Szczegóły publikacji
Opis bibliograficzny
Auto-scaling of scientific workflows in Kubernetes / Bartosz BALIŚ, Andrzej Broński, Mateusz Szarek // W: Computational Science – ICCS 2022 : 22nd international conference : London, UK, June 21–23, 2022 : proceedings, Pt. 2 / eds. Derek Groen, [et al.]. — Cham : Springer Nature Switzerland, cop. 2022. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 13351). — ISBN: 978-3-031-08753-0; e-ISBN: 978-3-031-08754-7. — S. 33–40. — Bibliogr., Abstr. — Publikacja dostępna online od: 2022-06-15
Autorzy (3)
Słowa kluczowe
Dane bibliometryczne
ID BaDAP | 140682 |
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Data dodania do BaDAP | 2022-06-24 |
DOI | 10.1007/978-3-031-08754-7_5 |
Rok publikacji | 2022 |
Typ publikacji | materiały konferencyjne (aut.) |
Otwarty dostęp | |
Wydawca | Springer |
Konferencja | 22nd International Conference on Computational Science |
Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
Kubernetes has gained extreme popularity as a cloud-native platform for distributed applications. However, scientific computations which typically consist of a large number of jobs – such as scientific workflows – are not typical workloads for which Kubernetes was designed. In this paper, we investigate the problem of autoscaling, i.e. adjusting the computing infrastructure to the current resource demands. We propose a solution for auto-scaling that takes advantage of the known workflow structure to improve scaling decisions by predicting resource demands for the near future. Such a predictive autoscaling policy is experimentally evaluated and compared to a regular reactive policy where only the current demand is taken into account. The experimental evaluation is done using the HyperFlow workflow management systems running five simultaneous instances of the Montage workflow on a Kubernetes cluster deployed in the Google Cloud Platform. The results indicate that the predictive policy allows achieving better elasticity and execution time, while reducing monetary cost.