Local Ranking

  • Dmitry Korzun
  • Andrei Gurtov
Chapter
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Abstract

Intrinsically, P2P networks are anonymous, dynamic, and autonomous. Participants have natural disincentives to cooperate because cooperation consumes their own resources and may degrade their own performance. Consequently, each node selfishly attempts to maximize own utility lowering the overall utility of the system. Avoiding this “tragedy of the commons” requires incentives for cooperation. This chapter considers design principles for providing incentives for cooperation in resource sharing among nodes. Fair resource sharing needs differentiation based on the contribution/consumption each node has in the system. We state the problem of local ranking in a P2P resource sharing system with multiple resources. It provides rank-based differentiation of competing nodes and their resources. Introducing ranks rewards cooperating nodes and punishes defect behavior. In this chapter, we focus on ranking models with local knowledge only; the class of models with global system knowledge is discussed later in Chap. 10.

Keywords

Bandwidth Allocation Exchange Balance Upload Bandwidth Local Ranking Download Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Dmitry Korzun
    • 1
    • 2
  • Andrei Gurtov
    • 3
  1. 1.Helsinki Institute for Information TechnologyAalto UniversityAaltoFinland
  2. 2.Department of Computer SciencePetrozavodsk State UniversityPetrozavodskRussia
  3. 3.Centre for Wireless CommunicationsUniversity of OuluOuluFinland

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