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Defining and measuring microservice granularity, a literature overview

Vera-Rivera, Fredy H.; Gaona, Carlos; Astudillo, Hernán (PeerJ Computer ScienceEstados Unidos, 2018-09-08)

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 ...
Microservices backlog – a genetic programming technique for identification and evaluation of microservices from user stories.

Vera-Rivera, F. H.; Puerto Cuadros, Eduard Gilberto; Astudillo, Hernán; Gaona, Carlos (IEEE Access, 2017)

The microservice granularity directly affects the quality attributes and usage of computational resources of the system, determining optimal microservice granularity is an open research topic. Microservices granularity is ...
SEMGROMI—a semantic grouping algorithm to identifying microservices using semantic similarity of user stories

Vera-Rivera, F. H.; Puerto Cuadros, Eduard Gilberto; PEREZ GUTIERREZ, BORIS RAINIERO; Astudillo, Hernán; Gaona, Carlos (PeerJ Computer Science, 2023-05-12)

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 ...

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Defining and measuring microservice granularity, a literature overview
...
Vera-Rivera, Fredy H. | 2018-09-08

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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Microservices backlog – a genetic programming technique for identification and evaluation of microservices from user stories.
...
Vera-Rivera, F. H. | 2017

The microservice granularity directly affects the quality attributes and usage of computational resources of the system, determining optimal microservice granularity is an open research topic. Microservices granularity is defined by the number of operations exposed by the microservice, the number of microservices that compose the whole application, and its complexity and dependencies. This paper describes "Microservice Backlog (MB)", a semiautomatic model for defining and evaluating the granularity of microservice-based applications; MB uses genetic programming technique to calculate at design time the granularity of each microservice from the user stories in the "product backlog" or release planning; the genetic algorithm combined coupling, cohesion, granularity, semantic similarity, and complexity metrics to define the number of microservices, and the user stories associated with each microservice. MB decomposes the candidate microservices, allowing to analyze graphically the size of each microservice, as well as its complexity, dependencies, coupling, cohesion metrics, and the number of calls or requests between microservices. The resulting decomposition (number of microservices and their granularity) performed by MB shows less coupling, higher cohesion, less complexity, fewer user stories associated with each microservice, and fewer calls among microservices. MB was validated against three existing methods, using two state-of-the-art applications (Cargo Tracking and JPet-Store), and one real-life applications (Foristom Conferences). The development team and/or architect can use metrics to identify the critical points of the system and determine at design time how the microservice-based application will be implemented.

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SEMGROMI—a semantic grouping algorithm to identifying microservices using semantic similarity of user stories
...
Vera-Rivera, F. H. | 2023-05-12

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 microservicesbased application.

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Listar por autor "Astudillo, Hernán"

Defining and measuring microservice granularity, a literature overview

Microservices backlog – a genetic programming technique for identification and evaluation of microservices from user stories.

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

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Defining and measuring microservice granularity, a literature overview

Microservices backlog – a genetic programming technique for identification and evaluation of microservices from user stories.

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

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Listar por autor "Astudillo, Hernán"

Defining and measuring microservice granularity, a literature overview ﻿

Microservices backlog – a genetic programming technique for identification and evaluation of microservices from user stories. ﻿

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

Envíos recientes

Defining and measuring microservice granularity, a literature overview

Microservices backlog – a genetic programming technique for identification and evaluation of microservices from user stories.

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

Portales Institucionales

Defining and measuring microservice granularity, a literature overview

Microservices backlog – a genetic programming technique for identification and evaluation of microservices from user stories.

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