Scenario-Based Microservice Retrieval Using Word2Vec

Ma, Shang‐Pin; Chuang, Yen; Lan, Ci-Wei; Chen, Hsi-Min; Huang, Chun-Ying; Li, Chia‐Yu

doi:10.1109/icebe.2018.00046

Cited by 6 publications

(11 citation statements)

References 10 publications

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Section: Resultsmentioning

confidence: 99%

“…Prachitmutita et al (2018) proposed a new self-scaling framework based on the predicted workload, with an artificial neural network, a recurrent neural network, and a resource scaling optimization algorithm used to create an automated system to manage the entire application with Infrastructure-as-a-service (IaaS) (Prachitmutita et al, 2018). Ma et al (2018) proposed an approach, called scenario-based microservice retrieval (SMSR), to recommend appropriate microservices for users based on the Behavior-driven Development (BDD) test scenarios written by the user. The proposed service retrieval algorithm is based on word2vec, an automatic learning method widely used in natural language processing (NLP) to perform service filtering and calculate service similarity (Ma et al, 2018).…”

Section: Quality Attributes and Artificial Intelligencementioning

confidence: 99%

“…Ma et al (2018) proposed an approach, called scenario-based microservice retrieval (SMSR), to recommend appropriate microservices for users based on the Behavior-driven Development (BDD) test scenarios written by the user. The proposed service retrieval algorithm is based on word2vec, an automatic learning method widely used in natural language processing (NLP) to perform service filtering and calculate service similarity (Ma et al, 2018). Abdullah, Iqbal & Erradi (2019) proposed a complete automated system for breaking down an application into microservices, implementing microservices using appropriate resources, and automatically scaling microservices to maintain the desired response time (Abdullah, Iqbal & Erradi, 2019).…”

Section: Quality Attributes and Artificial Intelligencementioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Quality Attributes and Artificial Intelligencementioning

confidence: 99%

Section: Quality Attributes and Artificial Intelligencementioning

confidence: 99%

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Defining and measuring microservice granularity—a literature overview

Vera-Rivera

Gaona

Astudillo

2021

PeerJ Computer Science

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“…The research on the migration of monolith systems to a microservices architecture that uses automatic and semiautomatic methods already use heuristic, for example, Gyselet al 3 and Jin et al, 6 and machine learning, for example, Ma et al 32 and Abdullah et al, 33 techniques to the identification of candidate decompositions, but there is no work on the automatic identification of code-smells that recommends refactorings to ease monolith functionality migration in the context of a candidate decomposition.…”

Section: Related Workmentioning

confidence: 99%

Identification of monolith functionality refactorings for microservices migration

Correia

Silva

2022

Softw Pract Exp

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In the process of migrating a monolith to a microservice architecture, functionalities which execute on top of a single database as ACID transactions, have their execution split between several databases, one for each microservice.Therefore, the complexity of functionality migration increases, because of the lack of isolation of the functionality execution, which has to be handled by following an eventual consistency model. On the other hand, the object-oriented approach, commonly followed in the development of monolith systems, promotes fine-grained interactions in the functionalities implementation, which further increases the migration effort due to the large number of remote invocations between microservices, because it increases the number of microservices transactions that have to eventually be consistent. The main contribution of this paper is a heuristic method to help the software architect on the identification of functionality refactorings, to reduce the effort required to migrate the functionality to microservices when applying the SAGA pattern in the functionality microservices implementation. The heuristic accuracy and efficiency of the method is evaluated using a dataset extracted from 78 codebases and by comparing the results with expert refactorizations. Additionally, a performance analysis of the heuristic implementation is done.

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“…Effectively identifying microservice boundaries and functional responsibilities within a monolithic software system is a critical task, yet often proves elusive. Various techniques have emerged to assist in this process, analyzing features, dependencies, and execution patterns to potentially carve out well-defined microservices [2], [44], [62], [75], [105]. Despite these advancements, a comprehensive understanding of the strengths, weaknesses, and ongoing challenges of existing decomposition strategies remains elusive.…”

Section: Introductionmentioning

confidence: 99%

Evolution of Microservices Identification in Monolith Decomposition: A Systematic Review

Oumoussa,

Saidi

2024

IEEE Access

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Modernizing monolithic systems through microservices architectures (MSAs) promises significant benefits in terms of scalability, agility, and cloud adoption. However, this transition hinges on accurate and efficient microservices identification, a complex area of research still in its evolution. This systematic literature review delves into this challenge by exploring three critical questions: First, we examine how the field of microservices identification has evolved over time, analyzing publication trends, categorizing existing research, and mapping out different research objectives and methodologies employed. Second, we dive into the current state-of-the-art, showcasing cutting-edge methodologies and tools developed to address microservices identification challenges. We highlight promising approaches while identifying potential limitations. Third, we unveil both existing hurdles and future challenges in this domain, painting a comprehensive picture of the obstacles and opportunities that lie ahead. Our findings illuminate key areas demanding further attention, including the need for more automated and accurate identification tools, standardized evaluation benchmarks, and a deeper understanding of the human factors involved in successful transitions. By addressing these critical gaps, we aim to pave the way for smoother and more effective modernization of monolithic systems through microservices adoption.INDEX TERMS Microservices, microservices architecture, microservices identification, monolith application decomposition, monolith to microservices migration.

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Scenario-Based Microservice Retrieval Using Word2Vec

Cited by 6 publications

References 10 publications

Defining and measuring microservice granularity—a literature overview

Defining and measuring microservice granularity—a literature overview

Identification of monolith functionality refactorings for microservices migration

Evolution of Microservices Identification in Monolith Decomposition: A Systematic Review

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