A logical architecture design method for microservices architectures

Santos, Nuno; Salgado, Carlos E.; Morais, Francisco; Melo, Mônica Nascimento de; Silva, Sara; Martins, Raquel; Pereira, Marco; Rodrigues, Helena; Machado, Ricardo J.; Ferreira, Nuno; Pereira, M.

doi:10.1145/3344948.3344991

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…However, there is no specific pattern to determine the number and size of microservices. [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29]. [36], [37], [38], [39], [40], [41], [42] Performance.…”

Section: Related Workmentioning

confidence: 99%

Microservices Backlog–A Genetic Programming Technique for Identification and Evaluation of Microservices From User Stories

et al. 2021

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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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Section: Related Workmentioning

confidence: 99%

Microservices Backlog–A Genetic Programming Technique for Identification and Evaluation of Microservices From User Stories

et al. 2021

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“…Santos et al [14] proposed a model-based approach for designing a logical view of a microservice architecture (MSA), called 4SRS-MSLA. The approach is based on modeling a business domain in UML use cases, thus deriving a UML component diagram for the domain, and finally grouping the components into microservices.…”

Section: Related Workmentioning

confidence: 99%

Event Oriented vs Object Oriented Analysis for Microservice Architecture: An Exploratory Case Study

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et al. 2021

2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)

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The rapidly developing internet infrastructure together with the advances in software technology has enabled the development of cloud-based modern web applications that are much more responsive, flexible, and reliable compared to traditional monolithic applications. Such modern applications require new software design paradigms and architectures. Microservice-based architecture (MSbA), which aims to create small, isolated, loosely-coupled applications that work in cohesion, becoming widespread as one of these approaches. MSbA allows the developed applications to be deployed and maintained separately, as well as scaled on demand. However, there is no de facto method for the analysis and design of systems for these architectures. In this paper, we compared the usefulness of the object-oriented (OO) and event-oriented (EO) approaches for analyzing and designing MS-based systems. More specifically, we performed an exploratory case study to analyze, design, and implement a software application dealing with the 'application and evaluation process of graduate students at IzTech'. This paper discusses the results of this case study. We observe that the EO approaches have significant advantages with respect to the OO approaches.Keywords-microservices, event-driven process chains, eEPC, event-oriented analysis, cloud 244 2021 47th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)

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“…Journal paper P17 The ENTICE Approach to Decompose Monolithic Services into Microservices.Towards a Methodology to Form Microservices from Monolithic Ones(Kecskemeti, Kertesz & Marosi, 2017). 2017Conference paper P18 Refactoring Orchestrated Web Services into Microservices Using Decomposition Pattern(Tusjunt & Vatanawood, 2018).Conference paperP19 A logical architecture design method for microservices architectures(Santos et al, 2019). Conference paper P20 A New Decomposition Method for Designing Microservices (Al-Debagy & Martinek, 2019).…”

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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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A logical architecture design method for microservices architectures

Cited by 6 publications

References 17 publications

Microservices Backlog–A Genetic Programming Technique for Identification and Evaluation of Microservices From User Stories

Microservices Backlog–A Genetic Programming Technique for Identification and Evaluation of Microservices From User Stories

Event Oriented vs Object Oriented Analysis for Microservice Architecture: An Exploratory Case Study

Defining and measuring microservice granularity—a literature overview

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