<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper proposes the use of the Hadoop-distributed file system HDFS as a storage system for large resources in music education. In order to improve the overall performance of HDFS for storing educational resources and to solve the deficiencies of the current system. This paper provides an overall analysis of the current state of research related to the placement algorithm and data storage strategy of HDFS, respectively. After combining the characteristics of educational resources themselves and establishing an educational resource value evaluation index system, an improved placement algorithm and a hybrid redundant storage strategy based on educational resource ratings are proposed, and the proposed algorithm or strategy is simulated and implemented and verified through source code analysis of HDFS-related algorithms. The results show that the average response time for uploading 10 songs is 7.734 s, and the average response time for image retrieval is 0.768 s. In the analysis of digital music education resources demand, teachers have the highest usage rate of music teaching theory (M=3.43), 38.45% of students’ demand is mainly for downloading music, and music lovers querying popular music has the highest percentage of 19.43%. The algorithm in this paper achieves the storage load balance of each node, which basically satisfies the demand for music education resources of teachers and students in universities.</p>
</abstract>

This paper proposes the use of the Hadoop-distributed file system HDFS as a storage system for large resources in music education. In order to improve the overall performance of HDFS for storing educational resources and to solve the deficiencies of the current system. This paper provides an overall analysis of the current state of research related to the placement algorithm and data storage strategy of HDFS, respectively. After combining the characteristics of educational resources themselves and establishing an educational resource value evaluation index system, an improved placement algorithm and a hybrid redundant storage strategy based on educational resource ratings are proposed, and the proposed algorithm or strategy is simulated and implemented and verified through source code analysis of HDFS-related algorithms. The results show that the average response time for uploading 10 songs is 7.734 s, and the average response time for image retrieval is 0.768 s. In the analysis of digital music education resources demand, teachers have the highest usage rate of music teaching theory (M=3.43), 38.45% of students’ demand is mainly for downloading music, and music lovers querying popular music has the highest percentage of 19.43%. The algorithm in this paper achieves the storage load balance of each node, which basically satisfies the demand for music education resources of teachers and students in universities.

Research on digital reform of music education resources in universities in the context of data governance

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper proposes the use of the Hadoop-distributed file system HDFS as a storage system for large resources in music education. In order to improve the...

Research on digital reform of music education resources in universities in the context of data governance

Publicado en línea: 09 oct 2023

Páginas: -

Recibido: 25 oct 2022

Aceptado: 19 abr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00573

Palabras clave
Hadoop, HDFS, Educational resource rating, Redundant storage strategy, Improved placement algorithm, Digitization of educational resources

© 2023 Lifang Zhang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on digital reform of music education resources in universities in the context of data governance

Publicado en línea: 09 oct 2023

Páginas: -

Recibido: 25 oct 2022

Aceptado: 19 abr 2023

DOI: https://doi.org/10.2478/amns.2023.2.00573

Palabras claveHadoop, HDFS, Educational resource rating, Redundant storage strategy, Improved placement algorithm, Digitization of educational resources

© 2023 Lifang Zhang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
Hadoop, HDFS, Educational resource rating, Redundant storage strategy, Improved placement algorithm, Digitization of educational resources