NDLI: Demand forecast and performance prediction in peer-assisted on-demand streaming systems

Content Provider	IEEE Xplore Digital Library
Author	Di Niu Zimu Liu Baochun Li Shuqiao Zhao
Copyright Year	2011
Description	Author affiliation: Multimedia Development Group, UUSee, Inc., Canada (Shuqiao Zhao) \|\| Department of Electrical and Computer Engineering, University of Toronto, Canada (Di Niu; Zimu Liu; Baochun Li)
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.
Starting Page	421
Ending Page	425
File Size	745970
Page Count	5
File Format	PDF
ISBN	9781424499199
ISSN	0743166X
e-ISBN	9781424499212
DOI	10.1109/INFCOM.2011.5935196
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-04-10
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Servers Bandwidth Streaming media Predictive models Internet Peer to peer computing Channel estimation
Content Type	Text
Resource Type	Article
Subject	Computer Science Electrical and Electronic Engineering

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