Differentiated services can ensure that traffic on some codepoints receives higher quality of service (QoS) than traffic on other codepoints, but without additional mechanisms it cannot target any particular QoS. Congestion-based pricing has been suggested as a method to target QoS in other network architectures. Here, we investigate whether congestion-based pricing can be used to control aggregate traffic into each codepoint by motivating users to choose the codepoints appropriate for each application. We focus on what information needs to be exchanged in a distributed fashion and assert that both price and QoS information must be available for users to make decisions.

We consider pricing of network resources in a reservation-based quality-of-service architecture. The pricing policy implements a distributed resource allocation to provide guaranteed bounds on packet loss and end-to-end delay for real-time applications. Distributed pricing roles are assigned to each user, each network node, and an arbitrager in between the user and the network. When delay constraints are not binding, we investigate two dynamic pricing algorithms using gradient projection and Newton's method to update prices, and prove their convergence. We analyze the performance of the dynamic pricing policies and show that the gradient algorithm using Newton's method converges more quickly and displays only a few small fluctuations. When delay constraints are binding, we investigate subgradient methods which can provide convergence to some range of the optimal allocation.

Differentiated services and integrated services architectures rely on mechanisms to dynamically allocate network resources. Here, we investigate the feasibility of dynamically adjusting prices based on congestion. The pricing policy implements a distributed resource allocation to provide guaranteed bounds on packet loss and end-to-end delay for real-time applications. Distributed pricing roles are assigned to each user, each network node, and an arbitrager in between the user and the network. We find that such dynamic pricing can be accomplished using simple gradient algorithms, but that this approach requires a small amount of steady communication about demand and supply along each route.