Dynamic Deployment and Scheduling Strategy for Dual-Service Pooling-Based Hierarchical Cloud Service System in Intelligent Buildings

Due to the excessive concentration of computing resources in the traditional centralized cloud service system, there will be three prominent problems of management confusion, construction cost and network delay. Therefore, we propose to virtualize regional edge computing resources in intelligent buildings as edge service pooling, then presents a hierarchical cloud platform with dual-service pooling structure and a dynamic strategy for the proposed model. The analytic hierarchy process (AHP) based quality of service (QoS) evaluation mechanism and the dynamic normal distribution selection method are adopted for service deployment. And the dynamic inertia particle swarm optimization (DI-PSO) algorithm is employed to realize task scheduling. Furthermore, the cloud platform and existing terminal server group are used to conduct platform structure comparison experiments, and the popular task scheduling algorithms are selected for simulation experiments. Experimental results of platform measurement show that the average service response time of different services can be improved by about 17.3 to 37.4 percent. The average occupancy ratio of computing resources can be reduced by about 5 percent. The simulation results show that the earliest completion time of single task list can be decreased by 11.3 to 20.9 percent, and the makespan of 100 task lists can be improved by 0.3 times.

Automated summary

Due to the concentration of computing resources in centralized cloud service systems, management confusion, construction costs, and network delay have become prominent issues. To address this, a virtualized edge service pooling system is proposed, along with a hierarchical cloud platform and a dynamic strategy. Quality of service evaluation mechanisms and dynamic task scheduling algorithms are adopted, resulting in improved service response times and resource utilization.