The analysis of edge computing combined with cloud computing in strategy optimization of music educational resource scheduling

With the rapid development of Internet technology, many platforms provide online music services for users. Faced with a large number of music education resources, users are often difficult to choose. A system architecture based on the combination of edge computing (EC) and cloud computing (CC) is proposed to optimize the scheduling strategy of educational resources. The present work first briefly introduces the combination and advantages of EC and CC technology, and analyzes the demand for educational resource scheduling. The contribution and innovation of the research are to introduce EC into the technical scheme of piano music resource scheduling to improve the network and system architecture of the platform. The application results show that the resource utilization rate of the proposed multi-round iterative resource scheduling algorithm is 29% higher than that of the basic genetic algorithm (GA) and ant colony optimization (ACO), the user waiting time is reduced by 15%, and the system efficiency is improved by 23%. This shows that, compared with the traditional resource scheduling strategy, the proposed resource scheduling strategy can reduce delay, improve system efficiency and resource utilization, and improve service level. The research results contribute to support new teaching forms, improve platform application performance and intelligent level, and further improve user experience.