Band-Area Resource Management Platform and Accelerated Particle Swarm Optimization Algorithm for Container Deployment in Internet-of-Things Cloud
The method of building and deploying applications through the combination of container virtualization technology and a microservices framework has been widely used in Internet-of-Things clouds. However, there are gaps and a lack of coordination mechanisms between the Internet-of-Things and cloud com...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2022-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9857907/ |
| _version_ | 1828259551755894784 |
|---|---|
| author | Mingxue Ouyang Jianqing Xi Weihua Bai Keqin Li |
| author_facet | Mingxue Ouyang Jianqing Xi Weihua Bai Keqin Li |
| author_sort | Mingxue Ouyang |
| collection | DOAJ |
| description | The method of building and deploying applications through the combination of container virtualization technology and a microservices framework has been widely used in Internet-of-Things clouds. However, there are gaps and a lack of coordination mechanisms between the Internet-of-Things and cloud computing. This study constructs a resource management platform, which is based on application container virtualization technology and combined with the microservices framework. The platform provide a support environment for the construction and deployment of Internet-of-Things cloud applications. However, there is no unified specification for the microservices templates. Therefore, a new service model called tool service was designed. The invocation relationship between services is studied, and developers can combine services through the invocation relationship between services to form a service function chain. However, container-based service deployment remains an unresolved issue. The deployment method of a container involves the quality of service of end users and the profit of cloud providers. To balance the profits of both parties, it is necessary to minimize the service response time and improve the resource utilization of the cloud data center. To address this problem, an accelerated particle swarm optimization strategy is proposed to realize service deployment. Through the invocation relationship between services, the execution containers are aggregated, so as to reduce the service transmission overhead and improve resource utilization. Compared with the experimental results of existing deployment strategies, the proposed optimization strategy has significantly improved performance parameters such as service transmission overhead, container aggregation, and resource utilization. |
| first_indexed | 2024-04-13T03:10:44Z |
| format | Article |
| id | doaj.art-6c2ae1f9800d42568fd312aa6e77d84a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-13T03:10:44Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-6c2ae1f9800d42568fd312aa6e77d84a2022-12-22T03:05:05ZengIEEEIEEE Access2169-35362022-01-0110868448686310.1109/ACCESS.2022.31989719857907Band-Area Resource Management Platform and Accelerated Particle Swarm Optimization Algorithm for Container Deployment in Internet-of-Things CloudMingxue Ouyang0https://orcid.org/0000-0002-1205-4730Jianqing Xi1Weihua Bai2https://orcid.org/0000-0001-8333-7415Keqin Li3https://orcid.org/0000-0001-5224-4048School of Software Engineering, South China University of Technology, Guangzhou, ChinaSchool of Software Engineering, South China University of Technology, Guangzhou, ChinaSchool of Computer Science, Zhaoqing University, Zhaoqing, ChinaDepartment of Computer Science, State University of New York, New Paltz, NY, USAThe method of building and deploying applications through the combination of container virtualization technology and a microservices framework has been widely used in Internet-of-Things clouds. However, there are gaps and a lack of coordination mechanisms between the Internet-of-Things and cloud computing. This study constructs a resource management platform, which is based on application container virtualization technology and combined with the microservices framework. The platform provide a support environment for the construction and deployment of Internet-of-Things cloud applications. However, there is no unified specification for the microservices templates. Therefore, a new service model called tool service was designed. The invocation relationship between services is studied, and developers can combine services through the invocation relationship between services to form a service function chain. However, container-based service deployment remains an unresolved issue. The deployment method of a container involves the quality of service of end users and the profit of cloud providers. To balance the profits of both parties, it is necessary to minimize the service response time and improve the resource utilization of the cloud data center. To address this problem, an accelerated particle swarm optimization strategy is proposed to realize service deployment. Through the invocation relationship between services, the execution containers are aggregated, so as to reduce the service transmission overhead and improve resource utilization. Compared with the experimental results of existing deployment strategies, the proposed optimization strategy has significantly improved performance parameters such as service transmission overhead, container aggregation, and resource utilization.https://ieeexplore.ieee.org/document/9857907/Accelerated particle swarm optimizationcloud computingcontainerInternet-of-thingsmicroservicesmulti-objective optimization |
| spellingShingle | Mingxue Ouyang Jianqing Xi Weihua Bai Keqin Li Band-Area Resource Management Platform and Accelerated Particle Swarm Optimization Algorithm for Container Deployment in Internet-of-Things Cloud IEEE Access Accelerated particle swarm optimization cloud computing container Internet-of-things microservices multi-objective optimization |
| title | Band-Area Resource Management Platform and Accelerated Particle Swarm Optimization Algorithm for Container Deployment in Internet-of-Things Cloud |
| title_full | Band-Area Resource Management Platform and Accelerated Particle Swarm Optimization Algorithm for Container Deployment in Internet-of-Things Cloud |
| title_fullStr | Band-Area Resource Management Platform and Accelerated Particle Swarm Optimization Algorithm for Container Deployment in Internet-of-Things Cloud |
| title_full_unstemmed | Band-Area Resource Management Platform and Accelerated Particle Swarm Optimization Algorithm for Container Deployment in Internet-of-Things Cloud |
| title_short | Band-Area Resource Management Platform and Accelerated Particle Swarm Optimization Algorithm for Container Deployment in Internet-of-Things Cloud |
| title_sort | band area resource management platform and accelerated particle swarm optimization algorithm for container deployment in internet of things cloud |
| topic | Accelerated particle swarm optimization cloud computing container Internet-of-things microservices multi-objective optimization |
| url | https://ieeexplore.ieee.org/document/9857907/ |
| work_keys_str_mv | AT mingxueouyang bandarearesourcemanagementplatformandacceleratedparticleswarmoptimizationalgorithmforcontainerdeploymentininternetofthingscloud AT jianqingxi bandarearesourcemanagementplatformandacceleratedparticleswarmoptimizationalgorithmforcontainerdeploymentininternetofthingscloud AT weihuabai bandarearesourcemanagementplatformandacceleratedparticleswarmoptimizationalgorithmforcontainerdeploymentininternetofthingscloud AT keqinli bandarearesourcemanagementplatformandacceleratedparticleswarmoptimizationalgorithmforcontainerdeploymentininternetofthingscloud |