Resource Pricing for Connection-Oriented Networks

pricing game theory connection oriented networks

Ninan, Bobby M ; Dr. Michael Devetsikiotis, Committee Chair,Dr. Stephen Campbell, Committee Member,Dr. Arne Nilsson, Committee Member,Dr. Yannis Viniotis, Committee Member,Ninan, Bobby M ; Dr. Michael Devetsikiotis ; Committee Chair ; Dr. Stephen Campbell ; Committee Member ; Dr. Arne Nilsson ; Committee Member ; Dr. Yannis Viniotis ; Committee Member

University:North Carolina State University

关键词: pricing game theory connection oriented networks;   

Others  :  https://repository.lib.ncsu.edu/bitstream/handle/1840.16/5163/etd.pdf?sequence=1&isAllowed=y

美国|英语

来源: null

【 摘 要 】

Network pricing has important implications in the revenue generation, resource management, system optimization and congestion control of computer networks. We depart from the prevalent idea of marginal cost pricing and provide a holistic, bi-level optimization framework to model the interaction between network entities in a connection oriented network. Users are treated as utility maximizing entities who allocate the available bandwidth among themselves by playing a distributed, noncooperative rate game. The ensuing Nash equilibrium is analyzed for the single link Erlang network and the multi-link product form networks. Variants based on the upper bound of the blocking are also studied owing to their role in reducing computational complexity. Theoretical results are then validated using numerical simulation for varying network scenarios. An extension of the rate adaptation game based on Recursive Least Squares is proposed for dealing with the imperfect information scenario. These exhibited favorable convergence, accuracy and scalability properties. Gradient-free schemes are then developed for revenue maximization. These are based on novel stochastic approximation techniques such as Finite Difference Stochastic Approximation (FDSA) and Simultaneous Perturbation Stochastic Approximation (SPSA). It is observed that the network employed price discrimination for optimizing its objective function and partitioning its available capacity among competing users.