Dynamic file-selection policies for bundling in BitTorrent-like systems

Abstract

BitTorrent-like swarming technologies are very effective for popular content, but less so for the `long tail' of files with disparate popularities, which do not have sufficiently many peers to enable efficient collaboration. Performance degradations are especially pronounced in swarms with reduced file availability. Static bundling groups files into a single data content. It requires no modification to the BitTorrent client, and has been shown to improve availability of unpopular files in BitTorrent swarms. However, as peers are forced to download undesired file pieces, download times increase, especially for peers downloading popular files. We propose to use Stochastic Games and Markov Decision Process (MDP) to model and analyze optimal peer strategies, in a selfish and a cooperative setting respectively, for a BitTorrent-like system with multiple files. Each peer wishes to download a subset of the files, and we allow peers to dynamically decide whether to collaborate with peers targeting a different set of files or not, given the current system state. The Stochastic Game and MPD models take into account both piece availability and average download times, and allow us to study if and when downloading unwanted content can be beneficial. We use dynamic programming to solve the two models, contrast the level of collaboration observed in the selfish and the cooperative settings, and propose an enhanced piece selection mechanism for BitTorrent-like systems with dynamic download decision making. We demonstrate the effectiveness of dynamic file piece selection through both simulations and experiments using a modified BitTorrent client.