Latest revision as of 10:59, 15 February 2021

Abstract

Container-based application deployments have received significant attention in recent years. Operating system virtualization based on containers as a mechanism to deploy and manage complex, large-scale software systems has become a popular mechanism for application deployment and operation. Packaging application components into selfcontained artifacts has brought substantial flexibility to developers and operation teams alike. However, this flexibility comes at a price. Practitioners need to respect numerous constraints ranging from security and compliance requirements, to specific regulatory conditions. Fulfilling these requirements is especially challenging in specialized domains with large numbers of stakeholders.Moreover, the rapidly growing number of container images to be managed due to the introduction of new or updated applications and respective components, leads to significant challenges for container management and adaptation. In this paper, we introduce Smart Brix, a framework for continuous evolution of container application deployments that tackles these challenges. Smart Brix integrates andunifies concepts of continuous integration, runtimemonitoring, and operational analytics. Furthermore, it allows practitioners to define generic analytics and compensation pipelines composed of self-assembling processing components to autonomously validate and verify containers to be deployed.We illustrate the feasibility of our approach by evaluating our framework using a case study from the smart city domain. We show that Smart Brix is horizontally scalable and runtime of the implemented analysis and compensation pipelines scales linearly with the number of container application packages. Subjects Adaptive and Self-Organizing Systems, Distributed and Parallel Computing, Software Engineering

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• https://peerj.com/articles/cs-66.pdf under the license https://creativecommons.org/licenses/by

• https://peerj.com/articles/cs-66.pdf,

https://peerj.com/articles/cs-66,

https://core.ac.uk/display/90428897,

https://dblp.uni-trier.de/db/journals/peerj-cs/peerj-cs2.html#SchleicherVID16,

http://dx.doi.org/10.7717/peerj-cs.66,

https://dx.doi.org/10.7717/peerj-cs.66,

https://doaj.org/article/1fe1b2fb06f04446b03a8abfba4d467f,

https://doi.org/10.7717/peerj-cs.66,

https://dsg.tuwien.ac.at/staff/inzinger/bibliography/Schleicher_2016smartbrix.html,

https://academic.microsoft.com/#/detail/2428059906 under the license cc-by

• https://peerj.com/articles/cs-66.pdf,

https://peerj.com/articles/cs-66,

https://doaj.org/toc/2376-5992

• https://peerj.com/articles/cs-66.pdf,

https://peerj.com/articles/cs-66.xml,

https://peerj.com/articles/cs-66.html,

http://dx.doi.org/10.7717/peerj-cs.66

under the license http://creativecommons.org/licenses/by/4.0/