Integrating Piece and Peer Selection in Content Distribution Networks

Abstract

Content Distribution Networks (CDNs) could benefit from peer-to-peer (P2P) network techniques to improve content delivery time by leveraging the upload capacity of the entire network. In most available solutions, peers first select a set of partners, and later select the pieces of content to exchange. It is fundamental instead that both peer and piece selection algorithms are performed in a consistent and integrated way. To this aim, we propose a content distribution protocol that unifies piece and peer selection in a single algorithm which optimizes the swarming effects. Our approach leverages Flajolet-Martin sketches, a technique based on Bloom filters, to estimate the number of distinct pieces in the CDN, and then adopts a fractional knapsack problem approach to effectively utilize the entire upload capacity of each peer. We tested our solution with both simulations and Planetlab experiments, showing how the piece estimation at every peer is a good approximation of the global rarest piece across the network, and not just across the first hop neighborhood. Moreover, we show how our solution improves the average downloading time by up to 20%. If we consider only the fastest 50% of peers, the downloading time is improved by 100%. Furthermore, our solution decreases the average first content uploading time by 80% with respect to standard P2P protocols which use a local rarest first piece selection, and tit-for-tat as peer selection algorithm.