of optimization goals like minimizing cost, reducing execution time, and cost-makespan trade-offs, consisting of dimensions such as single vs. multi-cloud, batch vs. stream processing, etc. We highlight gaps in primary research, including the underexploration of multi-tenant environments and lack of industry evaluation, and suggest directions for future research. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_01954 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Optimization Opportunities for Cloud-Based Data Pipeline Infrastructures Jablonski, Johannes Schwarz, Georg-Daniel Heltweg, Philip Riehle, Dirk Distributed, Parallel, and Cluster Computing Cloud infrastructure supports the efficient operation of data pipelines regarding requirements like cost, speed, and resource utilization. We present an integrated view of optimization opportunities for cloud-based data pipelines by conducting a systematic review of existing literature on optimization approaches to cloud infrastructure performance for data pipelines. Our study contributes a theory of optimization goals like minimizing cost, reducing execution time, and cost-makespan trade-offs, consisting of dimensions such as single vs. multi-cloud, batch vs. stream processing, etc. We highlight gaps in primary research, including the underexploration of multi-tenant environments and lack of industry evaluation, and suggest directions for future research. |
| title | Optimization Opportunities for Cloud-Based Data Pipeline Infrastructures |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2604.01954 |