Investigating the Influence of Data Architecture on the Performance of Kubernetes-Based Applications.
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| Title: | Investigating the Influence of Data Architecture on the Performance of Kubernetes-Based Applications. |
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| Authors: | AN LU1 109305092@nccu.edu.tw, CHUN-FENG LIAO2,3 cfliao@nccu.edu.tw |
| Source: | Journal of Information Science & Engineering. Jan2025, Vol. 41 Issue 1, p25-42. 18p. |
| Subjects: | System administrators, Decision making, Scalability, Popularity, Containers |
| Abstract: | Kubernetes has gained significant importance and popularity as a leading container orchestration platform, facilitating the deployment and management of distributed applications. However, despite its widespread adoption. the exploration of data architecture in the context of Kubernetes-based applications remains limited. The design and configuration of data architecture of a Kubernetes cluster can profoundly impact the overall performance and scalability of applications. This paper aims to investigate the in fluence of data architecture on the performance of Kubernetes-based applications. By analyzing various aspects of data architecture we aim to understand their impact on crucial performance metrics through a series of experiments and performance evaluations. Specifically, we examine different data architecture configurations and their effects on application performance. The outcomes of this research contribute to enhancing the understanding of the relationship between data architecture and the performance of Kubernetes-based applications, empowering developers and system administrators to make informed decisions when optimizing and designing data architecture for their applications in a Kubernetes environment. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Information Science & Engineering is the property of Institute of Information Science, Academia Sinica and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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