Title page for ETD etd-11302004-125645

Type of Document

Dissertation

Author

Ninan, Bobby M,

Author's Email Address

bmninan@gmail.com

URN

etd-11302004-125645

Title

Resource Pricing for Connection-Oriented Networks

Degree

PhD

Graduate Program

Operations Research

Advisory Committee

Advisor Name	Title
Dr. Michael Devetsikiotis	Committee Chair
Dr. Arne Nilsson	Committee Member
Dr. Stephen Campbell	Committee Member
Dr. Yannis Viniotis	Committee Member

Keywords

pricing game theory connection oriented networks

Date of Defense

2004-11-22

Availability

unrestricted

Abstract

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.

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