Adaptive multi-objective swarm intelligence for containerized microservice deployment
Container-based microservice architecture is essential for modern applications. However, optimizing deployment remains critically challenging due to complex interdependencies among microservices. In this paper, we propose a formalized deployment model by systematically analyzing the interdependencie...
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| Vydáno v: | Future generation computer systems Ročník 174; s. 108012 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier B.V
01.01.2026
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| ISSN: | 0167-739X |
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| Abstract | Container-based microservice architecture is essential for modern applications. However, optimizing deployment remains critically challenging due to complex interdependencies among microservices. In this paper, we propose a formalized deployment model by systematically analyzing the interdependencies within Service Function Chains (SFCs). To achieve this, we design a novel swarm intelligence optimization algorithm, named Multi-objective Sand Cat Swarm Optimization with Hybrid Strategies (MSCSO-HS), for multi-objective optimization in microservice deployment. Our algorithm effectively optimizes inter-microservice communication costs and enhances container aggregation density to improve application reliability and maximize resource utilization. Extensive experiments demonstrate that MASCSO outperforms state-of-the-art algorithms for all optimization metrics. Our model achieves improvements of 23.76% in communication latency, 47.51% in deployment density, 38.70% in failure rate, 58.50% in CPU utilization, and 53.81% in RAM usage. The MASCSO framework not only enhances microservice performance and reliability but also provides a robust solution for resource scheduling in cloud environments for microservice deployment. |
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| AbstractList | Container-based microservice architecture is essential for modern applications. However, optimizing deployment remains critically challenging due to complex interdependencies among microservices. In this paper, we propose a formalized deployment model by systematically analyzing the interdependencies within Service Function Chains (SFCs). To achieve this, we design a novel swarm intelligence optimization algorithm, named Multi-objective Sand Cat Swarm Optimization with Hybrid Strategies (MSCSO-HS), for multi-objective optimization in microservice deployment. Our algorithm effectively optimizes inter-microservice communication costs and enhances container aggregation density to improve application reliability and maximize resource utilization. Extensive experiments demonstrate that MASCSO outperforms state-of-the-art algorithms for all optimization metrics. Our model achieves improvements of 23.76% in communication latency, 47.51% in deployment density, 38.70% in failure rate, 58.50% in CPU utilization, and 53.81% in RAM usage. The MASCSO framework not only enhances microservice performance and reliability but also provides a robust solution for resource scheduling in cloud environments for microservice deployment. |
| ArticleNumber | 108012 |
| Author | Lin, Weiwei Zhou, Teng Li, Keqin Zhu, Jiaxian Bai, Weihua Zhang, Huibing |
| Author_xml | – sequence: 1 givenname: Jiaxian surname: Zhu fullname: Zhu, Jiaxian organization: School of Computer Science, Zhaoqing University, Zhaoqing, China – sequence: 2 givenname: Weihua surname: Bai fullname: Bai, Weihua organization: School of Computer Science, Zhaoqing University, Zhaoqing, China – sequence: 3 givenname: Huibing surname: Zhang fullname: Zhang, Huibing email: zhanghuibing@guet.edu.cn organization: The Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, China – sequence: 4 givenname: Weiwei orcidid: 0000-0001-6876-1795 surname: Lin fullname: Lin, Weiwei organization: School of Computer Science and Engineering, South China University of Technology, Guangzhou, China – sequence: 5 givenname: Teng orcidid: 0000-0003-1920-8891 surname: Zhou fullname: Zhou, Teng email: teng.zhou@hainanu.edu.cn organization: School of Cyberspace Security (School of Cryptology), Hainan University, Haikou, China – sequence: 6 givenname: Keqin orcidid: 0000-0001-5224-4048 surname: Li fullname: Li, Keqin organization: Department of Computer Science, State University of New York, New Paltz, NY, USA |
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| Cites_doi | 10.1186/s13677-022-00304-7 10.1016/j.jss.2023.111910 10.1145/3297280.3297295 10.1007/s40747-020-00180-1 10.1109/TCC.2020.2990982 10.1109/TNSE.2022.3150755 10.1016/j.eswa.2022.117151 10.1109/TEVC.2008.925798 10.1109/JSAC.2019.2895473 10.3390/electronics12122614 10.1007/s11227-023-05714-1 10.1007/s00366-022-01604-x 10.1109/ACCESS.2022.3198971 10.1007/s11047-008-9098-4 10.1109/TEVC.2004.826067 10.1016/j.comcom.2023.02.012 10.1016/j.jksuci.2021.03.002 10.1016/j.future.2022.01.012 10.1002/cpe.7665 10.1109/TEVC.2007.892759 10.1111/exsy.13331 10.1109/4235.996017 10.1016/j.eswa.2015.10.039 10.3390/math13071158 10.1007/s10723-017-9419-x 10.1002/cpe.5536 10.1109/ACCESS.2019.2924414 10.1145/3592598 10.1109/ACCESS.2021.3077550 |
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| Keywords | Microservice deployment Multi-objective optimization Swarm intelligence optimization algorithms Containerized microservice |
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| SubjectTerms | Containerized microservice Microservice deployment Multi-objective optimization Swarm intelligence optimization algorithms |
| Title | Adaptive multi-objective swarm intelligence for containerized microservice deployment |
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