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Measurement and Analysis of an Internet Streaming Service to Mobile Devices

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6 Author(s)
Yao Liu ; Dept. of Comput. Sci., George Mason Univ., Fairfax, VA, USA ; Fei Li ; Lei Guo ; Bo Shen
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Receiving Internet streaming services on various mobile devices is getting increasingly popular, and cloud platforms have also been gradually employed for delivering streaming services to mobile devices. While a number of studies have been conducted at the client side to understand and characterize Internet mobile streaming delivery, little is known about the server side, particularly for the recent cloud-based Internet mobile streaming delivery. In this work, we aim to investigate the Internet mobile streaming service at the server side. For this purpose, we have collected a 4-month server-side log on the cloud (with 1,002 TB delivered video traffic) from a top Internet mobile streaming service provider serving worldwide mobile users. Through trace analysis, we find that 1) a major challenge for providing Internet mobile streaming services is rooted from the mobile device hardware and software heterogeneity. In this workload, we find over 3,400 different hardware models with more than 100 different screen resolutions running 14 different mobile OS and three audio codecs and four video codecs. 2) To deal with the device heterogeneity, CPU-intensive transcoding is used on the cloud to customize the video to the appropriate versions at runtime for different devices. A video clip could be transcoded into more than 40 different versions to serve requests from different devices. 3) Compared to videos in traditional Internet streaming, mobile streaming videos are typically of much smaller size (a median of 1.68 MBytes) and shorter duration (a median of 2.7 minutes). Furthermore, the daily mobile user accesses are more skewed following a Zipf-like distribution but users' interests also quickly shift. Considering the huge demand of CPU cycles for online transcoding, we further examine server-side caching to reduce the total CPU cycle demand from the cloud. We show that a policy considering different versions of a video altogether outperforms other intuitive ones when the - ache size is limited.

Published in:

Parallel and Distributed Systems, IEEE Transactions on  (Volume:24 ,  Issue: 11 )

Date of Publication:

Nov. 2013

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