It is shown that bandwidth estimation in packet networks can be viewed in terms of min-plus linear system theory. The available bandwidth of a link or complete path is expressed in terms of a {\em service curve}, which is a function that appears in the network calculus to express the service available to a traffic flow. The service curve is estimated based on measurements of a sequence of probing packets or passive measurements of a sample path of arrivals. It is shown that existing bandwidth estimation methods can be derived in the min-plus algebra of the network calculus, thus providing further mathematical justification for these methods. Principal difficulties of estimating available bandwidth from measurement of network probes are related to potential non-linearities of the underlying network. When networks are viewed as systems that operate either in a linear or in a non-linear regime, it is argued that probing schemes extract the most information at a point when the network crosses from a linear to a non-linear regime. Experiments on the Emulab testbed at the University of Utah evaluate the robustness of the system theoretic interpretation of networks in practice. Multi-node experiments evaluate how well the convolution operation of the min-plus algebra provides estimates for the available bandwidth of a path from estimates of individual links.
MR-BART: Multi-Rate Available Bandwidth Estimation in Real-Time
本文提出了多速率实时可用带宽（Multi-Rate Bandwidth Available in Real Time，MR-BART）算法来估计网络路径的端到端可用带宽（Available Bandwidth，AB），该算法是对利用多速率探测包序列和卡尔曼滤波的实时可用带宽（Bandwidth Available in Real Time，BART）算法的扩展。通过与BART算法的比较，证明了该方法比BART算法具有更好的鲁棒性和更快的收敛速度，并且能够获得更精确的AB估计。进一步分析了MR-BART算法的估计误差，得到了基于系统参数的AB估计误差的解析表达式和经验表达式。
ProCAVE: A Self-Adaptive, Full-Lifecycle Edge Caching Framework for Video Streaming via Predictive Bandwidth Estimation and Preference-Aware Deep Reinforcement Learning
Tarbiat Modares University（塔比尔·莫达雷斯大学）；ICT Research Institute（信息通信技术研究所）
