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Using Aggregation for Adaptive Super-Peer Discovery on the Gradient Topology

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Self-Managed Networks, Systems, and Services (SelfMan 2006)

Part of the book series: Lecture Notes in Computer Science ((LNCCN,volume 3996))

Abstract

Peer-to-peer environments exhibit a very high diversity in individual peer characteristics ranging by orders of magnitude in terms of uptime, available bandwidth, and storage space. Many systems attempt to exploit this resource heterogeneity by using the best performing and most reliable peers, called super-peers, for hosting system services. However, due to inherent decentralisation, scale, dynamism, and complexity of P2P environments, self-managing super-peer selection is a challenging problem. In this paper, decentralised aggregation techniques are used to reduce the uncertainty about system properties by approximating the peer utility distribution allowing peers to calculate adaptive thresholds in order to discover appropriate super-peers. Furthermore, a heuristic search algorithm is described that allows super-peers, above a certain utility threshold, to be efficiently discovered and utilised by any peer in the system.

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Sacha, J., Dowling, J., Cunningham, R., Meier, R. (2006). Using Aggregation for Adaptive Super-Peer Discovery on the Gradient Topology. In: Keller, A., Martin-Flatin, JP. (eds) Self-Managed Networks, Systems, and Services. SelfMan 2006. Lecture Notes in Computer Science, vol 3996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11767886_6

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  • DOI: https://doi.org/10.1007/11767886_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34739-2

  • Online ISBN: 978-3-540-34740-8

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