Band-Area Application Container and Artificial Fish Swarm Algorithm for Multi-Objective Optimization in Internet-of-Things Cloud

Abstract

Container virtualization methods based on application deployment levels have been widely adopted in cloud-computing environments to implement application construction, deployment, and migration. However, most application containers focus on the interface between the applications and hosts and lack collaboration between application containers. This study proposes a new application container model that contains users, application services, documents, and messages, called Band-area Application Container. A salient feature of the Band-area is that it can express a variety of things in reality, such as organizations or individuals. End users can build a complex and changeable application system through cooperation between the Band-areas. However, the resource allocation of non Internet-of-Thing and Internet-of-Thing tasks from the application container is an open issue. The resource allocation method of tasks should not only improve the quality of the user experience, but also reduce energy consumption by improving the resource utilization of the server. To solve this problem, an artificial fish swarm algorithm is proposed to optimize container-based task scheduling. The algorithm considers not only the reliability, processing time overhead, and energy consumption of the task, but also the resource utilization of the servers. Experimental evaluation shows that, compared with the existing three algorithms, the algorithm obtains a better improvement rate in task processing time overhead, energy consumption, reliability, and cluster load balancing.