Please use this identifier to cite or link to this item: https://doi.org/10.21256/zhaw-29553
Publication type: Conference paper
Type of review: Peer review (publication)
Title: Workload deployment and configuration reconciliation at scale in kubernetes-based edge-cloud continuums
Authors: Hass, Daniel
Spillner, Josef
et. al: No
DOI: 10.1109/ISPDC55340.2022.00026
10.21256/zhaw-29553
Proceedings: 2022 21st International Symposium on Parallel and Distributed Computing (ISPDC)
Page(s): 121
Pages to: 128
Conference details: 21st International Symposium on Parallel and Distributed Computing (ISPDC), Basel, Switzerland, 11-13 July 2022
Issue Date: 11-Jul-2022
Publisher / Ed. Institution: IEEE
ISBN: 978-1-6654-8802-0
ISSN: 2379-5352
Language: English
Subjects: Continuum computing; Scalability; Configuration management; Container
Subject (DDC): 005: Computer programming, programs and data
Abstract: Continuum computing promises the abstraction of physical node location and node platform stack in order to create a seamless application deployment and execution across edges and cloud data centres. For industrial IoT applications, the demand to generate data insights in conjunction with an installed base of increasingly capable edge devices is calling for appropriate continuum computing interfaces. Derived from a case study in industrial water flow monitoring and based on the industry's de-facto standard Kubernetes to deploy complex containerised workloads, we present an appropriate continuum deployment mechanism based on custom Kubernetes controllers and CI/CD, called Kontinuum Controller. Through synthetic experiments and a holistic cross-provider deployment, we investigate its scalability with emphasis on reconciling adjusted configuration per application and per node, a critical requirement by industrial customers. Our findings convey that Kubernetes by default would enter undesirable oscillation already for modestly sized deployments. Thus, we also discuss possible solutions.
URI: https://digitalcollection.zhaw.ch/handle/11475/29553
Fulltext version: Accepted version
License (according to publishing contract): Licence according to publishing contract
Departement: School of Engineering
Organisational Unit: Institute of Computer Science (InIT)
Appears in collections:Publikationen School of Engineering

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Hass, D., & Spillner, J. (2022). Workload deployment and configuration reconciliation at scale in kubernetes-based edge-cloud continuums [Conference paper]. 2022 21st International Symposium on Parallel and Distributed Computing (ISPDC), 121–128. https://doi.org/10.1109/ISPDC55340.2022.00026
Hass, D. and Spillner, J. (2022) ‘Workload deployment and configuration reconciliation at scale in kubernetes-based edge-cloud continuums’, in 2022 21st International Symposium on Parallel and Distributed Computing (ISPDC). IEEE, pp. 121–128. Available at: https://doi.org/10.1109/ISPDC55340.2022.00026.
D. Hass and J. Spillner, “Workload deployment and configuration reconciliation at scale in kubernetes-based edge-cloud continuums,” in 2022 21st International Symposium on Parallel and Distributed Computing (ISPDC), Jul. 2022, pp. 121–128. doi: 10.1109/ISPDC55340.2022.00026.
HASS, Daniel und Josef SPILLNER, 2022. Workload deployment and configuration reconciliation at scale in kubernetes-based edge-cloud continuums. In: 2022 21st International Symposium on Parallel and Distributed Computing (ISPDC). Conference paper. IEEE. 11 Juli 2022. S. 121–128. ISBN 978-1-6654-8802-0
Hass, Daniel, and Josef Spillner. 2022. “Workload Deployment and Configuration Reconciliation at Scale in Kubernetes-Based Edge-Cloud Continuums.” Conference paper. In 2022 21st International Symposium on Parallel and Distributed Computing (ISPDC), 121–28. IEEE. https://doi.org/10.1109/ISPDC55340.2022.00026.
Hass, Daniel, and Josef Spillner. “Workload Deployment and Configuration Reconciliation at Scale in Kubernetes-Based Edge-Cloud Continuums.” 2022 21st International Symposium on Parallel and Distributed Computing (ISPDC), IEEE, 2022, pp. 121–28, https://doi.org/10.1109/ISPDC55340.2022.00026.


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