Band-Area Resource Management Platform and Accelerated Particle Swarm Optimization Algorithm for Container Deployment in Internet-of-Things Cloud

被引:5
|
作者
Ouyang, Mingxue [1 ]
Xi, Jianqing [1 ]
Bai, Weihua [2 ]
Li, Keqin [3 ]
机构
[1] South China Univ Technol, Sch Software Engn, Guangzhou 510006, Peoples R China
[2] Zhaoqing Univ, Sch Comp Sci, Zhaoqing 526061, Peoples R China
[3] SUNY Coll New Paltz, Dept Comp Sci, New Paltz, NY 12561 USA
来源
IEEE ACCESS | 2022年 / 10卷
关键词
Containers; Cloud computing; Internet of Things; Microservice architectures; Resource management; Optimization; Virtualization; Particle swarm optimization; Accelerated particle swarm optimization; cloud computing; container; Internet-of-things; microservices; multi-objective optimization; MULTIOBJECTIVE OPTIMIZATION; SERVICE COMPOSITION; COLONY ALGORITHM; MICROSERVICE; ENVIRONMENT;
D O I
10.1109/ACCESS.2022.3198971
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
引用
收藏
页码:86844 / 86863
页数:20
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