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dc.contributor.authorVera-Rivera, Fredy H.
dc.contributor.authorGaona, Carlos
dc.contributor.authorAstudillo, Hernán
dc.date.accessioned2021-11-26T16:10:41Z
dc.date.available2021-11-26T16:10:41Z
dc.date.issued2018-09-08
dc.identifier.urihttp://repositorio.ufps.edu.co/handle/ufps/1452
dc.description.abstractBackground 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.eng
dc.format.extent47 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isoengspa
dc.publisherPeerJ Computer Sciencespa
dc.relation.ispartofPeerJ Computer Science
dc.rightsPeerJ, Inc. 2012-2021 | Public user content licensed CC BY 4.0 unless otherwise specified.eng
dc.sourcehttps://peerj.com/articles/cs-695/#spa
dc.titleDefining and measuring microservice granularity, a literature overvieweng
dc.typeArtículo de revistaspa
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dc.identifier.doihttp://dx.doi.org/10.7717/peerj-cs.695
dc.publisher.placeEstados Unidosspa
dc.relation.citationedition(2021)spa
dc.relation.citationendpage47spa
dc.relation.citationissue(2021)spa
dc.relation.citationstartpage1spa
dc.relation.citesVera-Rivera, F. H., Gaona, C., & Astudillo, H. (2021). Defining and measuring microservice granularity—a literature overview. PeerJ Computer Science, 7, e695.
dc.relation.ispartofjournalPeerJ Computer Sciencespa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.creativecommonsAtribución 4.0 Internacional (CC BY 4.0)spa
dc.subject.proposalMicro service architectureeng
dc.subject.proposalService computingeng
dc.subject.proposalMicro-service granularityeng
dc.subject.proposalMetricseng
dc.subject.proposalMonolith to microserviceseng
dc.subject.proposalMicroservices decompositioneng
dc.subject.proposalQuality attributteseng
dc.subject.proposalSistematic literature revieweng
dc.type.coarhttp://purl.org/coar/resource_type/c_6501spa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/articlespa
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oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.type.versioninfo:eu-repo/semantics/publishedVersionspa


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