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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2018 Vol.8(2): 164-168 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2018.8.2.681

A Private Cloud-Based Video Transcoding Architecture for Project-Based Learning Environment

Mohd Faisal Ibrahim, Rosanita Adnan, Zarina Zainol, and Nur Atiqah Sia Abdullah
Abstract—Project-based learning (PBL) is a teaching method that integrates real-world problems and technology into the curriculum. The use of mobile and video technologies in PBL has made the learning environment much more interactive. However, the gap between the richness of video content and the limited capabilities of mobile devices still remains a challenge. In order to solve this problem, video transcoding can be implemented. However, this solution may provide extreme CPU consumption and processing delay on the server’s side. To overcome this issue, a private cloud-based video transcoding architecture is proposed. In this paper, the proposed solution was designed, implemented and compared with the server-based content switching method, and an experiment was conducted to evaluate the method in large-scale simulated workloads. The results show that the proposed architecture is capable of dealing with many concurrent users’ access and capable of reducing the video downloading time.

Index Terms—Cloud computing, project-based learning, video transcoding.

Mohd Faisal Ibrahim, Rosanita Adnan, Zarina Zainol and Nur Atiqah Sia Abdullah are with the Faculty of Computer and Mathematical Sciences, UiTM, Shah Alam, Selangor, Malaysia (e-mail: faisal@tmsk.uitm.edu.my, rosanita@tmsk.uitm.edu.my, zarina@tmsk.uitm.edu.my, atiqah@tmsk.uitm.edu.my).

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Cite: Mohd Faisal Ibrahim, Rosanita Adnan, Zarina Zainol, and Nur Atiqah Sia Abdullah, "A Private Cloud-Based Video Transcoding Architecture for Project-Based Learning Environment," International Journal of Machine Learning and Computing vol. 8, no. 2, pp. 164-168, 2018.

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