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A New Decomposition Method for Designing Microservices.

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Τίτλος: A New Decomposition Method for Designing Microservices.
Συγγραφείς: Al-Debagy, Omar, Martinek, Peter
Πηγή: Periodica Polytechnica: Electrical Engineering & Computer Science; 2019, Vol. 63 Issue 4, p274-281, 8p
Θεματικοί όροι: Decomposition method, Software architecture, Technical specifications, Application program interfaces, Architects, Silhouettes
Περίληψη: Many companies are migrating from monolithic architectures to microservice architectures, and they need to decompose their applications in order to create a microservices application. Therefore, the need comes for an approach that helps software architects in the decomposition process. This paper presents a new approach for decomposing monolithic application to a microservices application through analyzing the application programming interface. Our proposed decomposition methodology uses word embedding models to obtain word representations using operation names, as well as, using a hierarchical clustering algorithm to group similar operation names together in order to get suitable microservices. Also, using grid search method to find the optimal parameter values for Affinity Propagation algorithm, which was used for clustering, as well as using silhouette coefficient score to compare the performance of the clustering parameters. The decomposition approach that was introduced in this research consists of the OpenAPI specifications as an input, then extracts the operation names from the specifications and converts them into average word embedding using fastText model. Lastly the decomposition approach is grouping these operation names using Affinity Propagation algorithm. The proposed methodology presented promising results with a precision of 0.84, recall of 0.78 and F-Measure of 0.81. [ABSTRACT FROM AUTHOR]
Copyright of Periodica Polytechnica: Electrical Engineering & Computer Science is the property of Periodica Polytechnica and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A New Decomposition Method for Designing Microservices.
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  Data: Periodica Polytechnica: Electrical Engineering & Computer Science; 2019, Vol. 63 Issue 4, p274-281, 8p
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  Data: <searchLink fieldCode="DE" term="%22Decomposition+method%22">Decomposition method</searchLink><br /><searchLink fieldCode="DE" term="%22Software+architecture%22">Software architecture</searchLink><br /><searchLink fieldCode="DE" term="%22Technical+specifications%22">Technical specifications</searchLink><br /><searchLink fieldCode="DE" term="%22Application+program+interfaces%22">Application program interfaces</searchLink><br /><searchLink fieldCode="DE" term="%22Architects%22">Architects</searchLink><br /><searchLink fieldCode="DE" term="%22Silhouettes%22">Silhouettes</searchLink>
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  Data: Many companies are migrating from monolithic architectures to microservice architectures, and they need to decompose their applications in order to create a microservices application. Therefore, the need comes for an approach that helps software architects in the decomposition process. This paper presents a new approach for decomposing monolithic application to a microservices application through analyzing the application programming interface. Our proposed decomposition methodology uses word embedding models to obtain word representations using operation names, as well as, using a hierarchical clustering algorithm to group similar operation names together in order to get suitable microservices. Also, using grid search method to find the optimal parameter values for Affinity Propagation algorithm, which was used for clustering, as well as using silhouette coefficient score to compare the performance of the clustering parameters. The decomposition approach that was introduced in this research consists of the OpenAPI specifications as an input, then extracts the operation names from the specifications and converts them into average word embedding using fastText model. Lastly the decomposition approach is grouping these operation names using Affinity Propagation algorithm. The proposed methodology presented promising results with a precision of 0.84, recall of 0.78 and F-Measure of 0.81. [ABSTRACT FROM AUTHOR]
– Name: Abstract
  Label:
  Group: Ab
  Data: <i>Copyright of Periodica Polytechnica: Electrical Engineering & Computer Science is the property of Periodica Polytechnica and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3311/PPee.13925
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        Text: English
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      – SubjectFull: Technical specifications
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              Text: 2019
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