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Replication for bio-inspired delivery in unstructured peer-to-peer networks

Many of the current bio-inspired delivery networks set their focus on search, e.g., by using artificial ants. If the network size and, therefore, the search space gets too large, the users experience high delays until the requested content can be consumed. In this paper we propose replication strategies to reduce this delay. Typical mechanisms, applied in unstructured P2P networks, such as replication at the target (owner replication) and replication on the travel path of content (path replication) are either inefficient or the user experience suffers because of the long distance between content and requester. Based on an previously introduced self-organizing hormone-based delivery algorithm, we compare seven existing and proposed replication mechanisms.We show by simulation that the exploitation of local knowledge about the desire for the requested content performs best in scale-free and random networks. These results are expected to provide a guide towards designing future self-organizing bio-inspired networks.