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Conference Papers Year : 2017

A Mood Value for Fair Resource Allocations

Francesca Fossati
Stefano Secci

Abstract

In networking and computing, resource allocation is typically addressed using classical sharing protocols as, for instance, the proportional division rule, the max-min fair allocation , or other solutions inspired by cooperative game theory. In this paper, we argue that, describing the resource allocation problem as a cooperative game, such classical resource allocation approaches, as well as associated notions of fairness, show important limitations. We identify in the individual satisfaction rate the key aspect of the challenge of defining a new notion of fairness and, consequently, a resource allocation algorithm more appropriate for the cooperative context. We generalize the concept of user satisfaction considering the set of admissible solutions for bankruptcy games. We adapt the Jain's fairness index to include the new user satisfaction rate. Accordingly, we propose a new allocation rule we call 'Mood Value'. For each user it equalizes our novel game-theoretic definition of user satisfaction with respect to a distribution of the resource. We test the mood value and the new fairness index through extensive simulations showing how they better support the fairness analysis.
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Dates and versions

hal-01445492 , version 1 (27-01-2017)
hal-01445492 , version 2 (20-04-2017)

Licence

Attribution - NonCommercial

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Francesca Fossati, Stefano Moretti, Stefano Secci. A Mood Value for Fair Resource Allocations. IFIP Networking 2017, Jun 2017, Stockholm, Sweden. ⟨10.23919/IFIPNetworking.2017.8264839⟩. ⟨hal-01445492v2⟩
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