Decentralized Learning for Pricing a RED Buffer - Archive ouverte HAL
Communication Dans Un Congrès Année : 2007

Decentralized Learning for Pricing a RED Buffer

Résumé

We study a buffer that implements the Random Early Detect/Discard (RED) mechanism to cope with congestion, and offers service differentiation by proposing a finite number of slopes at different prices for the RED probability. As a characteristic, the smaller the slope, the better the resulting QoS. Users are sensitive to their average throughput and to the price they pay. Since the study of the noncooperative game played is rendered difficult by the discrete nature of the strategy sets, and since it is not likely that users have a perfect knowledge of the game but only know their experienced utility, we introduce a decentralized learning algorithm to progressively reach a Nash equilibrium over time. We examine the effect of prices on the final game outcomes.

Mots clés

RED
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Dates et versions

hal-02165254 , version 1 (25-06-2019)

Identifiants

  • HAL Id : hal-02165254 , version 1

Citer

Patrick Maillé, Bruno Tuffin, Yiping Xing, Rajarathnam Chandramouli. Decentralized Learning for Pricing a RED Buffer. ICCCN'07 - 16th International Conference on Computer Communications and NetworkS, Aug 2007, Hawaii, United States. pp.346 - 351. ⟨hal-02165254⟩
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