Internet Video sharing web sites such as YouTube [1] have attracted millions of users in a dazzling speed during the past few years. Massive workload accompanies those web sites along with their business success. In order to understand the nature of such unprecedented massive workload and the impact on online video data center design, we analyze Yahoo! Video, the 2nd largest U.S. video sharing site in this paper. The main contribution of our work is an extensive trace-driven analysis of Yahoo! Video workload dynamics.
We crawled all 16 categories on the Yahoo! Video site for 46 days
(from July 17 to August 31 2007), and the data was collected every
30 minutes. This measurement rate was chosen as a tradeoff between
analysis requirement and resource constraint. Due to the massive
scale of Yahoo! Video site, we limited the data collection to the
first 10 pages of each category. Since each page contains 10 video
objects, each time the measurement collects dynamic workload
information for 1600 video files in total. Throughout the whole
collection period, we recorded 9,986 unique videos and a total of
32,064,496 video views. This can be translated into a daily video
request rate of 697064, and gave approximately 
coverage
on the total Yahoo! Video workload in July 2007, based
on [2].