Open Access

Architectural Evolution and Decomposition Strategies: A Comprehensive Analysis of Microservice Migration, Performance Optimization, And Machine Learning-Assisted Service Boundary Detection

PDF

Dr. Alistair Sterling ¹ ,

⁴ Department of Software Systems Engineering, University of Melbourne, Australia

Abstract

The paradigm shift from monolithic software architectures to microservices represents one of the most significant transitions in modern software engineering. This article provides an extensive investigation into the theoretical and practical dimensions of this transition, focusing on the systemic challenges of decomposition, migration, and performance orchestration. By synthesizing foundational principles from early architectural discourse with contemporary advancements in machine learning-assisted service boundary detection, the research delineates a multidimensional framework for modularizing legacy systems. We explore the granular differences between services, microservices, and nanoservices, while critically evaluating the infrastructure cost-efficiencies of serverless versus container-based deployments. Central to this study is the reconciliation of conflicting requirements between scalability and security, which often emerge during the decomposition phase. The methodology examines workload characterization and interface analysis as primary drivers for service identification, further enhanced by automated performance testing and resilience modeling. Results indicate that while microservices offer superior elasticity and independent deployability, the migration process introduces significant overhead in terms of network latency and operational complexity. This comprehensive analysis concludes with a roadmap for evolutionary architectural transformation, emphasizing the role of automated boundary detection in reducing the cognitive load of system architects.

Keywords

Microservices, Monolithic Architecture, Service Boundary Detection

References

Ahmadvand, M., Ibrahim, A.: Requirements reconciliation for scalable and secure microservice (de)composition. In: Proceedings - 2016 IEEE 24th International Requirements Engineering Conference Workshops, REW. pp. 68–73 (2016).

Amundsen, M. et al.: Microservice Architecture. O’Reilly (2016).

Balalaie, A. et al.: Microservices migration patterns. Softw. Pract. Exp. May 2018, 2019–2042 (2018).

Balalaie, Armin; Heydarnoori, Abbas; Jamshidi, Pooyan; Tamburri, Damian; Lynn, Theodore. Microservices migration patterns. 2018.

Baresi, L. et al.: Microservices Identification Through Interface Analysis. In: ESOCC 2017: Service-Oriented and Cloud Computing. pp. 19–33 (2017).

Bass, L. et al.: DevOps: A Software Architect’s Perspective. Addison-Wesley (2015).

de Camargo, A., Salvadori, I., Mello, R.d.S., Siqueira, F. An architecture to automate performance tests on microservices, in: International Conference on Information Integration and Web-Based Applications and Services, 2016, pp. 422–429.

Fowler, Martin. Microservices. 2011.

Harms, H., Rogowski, C., Lo Iacono, L. Guidelines for adopting frontend architectures and patterns in microservices-based systems, in: Joint Meeting on Foundations of Software Engineering, in: ESEC/FSE 2017, 2017, pp. 902–907.

K. S. Hebbar, “MACHINE LEARNING-ASSISTED SERVICE BOUNDARY DETECTION FOR MODULARIZING LEGACY SYSTEMS,” International Journal of Applied Engineering & Technology, vol. 04,no.02, pp. 401-414, Sep. 2022, https://romanpub.com/resources/ijaet-v4-2-2022-48.pdf

Heorhiadi, V., Rajagopalan, S., Jamjoom, H., Reiter, M.K., Sekar, V. Gremlin: Systematic resilience testing of microservices, in: International Conference on Distributed Computing Systems, ICDCS, 2016, pp. 57–66.

Khazaei, H., Barna, C., Beigi-Mohammadi, N., M. Litoiu. Efficiency analysis of provisioning microservices, in: International Conference on Cloud Computing Technology and Science, CloudCom, 2016, pp. 261–268.

Klock, S., Van Der Werf, J.M.E.M., Guelen, J.P., Jansen, S. Workload-based clustering of coherent feature sets in microservice architectures, in: International Conference on Software Architecture, ICSA, 2017, pp. 11–20.

Minor, Cirrus. Services, Microservices, Nanoservices – oh my! 2014.

Newman, Sam. Building Microservices. 1st. O’Reilly Media, Inc., 2015.

Newman, Sam. Monolith to Microservices: Evolutionary Patterns to Transform Your Monolith. 2019.

Salah, T., Zemerly, M.J., Yeun, C.Y., Al-Qutayri, M., Al-Hammadi, Y. Performance comparison between container-based and VM-based services, in: Conference on Innovations in Clouds, Internet and Networks, ICIN, 2017, pp. 185–190.

Ueda, T., Nakaike, T., Ohara, M. Workload characterization for microservices, in: International Symposium on Workload Characterization, IISWC, 2016, pp. 1–10.

Villamizar, M., Garces, O., Castro, H., Verano, M., Salamanca, L., Casallas, R., Gil, S. Evaluating the monolithic and the microservice architecture pattern to deploy web applications in the cloud, in: Computing Colombian Conference, 10CCC, 2015, pp. 583–590.

Villamizar, M., Garces, O., Ochoa, L., Castro, H., Salamanca, L., Verano, M., Casallas, R., Gil, S., Valencia, C., Zambrano, A., Lang, M. Infrastructure cost comparison of running web applications in the cloud using AWS lambda and monolithic and microservice architectures, in: International Symposium on Cluster, Cloud and Grid Computing, CCGrid, 2016, pp. 179–182.

Most read articles by the same author(s)

<< < 6 7 8 9 10 11 12 > >>

International Journal of Modern Computer Science and IT Innovations

Architectural Evolution and Decomposition Strategies: A Comprehensive Analysis of Microservice Migration, Performance Optimization, And Machine Learning-Assisted Service Boundary Detection

Abstract

Keywords

References

Most read articles by the same author(s)

Similar Articles