Please use this identifier to cite or link to this item:
http://hdl.handle.net/10071/35185
Author(s): | Figueira, J. Coutinho, C. |
Editor: | Francesco Longo Weiming Shen Antonio Padovano |
Date: | 2024 |
Title: | Developing self-adaptive microservices |
Volume: | 232 |
Book title/volume: | Procedia Computer Science |
Pages: | 264 - 273 |
Event title: | 5th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2023 |
Reference: | Figueira, J., & Coutinho, C. (2024). Developing self-adaptive microservices. In F. Longo, W. Shen, & A. Padovano (Eds.), Procedia Computer Science (Vol. 232, pp. 264–273). Elsevier. https://doi.org/10.1016/j.procs.2024.01.026 |
ISSN: | 1877-0509 |
DOI (Digital Object Identifier): | 10.1016/j.procs.2024.01.026 |
Keywords: | Microservices architecture Cloud computing Self-adaptive systems Kubernetes Azure Container Apps |
Abstract: | The modern development approaches are establishing microservices and cloud computing as major trends to benefit the technological community. However, these technologies are often prone to multiple issues regarding parallel development by numerous parties, delivery strategies and resource allocation. This paper proposes a novel architecture for developing self-adaptive microservices, using Kubernetes through the Azure Container Apps, including a strategy that will complement the architecture to enhance the development of microservices and aiming to achieve a solution that allows the readers to deliver software faster, with more resilience, more scalable, and more cost-effective, depending as low as possible from human intervention to maintain and scale. The author will apply the acquired knowledge to propose and test an architecture for a real use case scenario, building a notifications service integrated with a complex cloud-based web application system. |
Peerreviewed: | yes |
Access type: | Open Access |
Appears in Collections: | ISTAR-CRI - Comunicações a conferências internacionais |
Files in This Item:
File | Size | Format | |
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conferenceObject_109448.pdf | 1,41 MB | Adobe PDF | View/Open |
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