The common utilization-based definition of available bandwidth and many of the existing tools to estimate it suffer from several important weaknesses: i) most tools report a point estimate of average available bandwidth over a measurement interval and do not provide a confidence interval; ii) the commonly adopted models used to relate the available bandwidth metric to the measured data are invalid in almost all practical scenarios; iii) existing tools do not scale well and are not suited to the task of multi-path estimation in large-scale networks; iv) almost all tools use ad-hoc techniques to address measurement noise; and v) tools do not provide enough flexibility in terms of accuracy, overhead, latency and reliability to adapt to the requirements of various applications. In this paper we propose a new definition for available bandwidth and a novel framework that addresses these issues. We define probabilistic available bandwidth (PAB) as the largest input rate at which we can send a traffic flow along a path while achieving, with specified probability, an output rate that is almost as large as the input rate. PAB is expressed directly in terms of the measurable output rate and includes adjustable parameters that allow the user to adapt to different application requirements. Our probabilistic framework to estimate network-wide probabilistic available bandwidth is based on packet trains, Bayesian inference, factor graphs and active sampling. We deploy our tool on the PlanetLab network and our results show that we can obtain accurate estimates with a much smaller measurement overhead compared to existing approaches.
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）
