On the User Behavior Characteristic Based Data Prefetching Scheme for Peer-to-peer Video-on-demand Systems
Random seeking is a common behavior while the users enjoy the video-on-demand service,which changes the playback progress suddenly but requires very fast response delay.In order to fasten the response of random seeking effectively,data prefetching is adopted to get some data for future consumption.However,how to get more useful data in the limited storage space is a tough issue of prefetching.In this paper,a range constraint,popularity aware data prefetching scheme(RC-PAP) for peer-to-peer video on demand system is proposed according to the user behavior model of random seeking and the popularity of media data blocks.The simulation results show that RC-PAP can improve the response of random seeking and mitigate stress of the source server significantly compared with current prefetching approaches such as random data prefetching.