Microservice combination optimisation based on improved gray wolf algorithm
Microservices architecture is a new paradigm for application development. The problem of optimising the performance of microservice architectures from a non-functional perspective is a typical Nondeterministic Polynomial (NP) problem. Therefore, aiming to quantify the non-functional requirements of...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2023-12-01
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| Series: | Connection Science |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/09540091.2023.2175791 |
| _version_ | 1797684006224920576 |
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| author | Jingjing Hu Xiaojun Xu Jin Hao Xiuqi Yang Kefan Qiu Yuanzhang Li |
| author_facet | Jingjing Hu Xiaojun Xu Jin Hao Xiuqi Yang Kefan Qiu Yuanzhang Li |
| author_sort | Jingjing Hu |
| collection | DOAJ |
| description | Microservices architecture is a new paradigm for application development. The problem of optimising the performance of microservice architectures from a non-functional perspective is a typical Nondeterministic Polynomial (NP) problem. Therefore, aiming to quantify the non-functional requirements of computing microservice systems, while solving the problem of latency in computing the best combination of services with the maximum QoS objective function value, this paper proposes a microservice combination approach based on the QoS model and a CGWO algorithm for optimisation computation for this model. The experimental results verify that the error rate of the method is only 0.528% on the non-functional combination optimisation problem, and the computational efficiency of the algorithm increases by 97.29% when the complexity of the problem search space increases, while CGWO improves 65.97% and 81.25% respectively in the accuracy of optimisation compared to the prototype of the algorithm (GWO), and has a stable optimisation performance, aspect. It proves that the research in this paper has a high advantage in automatically searching for the best QoS for the microservice combination problem. |
| first_indexed | 2024-03-12T00:24:09Z |
| format | Article |
| id | doaj.art-7b90befb9ae44f77a43d01aeeb4777da |
| institution | Directory Open Access Journal |
| issn | 0954-0091 1360-0494 |
| language | English |
| last_indexed | 2024-03-12T00:24:09Z |
| publishDate | 2023-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Connection Science |
| spelling | doaj.art-7b90befb9ae44f77a43d01aeeb4777da2023-09-15T10:48:01ZengTaylor & Francis GroupConnection Science0954-00911360-04942023-12-0135110.1080/09540091.2023.21757912175791Microservice combination optimisation based on improved gray wolf algorithmJingjing Hu0Xiaojun Xu1Jin Hao2Xiuqi Yang3Kefan Qiu4Yuanzhang Li5Beijing Institute of TechnologyBeijing Institute of TechnologyBeijing Institute of TechnologyBeijing Institute of TechnologyBeijing Institute of TechnologyBeijing Institute of TechnologyMicroservices architecture is a new paradigm for application development. The problem of optimising the performance of microservice architectures from a non-functional perspective is a typical Nondeterministic Polynomial (NP) problem. Therefore, aiming to quantify the non-functional requirements of computing microservice systems, while solving the problem of latency in computing the best combination of services with the maximum QoS objective function value, this paper proposes a microservice combination approach based on the QoS model and a CGWO algorithm for optimisation computation for this model. The experimental results verify that the error rate of the method is only 0.528% on the non-functional combination optimisation problem, and the computational efficiency of the algorithm increases by 97.29% when the complexity of the problem search space increases, while CGWO improves 65.97% and 81.25% respectively in the accuracy of optimisation compared to the prototype of the algorithm (GWO), and has a stable optimisation performance, aspect. It proves that the research in this paper has a high advantage in automatically searching for the best QoS for the microservice combination problem.http://dx.doi.org/10.1080/09540091.2023.2175791quality of servicegrey wolf optimizermicroservice combination optimization |
| spellingShingle | Jingjing Hu Xiaojun Xu Jin Hao Xiuqi Yang Kefan Qiu Yuanzhang Li Microservice combination optimisation based on improved gray wolf algorithm Connection Science quality of service grey wolf optimizer microservice combination optimization |
| title | Microservice combination optimisation based on improved gray wolf algorithm |
| title_full | Microservice combination optimisation based on improved gray wolf algorithm |
| title_fullStr | Microservice combination optimisation based on improved gray wolf algorithm |
| title_full_unstemmed | Microservice combination optimisation based on improved gray wolf algorithm |
| title_short | Microservice combination optimisation based on improved gray wolf algorithm |
| title_sort | microservice combination optimisation based on improved gray wolf algorithm |
| topic | quality of service grey wolf optimizer microservice combination optimization |
| url | http://dx.doi.org/10.1080/09540091.2023.2175791 |
| work_keys_str_mv | AT jingjinghu microservicecombinationoptimisationbasedonimprovedgraywolfalgorithm AT xiaojunxu microservicecombinationoptimisationbasedonimprovedgraywolfalgorithm AT jinhao microservicecombinationoptimisationbasedonimprovedgraywolfalgorithm AT xiuqiyang microservicecombinationoptimisationbasedonimprovedgraywolfalgorithm AT kefanqiu microservicecombinationoptimisationbasedonimprovedgraywolfalgorithm AT yuanzhangli microservicecombinationoptimisationbasedonimprovedgraywolfalgorithm |