Investigation on the Content Popularity Distribution under K-Transformation in Streaming Applications

An accurate understanding on the content popularity distribution of media objects in streaming applications is required when designing efficient caching algorithms for the streaming media. However, there is no empirical model to describe this distribution. Our investigation shows that the content popularity of a hot streaming media file exhibits a Zipf-like distribution under K-transformation, after analyzing 250 hot clips in 2 long-term traces. A specific function is proposed to describe this phenomenon. This result builds a foundation for designing intelligent caching algorithms.