Simon C. Borst

Virtual partitioning for robust resource sharing: computational techniques for heterogeneous traffic

We consider virtual partitioning (VP), which is a scheme for sharing a resource among several traffic classes in an efficient, fair, and robust manner. In the preliminary design stage, each traffic class is allocated a nominal capacity, which is based on expected offered traffic and required quality of service. During operations, if the current capacity usage by a class exceeds its nominal allocation, then it is declared to be in overload, and a state-dependent trunk reservation mechanism gives it lower priority in the admission of new calls. We develop efficient computational algorithms for the case of heterogeneous traffic, and perform extensive numerical experiments to demonstrate the accuracy of the various approximations. The performance of VP is examined and compared to that of complete sharing and complete partitioning. Particular weight is placed on robustness, meaning that traffic classes with arrival rates conforming to the design continue to receive the required quality of service, despite the presence of misbehaving classes with excessive arrival rates. We adopt a reward-penalty paradigm as a combined measure for efficiency and fairness, and show that not only is the revenue generated by VP extremely close to the maximum achievable value, but that the structural form of the optimal policy also closely resembles that of VP. The numerical results confirm that the scheme is efficient, fair, and very robust.

Dynamic optimization in future cellular networks

With multiple air-interface support capabilities and higher cell densities, future cellular networks will offer a diverse spectrum of user services. The resulting dynamics in traffic load and resource demand will challenge present control loop algorithms. In addition, frequent upgrades in the network infrastructure will substantially increase the network operation costs if done using current optimization methodology. This motivates the development of dynamic control algorithms that can automatically adjust the network to changes in both traffic and network conditions and autonomously adapt when new cells are added to the system. Bell Labs is pursuing efforts to realize such algorithms with research on near-term approaches that benefit present third-generation (3G) systems and the development of control features for future networks that perform dynamic parameter adjustment across protocol layers. In this paper, we describe the development of conceptual approaches, algorithms, modeling, simulation, and real-time measurements that provide the foundation for future dynamic network optimization techniques.

User-level QoS and traffic engineering for 3G wireless 1×EV-DO systems

Third-generation (3G) wireless systems such as 3G1X, 1×EV-DO, and 1xEV-DV provide support for a variety of high-speed data applications. The success of these services critically relies on the capability to ensure an adequate quality of service (QoS) experience to users at an affordable price. With wireless bandwidth at a premium, traffic engineering and network planning play a vital role in addressing these challenges. We present models and techniques that we have developed for quantifying the QoS perception of 1×EV-DO users generating file transfer protocol (FTP) or Web browsing sessions. We show how user-level QoS measures may be evaluated by means of a Processor-Sharing model that explicitly accounts for the throughput gains from multi-user scheduling. The model provides simple analytical formulas for key performance metrics such as response times, blocking probabilities, and throughput. Analytical models are especially useful for network deployment and in-service tuning purposes due to the intrinsic difficulties associated with simulation-based optimization approaches.