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Just a brief announcement: the papers presented at the IMC 07 are available on the web. There are many interesting publications and it’s worth to look at some of the papers.
Two papers are covering YouTube conntent [1] and traffic [2]. The first one received the best paper award. The paper by Cha et al. [1] is devoted to the analysis of user generated content offered at YouTube. Content production patterns, user participation and the way of how web surfer’s find content are examined. It was interesting to me that the authors also analysed content aliasing, i.e. multiple copy of the same video are present. They stated that “Most videos have 1 to 4 aliases, while the maximum number of aliases is 89 (…) A large number of aliases are uploaded on the same day as the original video or within a week.” (cf. Section 6.1). Moreover, they showed that simple caching of the most popular videos can offload server traffic by as much as 50%.
In contrast, Gill et al. [2] characterise YouTube traffic measured at the edge (university network) during 85 consecutive days . YouTube traffic was responsible for 4.6 % of the total traffic on the campus Internet link (625,593 videos viewed). The authors also highlight that local caches (in-network) could shrink the traffic, as 50% of the video requests relate to previously requested videos. They state that caching could reduce YouTube traffic in the campus Internet link by a factor of 2, translating to 3.19 TB. However, it was quite interesting to see that although YouTube imposes a limit on the maximum video file size of 100 MB, 0.1 % of the analysed video were larger than that limit. Only 10 % of the analysed videos were larger than 21.9 MB. The file size should reflect the short duration of most videos: “the mean video duration observed on campus is 4.15 minutes with a median of 3.33 minutes (…) 52.3 % of the videos in the all time popular category are between 3 and 5 minutes long.”. They also evaluated the encoding bit-rate of the served videos, suggesting that the target audience are broadband users, the age and rating of the videos. Social networks were also subject to [3].
Dischinger et al. [4] presented a nice analysis of residual broadband access networks (focusing on cable and DSL links) by sending ICMP ping probes and TCP reset packets to sinks. The main research questions were: “1. what are the typical bandwidth, latency and loss characteristics of residual broadband links? 2.) how do the characteristics of broadband networks differ from those of academic or corporate networks and 3.) what are the implications of broadband-network properties for future protocol and system designers?” Some of the findings were that “many cable links show high variation in link bandwidths over shot timescales. Packet transmissions over cable suffer [from?] high jitter as a result of cable’s time-slotted access policy. DSL links show large last-hop delays and considerable deployment of active queue management policies such as random early detection (RED).”
All in all, there are many highly interesting papers and I suggest to take a look at them.
References:
[1] Cha et al.: “I Tube, You Tube, Everybody Tubes: Analyzing the World’s Largest User Generated Content Video System” (2007)
[2] Gill et al.: “YouTube Traffic Characterization: A View From the Edge” (2007)
[3] Mislove et al.: “Measurement and Analysis of Online Social Networks” (2007)
[4] Dischinger et al.: “Characterizing Residential Broadband Networks” (2007)
