Analyzing YouTube Content Demand Patternsand Cacheabilityin a Swedish Municipal Network

University essay from KTH/Skolan för informations- och kommunikationsteknik (ICT)

Abstract:

User Generated Content (UGC) has boosted a high popularity since the birth of a wide range of web services allowing the distribution of such user-produced media content, whose patterns vary from textual information, photo galleries to videos on site. The boom of Internet of Things and the newly released HTML5 accelerate the development of multimedia patterns as well as the technology of distributing it. YouTube, as one of the most popular video sharing site, enjoys the top most numbers of video views and video uploads per day in the world. With the rapid growing of multimedia patterns as well as huge bandwidth demand from subscribers, the sheer volume of the traffic is going to severely strain the network resources.</p><p>Therefore, analyzing media streaming traffic patterns and cacheability in live IP-access networks today leads a hot issue among network operators and content providers. One possible solution could be caching popular contents with a high replay rate in a proxy server on LAN border or in users' terminals.</p><p>Based on the solution, this thesis project focuses on developing a measurement framework to associate network cacheability with video category and video duration under a typical Swedish municipal network. Experiments of focused parameters are performed to investigate potential user behavior rules. From the analysis of the results, Music traffic gets a rather ideal network gain as well as a remarkable terminal gain, indicating that it is more efficient to be stored close to end user. Film&amp;Animation traffic, however, is preferable to be cached in the network due to its high net gain. Besides, it is optimal to cache the video clips with a length between 3 and 5 minutes, especially the Music and Film&amp;Animation traffic. In addition, more than half of the replays occur during 16.00-24.00 and peak hours appear on average from 18.00 to 22.00. Lastly, only around 16% of the videos are global popular and very few heavy users tend to be local popular video viewers, depicting local limits and independent user interests

 

 

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