Controlling G-AIMD by Index Policy

Abstract : We consider the Generalized Additive Increase Multiplicative Decrease (G-AIMD) dynamics for resource allocation with alpha fairness utility function. This dynamics has a number of important applications such as internet congestion control, charging electric vehicles, and smart grids. We prove indexability for the special case of MIMD model and provide an efficient scheme to compute the index. The use of index policy allows us to avoid the curse of dimensionality. We also demonstrate through simulations for another special case, AIMD, that the index policy is close to optimal and significantly outperforms a natural heuristic which penalizes the strongest user.
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Communication dans un congrès
CDC 2017 - 56th IEEE Conference on Decision and Control, Dec 2017, Melbourne, Australia. IEEE, pp.1-6
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Soumis le : samedi 25 novembre 2017 - 12:57:38
Dernière modification le : jeudi 11 janvier 2018 - 16:31:57

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Konstantin Avrachenkov, Vivek Borkar, Sarath Pattathil. Controlling G-AIMD by Index Policy. CDC 2017 - 56th IEEE Conference on Decision and Control, Dec 2017, Melbourne, Australia. IEEE, pp.1-6. 〈hal-01648312〉

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