Optimization of Hybrid Cloud Data Scheduling in Online Laboratory of Distance Education Based on RCNN
Year: 2025
Type: conference-paper
Abstract: When the online laboratory of distance education is controlled in different places, when a large number of prototypes or demonstration resources appear, the response speed of scheduling methods is usually slow. Therefore, the hybrid cloud data scheduling optimization method of online laboratory of distance education based on RCNN is studied. The resource database of hybrid cloud data center is constructed, and the static cache module is added, so that the contents of the database are preferentially saved and transmitted. Using database connection pool technology and RCNN model, a hybrid cloud data resource matching model is established to realize intelligent matching of hybrid cloud data resources. After the hybrid cloud data resource matching is successful, a particle swarm optimization algorithm is generated to generate a global scheduling scheme that meets the needs of users. The experimental results show that the proposed method can effectively reduce the average response time of scheduling, improve the utilization rate of CPU resources, and reduce energy consumption. This method is superior in the scheduling optimization of hybrid cloud data in the online laboratory of distance education and has practical application value.
Author: Yeqiu Lin
Institution: Hainan Tropical Ocean University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
OpenAlex ID: W4411744615