C:/Files/Work/Projects/2007/MAMA 2007/PER-v03/MM1nPS.dvi
نویسندگان
چکیده
Recently, new services have emerged which utilize the Internet as a delivery mechanism for multimedia content. With the advent of broadband accesses, more users are willing to download large volume content from servers, such as video files of TV shows. While some popular video services (e.g. YouTube.com) or some broadcasting companies (e.g. ABC.com) use streaming data with Flash technology, some media distributors (e.g. iTunes) offer entire TV shows for download. In this study, we investigate the performance of the German site OnlineTVRecorder.com (OTR), which acts as an online video cassette recorder (VCR) where users can program their favorite shows over a web interface and download the recorded files from a server or its mirrors. These files are offered in different file formats and can consist of several hundred megabytes up to 1GB or more depending on the length of the TV show as well as the encoding format. OTR can, thus, be seen as an example for a server-based content distribution system with large data files. However, as these server farms are often overloaded, new requests are queued when the provided download slots are full. The restriction to a maximum number of simultaneous downloads guarantees a minimal download bandwidth for each user. Additionally, the service offers premium users prioritized access to downloading. The download duration itself depends on the total capacity of the server and the number of users currently sharing this capacity. On the other hand, users who might encounter slow downloads may abort their downloading attempt if their patience is exceeded. In this paper, we discuss analytical modeling approaches which consider the impact of the user’s impatience on the performance of an OTR server with different file size distributions. The paper is organized as follows. After describing the problem and related work, we formulate simple analytical models and compare their performance in terms of download duration and success ratio. Especially, we address the question of how to properly dimension the number of simultaneous downloads at a server in order to optimize the performance of the system and to maximize the user satisfaction.
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