Benchmarking Micro2Micro transformation: an approach with GNN and VAE
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Micro2Micro (2).pdf
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2025-05-11
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765.6Kb
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PDF
Description:
Final Accepted Manuscript
Affiliation
Systems and Industrial Engineering, University of ArizonaIssue Date
2024-05-11
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Springer Science and Business Media LLCCitation
Chy, M.S.H., Sooksatra, K., Yero, J. et al. Benchmarking Micro2Micro transformation: an approach with GNN and VAE. Cluster Comput (2024). https://doi.org/10.1007/s10586-024-04526-zJournal
Cluster ComputingRights
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.Collection Information
This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at repository@u.library.arizona.edu.Abstract
In the evolving landscape of software architecture, the shift from monolithic structures to agile, scalable microservices has revolutionized cloud-native application development. However, the inherent dynamism of microservices can lead to the inadvertent creation of unnecessary microservices, introducing complexity and inefficiency. Moreover, with a lack of control mechanisms in evolution, systems can lead to what is known as architecture degradation. This research ventures into the emerging domain of microservice-to-microservice transformation, a concept focused on optimizing existing cloud-native systems. We experiment with a machine learning methodology initially designed for monolith-to-microservices migration, adapting it to the complex microservices landscape, with a specific focus on the train-ticket application (Zhou in Association for Computing Machinery, https://doi.org/10.1145/3183440.3194991), which is an established system benchmark in the community. To identify the optimal microservice distribution, we employ a combination of the Variational Autoencoder and fuzzy c-means clustering. Our results demonstrate a close resemblance to the original application in terms of structural modularity. Though they fall short of achieving the ideal interface number exhibited by the original microservices, our findings highlight the potential of automated microservice composition, effectively narrowing the gap between human-designed and machine-generated microservices and advancing the field of software architecture.Note
12 month embargo; first published 11 May 2024ISSN
1386-7857EISSN
1573-7543Version
Final accepted manuscriptae974a485f413a2113503eed53cd6c53
10.1007/s10586-024-04526-z