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Managing Feature Compatibility in Kubernetes: Vendor Comparison and Analysis

Authors: Truyen, Eddy; Kratzke, Nane; Van Landuyt, Dimitri; Lagaisse, Bert; Joosen, Wouter;

Managing Feature Compatibility in Kubernetes: Vendor Comparison and Analysis

Abstract

Kubernetes (k8s) is a kind of cluster operating system for cloud-native workloads that has become a de-facto standard for container orchestration. Provided by more than one hundred vendors, it has the potential to protect the customer from vendor lock-in. However, the open-source k8s distribution consists of many optional and alternative features that must be explicitly activated and may depend on pre-configured system components. As a result, incompatibilities still may ensue among Kubernetes vendors. Mostly managed k8s services typically restrict the customizability of Kubernetes. This paper firstly compares the most relevant k8s vendors and, secondly, analyses the potential of Kubernetes to detect and configure compatible support for required features across vendors in a uniform manner. Our comparison is performed based on documented features, by testing, and by inspection of the configuration state of running clusters. Our analysis focuses on the potential of the end-to-end testing suite of Kubernetes to detect support for a desired feature in any Kubernetes vendor and the possibility of reconfiguring the studied vendors with missing features in a uniform manner. Our findings are threefold: First, incompatibilities arise between default cluster configurations of the studied vendors for approximately 18% of documented features. Second, matching end-to-end tests exist only for around 64% of features and for 17% of features these matching tests are not well developed for all vendors. Third, almost all feature incompatibilities can be resolved using a vendor-agnostic API. These insights are beneficial to avoid feature incompatibilities already in cloud-native application engineering processes. Moreover, the end-to-end testing suite can be extended in currently unlighted areas to provide better feature coverage.

Keywords

Technology, Computer systems organization, Testing, 46 Information and computing sciences, Containers, 09 Engineering, Licenses, Engineering, SYSTEMS, 10 Technology, MANAGEMENT, Cloud computing, 40 Engineering, Science & Technology, Computer Science, Information Systems, Configuration management, distributed architectures, cloud computing, architectures, Engineering, Electrical & Electronic, Open source software, CLOUD, TK1-9971, Computer Science, Telecommunications, Feature extraction, 08 Information and Computing Sciences, Electrical engineering. Electronics. Nuclear engineering

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    popularity
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    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Top 10%
Average
Top 10%
Green
gold