Download time reduction using recent performance-biased peer replacement in stochastic P2P content delivery networks

Peer-to-peer networks are a common methodology used for content delivery and data sharing on the Internet, mobile and wireless networks. The duration of any particular download session is highly dependent on the capacity of the node servers selected as source peers. Recent investigations have shown that specific total download times may deviate significantly from average total download times. Typical algorithms used in today´s Peer-to-Peer (P2P) systems have evolved from simply connecting to a single source peer for the entire download session to an approach where the download content is divided into chunks and a randomly selected source peer is chosen as the source for each chunk. Prior work has demonstrated that it is better to divide the download session into time slices and download as much as possible from a randomly selected source peer within each time interval rather than staying connected to a poorly performing source peer. The algorithm described in this investigation uses time-based source peer switching and maintains a small number of parallel download streams. At the end of each time interval, it does not randomly replace all source peers but keeps those source peers that are performing relatively better and replaces those performing relatively poorly with randomly selected new source partners. In this way, as the download progresses, parallel downloading typically progresses to a set of better and better performing source partners, therefore reducing average download times and reducing overall variance between download times. This approach has been shown in simulations to significantly reduce average download times. These improvements are gained while maintaining or further limiting the variance in performance between download sessions.