The Assured Forwarding (AF) service of the IETF DiffServ architecture provides a qualitative service differentiation between classes of traffic, in the sense that a low-priority class experiences higher loss rates and higher delays than a high-priority class. However, the AF service does not quantify the difference in the service given to classes. In an effort to strengthen the service guarantees of the AF service, we propose a Quantitative Assured Forwarding service with absolute and proportional differentiation of loss, service rates, and packet delays. We present a feedback-based algorithm which enforces the desired class-level differentiation on a per-hop basis, without the need for admission control or signaling. Measurement results from a testbed of FreeBSD PC-routers on a 100 Mbps Ethernet network show the effectiveness of the proposed service, and indicate that our implementation is suitable for networks with high data rates.

A novel algorithm for buffer management and packet scheduling is presented for providing loss and delay differentiation for traffic classes at a network router. The algorithm, called JoBS (Joint Buffer Management and Scheduling) , provides delay and loss differentiation independently at each node, without assuming admission control or policing. The novel capabilities of the proposed algorithm are that (1) scheduling and buffer management decisions are performed in a single step, and (2) both relative and (whenever possible) absolute QoS requirements of classes are supported. Numerical simulation examples, including results for a heuristic approximation, are presented to illustrate the effectiveness of the approach and to compare the new algorithm to existing methods for loss and delay differentiation.

A novel algorithm for buffer management and rate allocation is presented for providing loss and delay differentiation for traffic classes at a network router. The algorithm, called JoBS, provides delay and loss differentiation independently at each node, without assuming admission control or policing. Contrary to most existing algorithms, scheduling and buffer management decisions are performed in a single step. Both relative

The relative differentiation architecture does not require per-flow state at the network core or edges, nor admission control, but it can only provide higher classes with better service than lower classes. A central premise in the relative differentiation architecture is that users with an absolute QoS requirement can dynamically search for a class which provides the desired QoS level. In the first part of this paper, we investigate this Dynamic Class Selection (DCS) framework in the context of Proportional Delay Differentiation (PDD). We illustrate that, under certain conditions, DCS-capable users can meet absolute QoS requirements, even though the network only offers relative differentiation. For a simple link model, we give an algorithm that checks whether it is feasible to satisfy all users, and if this is the case, computes the minimum acceptable class selection for each user. Users converge in a distributed manner to this minimum acceptable class, if the DCS equilibrium is unique. However, suboptimal and even unacceptable DCS equilibria may also exist. Simulations of an end-to-end DCS algorithm provide further insight in the dynamic behavior of DCS, show the relation between DCS and the network Delay Differentiation Parameters, and demonstrate how to control the trade-off between a flow's performance and cost using DCS.

In this dissertation, we present a novel service architecture for the Internet, which reconciles application demand for strong service guarantees with the need for low computational overhead in network routers. The main contribution of this dissertation is the definition and realization of a new service, called Quantitative Assured Forwarding, which can offer absolute and relative differentiation of loss, service rates, and packet delays to classes of traffic. We devise and analyze mechanisms that implement the proposed service, and demonstrate the effectiveness of the approach through analysis, simulation and measurement experiments in a testbed network. To enable