CC-DRAM: Cloud Computing-based Dynamic Resource Allocation for Online learning Platform

Abstract

A cloud classroom is a new type of online education that has recently evolved within the framework of the Internet and education. Learning in a cloud classroom means students access course materials and goals online, collaborate with instructors and peers, and construct their knowledge base via the Internet. There is an insufficient individualized suggestion module and no way to alleviate information overload, which are features of the conventional cloud classroom model of instruction. Hence, this paper proposed a Cloud Computing-based Dynamic Resource Allocation Model (CC-DRAM) to improve content delivery and increase resource allocation in online learning. Consequently, the CC-DRAM operating under the customized recommendation system is used in this research. The system uses a collaborative filtering recommendation algorithm to enhance cloud work scheduling, learn users’ preferences, and provide better suggestions. It also allows for the integration and integrated management of different resources through technologies like distributed storage, virtualization, and networking. Based on experimental analysis of the CC-DRAM platform, which provides 24/7 access to digital materials for students and educators, we can now create individualized lesson plans that students and instructors may read, download, print, and share. In this proposed method, the scalability of distributed storage, user satisfaction, performance, the effectiveness of collaborative, and resource allocation metrics are analyzed and compared to the existing method; the values are gradually increased by the ratio of 97.8%, 98.2%, 99.34%, 96.12%, 98.41% respectively.