Due to large size and different popularity for different part of the video, most proxy caches for streaming medias cache only a part of the video. Thus, an accurate understanding on the internal popularity distributio...Due to large size and different popularity for different part of the video, most proxy caches for streaming medias cache only a part of the video. Thus, an accurate understanding on the internal popularity distribution of media objects in streaming applications is very important for the development of efficient cache mechanisms. This letter shows that the internal popularity of popular streaming media obeys a k-transformed Zipf-like distribution through analyzing two 6-month long traces recorded at different streaming video servers of an entertainment video-on-demand provider. This empirical model can be used to design an efficient cach- ing algorithm.展开更多
基金Supported by the National Natural Science Foundation of China (No.60302004), the Australian Research Council (Grant LX0240468) and Natural Science Foun-dation of Hubei, China (No.2005ABA264).
文摘Due to large size and different popularity for different part of the video, most proxy caches for streaming medias cache only a part of the video. Thus, an accurate understanding on the internal popularity distribution of media objects in streaming applications is very important for the development of efficient cache mechanisms. This letter shows that the internal popularity of popular streaming media obeys a k-transformed Zipf-like distribution through analyzing two 6-month long traces recorded at different streaming video servers of an entertainment video-on-demand provider. This empirical model can be used to design an efficient cach- ing algorithm.
