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Article
Towards an Automatic Identification of Microservices from Business Processes
Proceedings of the Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises, WETICE
  • Mohamed Daoud, Université Claude Bernard Lyon 1
  • Asmae El Mezouari, Caddy Ayyad University
  • Noura Faci, Université Claude Bernard Lyon 1
  • Djamal Benslimane, Université Claude Bernard Lyon 1
  • Zakaria Maamar, Zayed University
  • Aziz El Fazziki, Caddy Ayyad University
Document Type
Conference Proceeding
Publication Date
9-1-2020
Abstract

© 2020 IEEE. Microservices have emerged as an alternative solution to many existing technologies allowing to break monolithic applications into 'small' fine-grained, highly-cohesive, and loosely-coupled units. However, identifying microservices remains a challenge that could undermine this migration success. This paper proposes an approach for microservices automatic-identification from a set of business processes (BP). The approach is multi-models combining different independent models that represent a BP's control dependencies, data dependencies, semantic dependencies, respectively. the approach is also based on collaborative clustering. A case study about renting bikes is adopted to illustrate and demonstrate the approach. In term of precision, the results show how BPs as inputs permit to generate better microservices compared to other approaches discussed in the paper, as well.

ISBN
9781728169750
Publisher
IEEE
Disciplines
Keywords
  • Business Process,
  • Data dependencies,
  • Disco,
  • Microservices,
  • Semantics
Scopus ID
85100730236
Indexed in Scopus
Yes
Open Access
Yes
Open Access Type
Green: A manuscript of this publication is openly available in a repository
https://doi.org/10.1109/WETICE49692.2020.00017
Citation Information
Mohamed Daoud, Asmae El Mezouari, Noura Faci, Djamal Benslimane, et al.. "Towards an Automatic Identification of Microservices from Business Processes" Proceedings of the Workshop on Enabling Technologies: Infrastructure for Collaborative Enterprises, WETICE Vol. 2020-September (2020) p. 42 - 47 ISSN: <a href="https://v2.sherpa.ac.uk/id/publication/issn/1524-4547" target="_blank">1524-4547</a>
Available at: http://works.bepress.com/zakaria-maamar/172/