Szczegóły publikacji
Opis bibliograficzny
A serverless engine for high energy physics distributed analysis / Jacek Kuśnierz, Vincenzo E. Padulano, Maciej MALAWSKI, Kamil Burkiewicz, Enric Tejedor Saavedra, Pedro Alonso-Jordá, Michael Pitt, Valentina Avati // W: CCGrid 2022 [Dokument elektroniczny] : 22nd IEEE/ACM international symposium on Cluster, Cloud and Internet Computing : 16–19 May 2022, Taormina (Messina), Italy : proceedings / eds. Maria Fazio, [et al.]. — Wersja do Windows. — Dane tekstowe. — Piscataway : IEEE, cop. 2022. — Dod. ISBN: 978-1-6654-9957-6. — e-ISBN: 978-1-6654-9956-9. — S. 575–584. — Wymagania systemowe: Adobe Reader. — Bibliogr. s. 584, Abstr. — Publikacja dostępna online od: 2022-07-19. — J. Kuśnierz - dod. afiliacja: TUM Munich, Germany; V. Avati - afiliacja: CERN, Geneva, Switzerland
Autorzy (8)
- AGHKuśnierz Jacek
- Padulano Vincenzo Eduardo
- AGHMalawski Maciej
- AGHBurkiewicz Kamil
- Saavedra Enric Tejedor
- Alonso-Jordá Pedro
- Pitt Michael
- Avati Valentina
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 141253 |
|---|---|
| Data dodania do BaDAP | 2022-08-24 |
| Tekst źródłowy | URL |
| DOI | 10.1109/CCGrid54584.2022.00067 |
| Rok publikacji | 2022 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Institute of Electrical and Electronics Engineers (IEEE) |
| Konferencja | IEEE International Symposium on Cluster, Cloud and Grid Computing 2022 |
Abstract
The Large Hadron Collider (LHC) at CERN has generated in the last decade an unprecedented volume of data for the High-Energy Physics (HEP) field. Scientific collaborations interested in analysing such data very often require computing power beyond a single machine. This issue has been tackled traditionally by running analyses in distributed environments using stateful, managed batch computing systems. While this approach has been effective so far, current estimates for future computing needs of the field present large scaling challenges. Such a managed approach may not be the only viable way to tackle them and an interesting alternative could be provided by serverless architectures, to enable an even larger scaling potential. This work describes a novel approach to running real HEP scientific applications through a distributed serverless computing engine. The engine is built upon ROOT, a well-established HEP data analysis software, and distributes its computations to a large pool of concurrent executions on Amazon Web Services Lambda Serverless Platform. Thanks to the developed tool, physicists are able to access datasets stored at CERN (also those that are under restricted access policies) and process it on remote infrastructures outside of their typical environment. The analysis of the serverless functions is monitored at runtime to gather performance metrics, both for data- and computation-intensive workloads.
