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Microservices Data Mining for Analytics Feedback and Optimization

Microservices Data Mining for Analytics Feedback and Optimization

Kindson Munonye, Péter Martinek
Copyright: © 2021 |Volume: 17 |Issue: 1 |Pages: 22
ISSN: 1548-1115|EISSN: 1548-1123|EISBN13: 9781799859529|DOI: 10.4018/IJEIS.2021010102
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MLA

Munonye, Kindson, and Péter Martinek. "Microservices Data Mining for Analytics Feedback and Optimization." IJEIS vol.17, no.1 2021: pp.22-43. http://doi.org/10.4018/IJEIS.2021010102

APA

Munonye, K. & Martinek, P. (2021). Microservices Data Mining for Analytics Feedback and Optimization. International Journal of Enterprise Information Systems (IJEIS), 17(1), 22-43. http://doi.org/10.4018/IJEIS.2021010102

Chicago

Munonye, Kindson, and Péter Martinek. "Microservices Data Mining for Analytics Feedback and Optimization," International Journal of Enterprise Information Systems (IJEIS) 17, no.1: 22-43. http://doi.org/10.4018/IJEIS.2021010102

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Abstract

When microservices-based architectures are adopted for an enterprise application, a basic requirement would be an evaluation of the performance with the objective of continuous monitoring and improved efficiency. This evaluation helps businesses obtain a quantitative measure of the benefits of a shift from monolith to microservices. Additionally, the metrics obtained could be used as a mechanism for continuous improvement of production application. This research proposes a model based on the principles of data mining called stream analytics feedback and optimization (SAFAO), which can be used to achieve a continuous optimization of microservices. Stream analytics is due to the fact that the analysis is performed on online application with continuously generated lived data. This approach has been tested in a simulated production environment based on Docker containers. The authors were able to establish empirical measures which were continuously extracted via a data mining methodology and then fed back into the running application through configuration management. The results show a continuous improvement in the performance of the microservices as indicated in the results presented in this research.

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