Partitioning microservices

Joselyne, Munezero Immaculee; Tuheirwe-Mukasa, Doreen; Kanagwa, Benjamin; Balikuddembe, Joseph Kibombo

doi:10.1145/3195528.3195535

Cited by 9 publications

(2 citation statements)

References 11 publications

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“…Other techniques also used to identify microservices are domain engineering and domain-driven design (DDD) ( Josélyne et al, 2018 ; Krause et al, 2020 ), COSMIC function points ( Vural, Koyuncu & Misra, 2018 ), functional decomposition ( Tyszberowicz et al, 2018 ; Baresi, Garriga & De Renzis, 2017 ), class-based extraction model ( Mazlami, Cito & Leitner, 2017 ), data flow-driven decomposition algorithm ( Chen, Li & Li, 2017 ), functional partitioning through heuristics and microservices discovery algorithms ( De Alwis et al, 2018 ), process mining ( Taibi & Syst, 2019 ), and service cutter, a method for decomposing a service ( Gysel et al, 2016 ).…”

Section: Related Workmentioning

confidence: 99%

SEMGROMI—a semantic grouping algorithm to identifying microservices using semantic similarity of user stories

Vera-Rivera

Cuadros

Pérez

et al. 2023

PeerJ Computer Science

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Microservices is an architectural style for service-oriented distributed computing, and is being widely adopted in several domains, including autonomous vehicles, sensor networks, IoT systems, energy systems, telecommunications networks and telemedicine systems. When migrating a monolithic system to a microservices architecture, one of the key design problems is the “microservice granularity definition”, i.e., deciding how many microservices are needed and allocating computations among them. This article describes a semantic grouping algorithm (SEMGROMI), a technique that takes user stories, a well-known functional requirements specification technique, and identifies number and scope of candidate microservices using semantic similarity of the user stories’ textual description, while optimizing for low coupling, high cohesion, and high semantic similarity. Using the technique in four validation projects (two state-of-the-art projects and two industry projects), the proposed technique was compared with domain-driven design (DDD), the most frequent method used to identify microservices, and with a genetic algorithm previously proposed as part of the Microservices Backlog model. We found that SEMGROMI yields decompositions of user stories to microservices with high cohesion (from the semantic point of view) and low coupling, the complexity was reduced, also the communication between microservices and the estimated development time was decreased. Therefore, SEMGROMI is a viable option for the design and evaluation of microservices-based applications. The proposed semantic similarity-based technique (SEMGROMI) is part of the Microservices Backlog model, which allows to evaluate candidate microservices graphically and based on metrics to make design-time decisions about the architecture of the microservices-based application.

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Section: Related Workmentioning

confidence: 99%

SEMGROMI—a semantic grouping algorithm to identifying microservices using semantic similarity of user stories

Vera-Rivera

Cuadros

Pérez

et al. 2023

PeerJ Computer Science

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“…Conference paper P7 Partitioning Microservices: A domain engineering approach (Josélyne et al, 2018 (Ren et al, 2018).…”

Section: Conference Papermentioning

confidence: 99%

Defining and measuring microservice granularity—a literature overview

Vera-Rivera

Gaona

Astudillo

2021

PeerJ Computer Science

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Background Microservices are an architectural approach of growing use, and the optimal granularity of a microservice directly affects the application’s quality attributes and usage of computational resources. Determining microservice granularity is an open research topic. Methodology We conducted a systematic literature review to analyze literature that addresses the definition of microservice granularity. We searched in IEEE Xplore, ACM Digital Library and Scopus. The research questions were: Which approaches have been proposed to define microservice granularity and determine the microservices’ size? Which metrics are used to evaluate microservice granularity? Which quality attributes are addressed when researching microservice granularity? Results We found 326 papers and selected 29 after applying inclusion and exclusion criteria. The quality attributes most often addressed are runtime properties (e.g., scalability and performance), not development properties (e.g., maintainability). Most proposed metrics were about the product, both static (coupling, cohesion, complexity, source code) and runtime (performance, and usage of computational resources), and a few were about the development team and process. The most used techniques for defining microservices granularity were machine learning (clustering), semantic similarity, genetic programming, and domain engineering. Most papers were concerned with migration from monoliths to microservices; and a few addressed green-field development, but none address improvement of granularity in existing microservice-based systems. Conclusions Methodologically speaking, microservice granularity research is at a Wild West stage: no standard definition, no clear development—operation trade-offs, and scarce conceptual reuse (e.g., few methods seem applicable or replicable in projects other than their initial proposal). These gaps in granularity research offer clear options to investigate on continuous improvement of the development and operation of microservice-based systems.

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Domain-Driven Architecture Modeling and Rapid Prototyping with Context Mapper

Kapferer

Zimmermann

2021

Communications in Computer and Information Science

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Partitioning microservices

Cited by 9 publications

References 11 publications

SEMGROMI—a semantic grouping algorithm to identifying microservices using semantic similarity of user stories

SEMGROMI—a semantic grouping algorithm to identifying microservices using semantic similarity of user stories

Defining and measuring microservice granularity—a literature overview

Domain-Driven Architecture Modeling and Rapid Prototyping with Context Mapper

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