Repositori institucional URV
| Autor/es de la URV: | Eizaguirre Suárez, Germán Telmo / Sanchez Artigas, Marc |
| Autor según el artículo: | Eizaguirre, GT; Sánchez-Artigas, M |
| Direcció de correo del autor: | germantelmo.eizaguirre@urv.cat germantelmo.eizaguirre@urv.cat marc.sanchez@urv.cat marc.sanchez@urv.cat |
| Identificador del autor: | 0000-0002-2865-9873 0000-0002-2865-9873 0000-0002-9700-7318 0000-0002-9700-7318 |
| Año de publicación de la revista: | 2024-01-01 |
| Tipo de publicación: | Journal Publications |
| Referencia de l'ítem segons les normes APA: | Eizaguirre, GT; Sánchez-Artigas, M (2024). A Seer knows best: Auto-tuned object storage shuffling for serverless analytics. Journal Of Parallel And Distributed Computing, 183(), 104763-. DOI: 10.1016/j.jpdc.2023.104763 |
| Referencia al articulo segun fuente origial: | Journal Of Parallel And Distributed Computing. 183 104763- |
| Resumen: | Serverless platforms offer high resource elasticity and pay-as-you-go billing, making them a compelling choice for data analytics. To craft a “pure” serverless solution, the common practice is to transfer intermediate data between serverless functions via serverless object storage (IBM COS; AWS S3). However, prior works have led to inconclusive results about the performance of object storage systems, since they have left large margin for optimization. To verify that object storage has been underrated, we devise a novel shuffle manager for serverless data analytics called SEER. Specifically, SEER dynamically chooses between two shuffle algorithms to maximize performance. The algorithm choice is made online based on some predictive models, and very importantly, without end users having to specify intermediate shuffle data sizes at the time of the job submission. We integrate SEER with PyWren-IBM [31], a well-known serverless analytics framework, and evaluate it against both serverful (e.g., Spark) and serverless systems (e.g., Google BigQuery, Caerus [46] and SONIC [22]). Our results certify that our new shuffle manager can deliver performance improvements over them. |
| DOI del artículo: | 10.1016/j.jpdc.2023.104763 |
| Enlace a la fuente original: | https://www.sciencedirect.com/science/article/pii/S0743731523001338 |
| Versión del articulo depositado: | info:eu-repo/semantics/publishedVersion |
| Acceso a la licencia de uso: | https://creativecommons.org/licenses/by/3.0/es/ |
| Departamento: | Enginyeria Informàtica i Matemàtiques |
| URL Documento de licencia: | https://repositori.urv.cat/ca/proteccio-de-dades/ |
| Áreas temáticas: | Theoretical computer science Software Hardware and architecture Computer science, theory & methods Computer networks and communications Ciência da computação Artificial intelligence |
| Palabras clave: | Shuffle Serverless computing Object storage I/o optimization |
| Entidad: | Universitat Rovira i Virgili |
| Fecha de alta del registro: | 2026-05-09 |
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| DocumentPrincipal | DocumentPrincipal | application/pdf |
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