Case Study on Migration of Computer Experiment Teaching Environment to Amazon Cloud Platform
- DOI
- 10.2991/978-2-38476-297-2_103How to use a DOI?
- Keywords
- Cloud computing; Experimental teaching; Experimental environment; Big data storage and management
- Abstract
The concept of “Emerging Engineering Education” and “Outcome-Based Education” have put forward new requirements for the experimental teaching environment of computer discipline. Cloud computing boasts abundant resources, high elasticity, easy operation and maintenance, as well as safety and reliability. It effectively addresses the limitations of traditional on-premise labs, such as limited resources, low utilization rates, poor flexibility, and high maintenance costs. Taking the course “Comprehensive Experiment on Big Data Storage and Management” as an example, this paper introduces the practical process of migrating the experimental environment of the course from the traditional computer labs to the Amazon cloud platform, summarizes the advantages and innovations of the migration case, and provides a valuable reference and guidance for the practice of computer experimental courses on the cloud.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Wenhan Zhan AU - Yazhu Xu AU - Wenyu Chen AU - Hualong Huang PY - 2024 DA - 2024/10/31 TI - Case Study on Migration of Computer Experiment Teaching Environment to Amazon Cloud Platform BT - Proceedings of the 2024 8th International Seminar on Education, Management and Social Sciences (ISEMSS 2024) PB - Atlantis Press SP - 847 EP - 858 SN - 2352-5398 UR - https://doi.org/10.2991/978-2-38476-297-2_103 DO - 10.2991/978-2-38476-297-2_103 ID - Zhan2024 ER -