storage optimization for a peer-to-peer video-on-demand network
This paper explores requirements for efficient pre-seeding of video-on-demand (VoD) movie data onto numerous customer set-top boxes in a cable ISP environment. The pre-seeded content will then be distributed to other set-top boxes in the same cable community using a peer-to-peer (P2P) network protocol such as BitTorrent. The challenges and solutions required for P2P VoD provided by a fixed provider such as a cable company are fundamentally different from those seen in traditional P2P networks or client-server VoD solutions. Our work pre-positions data into set-top boxes using a mathematical programming algorithm. The objective of the algorithm is to minimize uplink traffic, given a popularity model for various pieces of content and information about storage and bandwidth capacity constraints at the customer nodes. Given the complex non-linear nature of P2P interactions, these mathematical programs are solved using non-linear optimization approaches. Using a BitTorrent-like peer-to-peer data delivery system, we show through extensive simulations that our mathematical model for pre-seeding data based on object popularity and node bandwidth availability leads to noticeably greater reductions in uplink traffic and VoD server load than a weighted-random pre-seeding scheme that only considers object popularity.
peer-to-peer video-on-demand bittorrent nonlinear programming multimedia streaming experimental systems