Defining and measuring microservice granularity—a literature overview

Abstract

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.

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

“… Evaluation of a case study: The evaluation was carried out in two academic projects (Cargo Tracking ( Baresi, Garriga & De Renzis, 2017 ) and JPetStore ( Jin et al, 2019 )) and two industry projects (Foristom Conferences and Sinplafut ( Vera-Rivera, Vera-Rivera & Gaona-Cuevas, 2019 )). Compare the results of the algorithm with other methods: We compared the decompositions proposed by our algorithm with those proposed by other state-of-the-art microservices identification methods: domain-driven design (DDD), service cutter ( Gysel et al, 2016 ), Microservices identification trough interface analysis (MITIA) ( Baresi, Garriga & De Renzis, 2017 ), and our own genetic programming technique ( Vera-Rivera, Gaona & Astudillo, 2021 ). We compared these approaches using coupling, cohesion, complexity, granularity, development time and performance metrics.…”

“…Compare the results of the algorithm with other methods: We compared the decompositions proposed by our algorithm with those proposed by other state-of-the-art microservices identification methods: domain-driven design (DDD), service cutter ( Gysel et al, 2016 ), Microservices identification trough interface analysis (MITIA) ( Baresi, Garriga & De Renzis, 2017 ), and our own genetic programming technique ( Vera-Rivera, Gaona & Astudillo, 2021 ). We compared these approaches using coupling, cohesion, complexity, granularity, development time and performance metrics.…”

“…According to Vera-Rivera, Gaona & Astudillo (2021) , the problem of microservice identification has been approached from several perspectives, including clustering in machine learning, domain engineering, genetic programming and semantic similarity as the most studied. The granularity of microservices is evaluated using metrics, in particular, to measure performance and to measure the degree of coupling.…”

“…User stories define microservice operations or services as requirements. Finally, none of the surveyed studies use data from agile practices or agile software development ( Vera-Rivera, Gaona & Astudillo, 2021 ).…”

“…For a more detailed comparative analysis of the related works please see Vera-Rivera, Gaona & Astudillo (2021) .…”