John Musacchio’s PhD Dissertation

You may download my dissertation as a single file, or in parts:

• Title, abstract, table of contents, acknowledgements...
• Part 1
• Part 2
• Appendix & Bibliography

The dissertation is divided into two parts:

PART 1:

In the first part of this dissertation, we study the economic interests of a wireless access point owner and his paying client, and model their interaction as a dynamic game. The key feature of this game is that the players have asymmetric information – the client knows more than the access provider. We find that if a client has a ``web browser'' utility function (a temporal utility function that grows linearly), it is a Nash equilibrium for the provider to charge the client a constant price per unit time. On the other hand, if the client has a ``file transferor'' utility function (a utility function that is a step function), the client would be unwilling to pay until the final time slot of the file transfer. We also study an expanded game where an access point sells to a reseller, which in turn sells to a mobile client and show that if the client has a web browser utility function, that constant price is a Nash equilibrium of the three player game. Finally, we study a two player game in which the access point does not know whether he faces a web browser or file transferor type client, and show conditions for which it is not a Nash equilibrium for the access point to maintain a constant price.

PART 2:

In the second part, we consider a simple flow control scheme to achieve fair flow rates in a stochastic queuing network. The queuing network consists of interconnected single-server stations equipped with per-flow queues that are served according to a weighted round-robin or similar discipline. The flow control, or ingress policing scheme as we call it, works as follows: Whenever any of a flow's queues exceeds a policing threshold, the network discards that flow's arriving packets at the network ingress, and does so until all of that flow's queues fall below their thresholds. To prove our results, we consider the fluid limit of a sequence of queuing networks with increasing thresholds. Using a Lyapunov function derived from the fluid limits, we find that as the policing thresholds are increased, the state of the stochastic system is attracted to a smaller and smaller neighborhood surrounding the equilibrium of the network’s fluid model. Furthermore, we show how this property implies that the achieved flow rates approach the max-min rates predicted by the fluid model.