Public Cloud Kubernetes Storage Performance Analysis

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F19%3A50016283" target="_blank" >RIV/62690094:18450/19:50016283 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-030-28374-2_56" target="_blank" >http://dx.doi.org/10.1007/978-3-030-28374-2_56</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-28374-2_56" target="_blank" >10.1007/978-3-030-28374-2_56</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Public Cloud Kubernetes Storage Performance Analysis

Popis výsledku v původním jazyce

Public cloud solutions offer a lot of possibilities for application configuration. The well-chosen architecture and configuration are important for the correct and efficient application run and affect performance and also user friendliness. If the application runs on top of Kubernetes cluster on public cloud solutions (e.g. Amazon Elastic Container Service for Kubernetes, Google Kubernetes Engine, Azure Kubernetes Service) it is necessary, among other things, also decide what data storage to use and this decision should be based on the workload type and requirements for other application and storage parameters. However, it is not easy to say which technology is the best or to use some simple test to get this result or a similar one. The main analysis and tests in this paper have been done on Microsoft’s Azure Kubernetes Service where have been tested these storage back-ends: AKS native storage class, AWS cloud volume mapped into the instance (via Azure hostPath with attached Azure managed disk), OpenEBS, Portworx, Gluster with Heketi and Ceph with Rook.

Název v anglickém jazyce

Public Cloud Kubernetes Storage Performance Analysis

Popis výsledku anglicky

Public cloud solutions offer a lot of possibilities for application configuration. The well-chosen architecture and configuration are important for the correct and efficient application run and affect performance and also user friendliness. If the application runs on top of Kubernetes cluster on public cloud solutions (e.g. Amazon Elastic Container Service for Kubernetes, Google Kubernetes Engine, Azure Kubernetes Service) it is necessary, among other things, also decide what data storage to use and this decision should be based on the workload type and requirements for other application and storage parameters. However, it is not easy to say which technology is the best or to use some simple test to get this result or a similar one. The main analysis and tests in this paper have been done on Microsoft’s Azure Kubernetes Service where have been tested these storage back-ends: AKS native storage class, AWS cloud volume mapped into the instance (via Azure hostPath with attached Azure managed disk), OpenEBS, Portworx, Gluster with Heketi and Ceph with Rook.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Computational Collective Intelligence

ISBN

978-3-030-28373-5

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

12

Strana od-do

649-660

Název nakladatele

Springer

Místo vydání

Cham

Místo konání akce

Hendaye

Datum konání akce

4. 9. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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