RIA: Resource Management for Networks: Service Provisioning, Flow Control, and Robustness

德克萨斯大学奥斯汀分校de Veciana RIA：网络资源管理：服务提供、流量控制和鲁棒性 这项工作的重点是通信网络的资源管理计划的发展。 基于模型的方法，使用有效的和解耦的带宽约束，保证个人和聚合的服务质量要求进行了研究。重点是实际问题，以及在网络层面上了解这些近似的准确性。 这些概念的进一步研究应该阐明流量控制对网络容量的影响。 由于在实践中，交通统计和需求可能是未知的或随时间变化的，在线估计和控制的方法也进行了研究。 例如，提出了一种自调谐速率控制节流阀，其原则上可以通过监测到达过程来最小化受到丢失或延迟约束的业务流的突发性。 这是迈向智能网络的第一步，智能网络可以监控、学习和适应流量统计和需求。 本研究的最终目标是开发基于给定流量流的性能保证的“鲁棒性”的比较方法。

University of Texas at Austin Gustavo A. de Veciana RIA: Resource Management for Networks: Service Provisioning, Flow Control, and Robustness This focus of this work is the development of resource management schemes for communication networks. Model based approaches using effective and decoupling bandwidth constraints that guarantee both individual and aggregate quality of service requirements are investigated. The emphasis is on practical issues, as well as on understanding the accuracy of these approximations at the network level. Further study of these concepts should elucidate the impact of flow control on network capacity. Since, in practice, traffic statistics and demand may be unknown or time varying, methods for on-line estimation and control are also investigated. For example, a self-tuning rate control throttle is proposed which could in principle minimize a traffic stream's burstiness subject to a loss or delay constraint by monitoring the arrival process. This is a first step towards intelligent networks that monitor, learn, and adapt to the traffic statistics and demands. The final goal of this research is to develop comparison methods based on the "robustness" of a performance guarantee for a given traffic stream.