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Analysis of a many-objective optimization approach for identifying microservices from legacy systems

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Abstract

The expensive maintenance of legacy systems leads companies to migrate such systems to modern architectures. Microservice architectural style has become a trend to modernize monolithic legacy systems. A microservice architecture consists of small, autonomous, and highly-independent services communicating by using lightweight network protocols. To support the designing of microservice architectures, recent studies have proposed either single or multi-objective approaches. In order to improve the effectiveness of existing approaches, we introduced toMicroservices that is a many-objective search-based approach to aid the identification of boundaries among services. In previous studies, we have focused on a qualitative evaluation of the applicability and adoption of the proposed approach from a practical point of view, thus the optimization process itself has not been investigated in depth. In this paper, we extend our previous work by performing a more in-depth analysis of our many-objective approach for microservice identification. We compare our approach against a baseline approach based on a random search using a set of performance indicators widely used in the literature of many-objective optimization. Our results are validated through a real-world case study. The study findings reveal that (i) the criteria optimized by our approach are interdependent and conflicting; and (ii) all candidate solutions lead to better performance indicators in comparison to random search. Overall, the proposed many-objective approach for microservice identification yields promising results, which shed light on insights for further improvements.

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  1. http://jmetal.sourceforge.net/

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Acknowledgements

This study was partially funded by CNPq grants 151723/2020-6, 428994/2018-0, 434969/2018-4, 312149/2016-6, 309844/2018-5, 421306/2018-1, and 408356/2018-9 CAPES grants 175956 and 88887.473590/2020-00 FAPERJ grants 22520-7/2016, 010002285/2019, and PDR-10 Fellowship 202073/2020; FAPPR grants 51152 and 51435.

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Authors and Affiliations

  1. Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Rio de Janeiro, Brazil

    Wesley K. G. Assunção, Luiz Carvalho, Alessandro Garcia, Juliana Alves Pereira & Carlos Lucena

  2. State University of Maringá (UEM), Maringá, Brazil

    Thelma Elita Colanzi

  3. Tecgraf Institute, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil

    Maria Julia de Lima

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  1. Wesley K. G. Assunção
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Correspondence to Wesley K. G. Assunção.

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Communicated by: Aldeida Aleti, Annibale Panichella, and Shin Yoo

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Assunção, W.K.G., Colanzi, T.E., Carvalho, L. et al. Analysis of a many-objective optimization approach for identifying microservices from legacy systems. Empir Software Eng 27, 51 (2022). https://doi.org/10.1007/s10664-021-10049-7

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