Title :
Demand forecast and performance prediction in peer-assisted on-demand streaming systems
Author :
Niu, Di ; Liu, Zimu ; Li, Baochun ; Zhao, Shuqiao
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Toronto, Toronto, ON, Canada
Abstract :
Peer-assisted on-demand video streaming services are extremely large-scale distributed systems on the Internet. Automated demand forecast and performance prediction, if implemented, can help with capacity planning and quality control so that sufficient server bandwidth can always be supplied to each video channel without incurring wastage. In this paper, we use time-series analysis techniques to automatically predict the online population, the peer upload and the server bandwidth demand in each video channel, based on the learning of both human factors and system dynamics from online measurements. The proposed mechanisms are evaluated on a large dataset collected from a commercial Internet video-on-demand system.
Keywords :
Internet; peer-to-peer computing; quality control; time series; video on demand; video streaming; Internet video-on-demand system; automated demand forecasting; capacity planning; distributed system; online measurement; online population prediction; peer-assisted on-demand video streaming service; performance prediction; quality control; server bandwidth; time-series analysis technique; video channel; Bandwidth; Channel estimation; Internet; Peer to peer computing; Predictive models; Servers; Streaming media;
Conference_Titel :
INFOCOM, 2011 Proceedings IEEE
Conference_Location :
Shanghai
Print_ISBN :
978-1-4244-9919-9
DOI :
10.1109/INFCOM.2011.5935196
