Open AccessBand-Area Resource Management Platform and Accelerated Particle Swarm Optimization Algorithm for Container Deployment in Internet-of-Things Cloud
Author(s) -
Mingxue Ouyang,
Jianqing Xi,
Weihua Bai,
Keqin Li
Publication year - 2022
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2022.3198971
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
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.