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 Cloud Media DJ Platform: Performance Perspective
Tác giả hoặc Nhóm tác giả: Jinwoong Jung, Joohyun Lee, Sanggil Yeoum, Junghyun Bum, Thien Binh Dang, Hyunseung Choo
Nơi đăng: International Conference on Future Data and Security Engineering; Số: LNISA,volume 11251;Từ->đến trang: 335–348;Năm: 2018
Lĩnh vực: Công nghệ thông tin; Loại: Báo cáo; Thể loại: Quốc tế
TÓM TẮT
With the development of mobile devices and the cloud, many kinds of offline services that people have been handling is replaced by online ones. In the past, Korean music cafe DJ performed the role of playing music requested and reciting the story of customers. We replace this offline service with an online one consisting of a public cloud, a streaming server, a streaming client, and a mobile device. The user requests music, video, and messages using the mobile, and receives services through a public screen and speakers in the space where the user is located. We also design and implement Cloud Media DJ platform. This paper focuses on the performance of this platform. We use Docker container distribution technology to accommodate large-scale traffic. In experimental evaluation results, the proposed platform reduces the response time from 25% (minimum) to 75% (maximum) according to the number of concurrent users compared to the existing one.
ABSTRACT
With the development of mobile devices and the cloud, many kinds of offline services that people have been handling is replaced by online ones. In the past, Korean music cafe DJ performed the role of playing music requested and reciting the story of customers. We replace this offline service with an online one consisting of a public cloud, a streaming server, a streaming client, and a mobile device. The user requests music, video, and messages using the mobile, and receives services through a public screen and speakers in the space where the user is located. We also design and implement Cloud Media DJ platform. This paper focuses on the performance of this platform. We use Docker container distribution technology to accommodate large-scale traffic. In experimental evaluation results, the proposed platform reduces the response time from 25% (minimum) to 75% (maximum) according to the number of concurrent users compared to the existing one.
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