Today’s Internet Protocol (IP), the Internet’s network-layer protocol, providesa best-effort service to all users without any guaranteed bandwidth. However,for certain applications that have stringent network performance requirementsin terms of bandwidth, it is significantly important to provide Quality of Ser-vice (QoS) guarantees in IP networks. The end-to-end available bandwidth of anetwork path, i.e., the residual capacity that is left over by other traffic, is deter-mined by its tight link, that is the link that has the minimal available bandwidth.The tight link may differ from the bottleneck link, i.e., the link with the minimalcapacity.Passive and active measurements are the two fundamental approaches usedto estimate the available bandwidth in IP networks. Unlike passive measurement tools that are based on the non-intrusive monitoring of traffic, active toolsare based on the concept of self-induced congestion. The dispersion, whicharises when packets traverse a network, carries information that can reveal relevant network characteristics. Using a fluid-flow probe gap model of a tight linkwith First-in, First-out (FIFO) multiplexing, accepted probing tools measure thepacket dispersion to estimate the available bandwidth. Difficulties arise, how-ever, if the dispersion is distorted compared to the model, e.g., by non-fluidtraffic, multiple tight links, clustering of packets due to interrupt coalescingand inaccurate time-stamping in general. It is recognized that modeling theseeffects is cumbersome if not intractable.To alleviate the variability of noise-afflicted packet gaps, the state-of-the-artbandwidth estimation techniques use post-processing of the measurement results, e.g., averaging over several packet pairs or packet trains, linear regression,or a Kalman filter. These techniques, however, do not overcome the basic as-sumptions of the deterministic fluid model. While packet trains and statisticalpost-processing help to reduce the variability of available bandwidth estimates,these cannot resolve systematic deviations such as the underestimation biasin case of random cross traffic and multiple tight links. The limitations of thestate-of-the-art methods motivate us to explore the use of machine learning inend-to-end active and passive available bandwidth estimation.We investigate how to benefit from machine learning while using standard packet train probes for active available bandwidth estimation. To reducethe amount of required training data, we propose a regression-based scale-invariant method that is applicable without prior calibration to networks of arbitrary capacity. To reduce the amount of probe traffic further, we implementa neural network that acts as a recommender and can effectively select theprobe rates that reduce the estimation error most quickly. We also evaluate ourmethod with other regression-based supervised machine learning techniques.Furthermore, we propose two different multi-class classification-based meth-ods for available bandwidth estimation. The first method employs reinforcement learning that learns through the network path’s observations withouthaving a training phase. We formulate the available bandwidth estimation as asingle-state Markov Decision Process (MDP) multi-armed bandit problem andimplement the ε-greedy algorithm to find the available bandwidth, where ε isa parameter that controls the exploration vs. exploitation trade-off.We propose another supervised learning-based classification method to ob-tain reliable available bandwidth estimates with a reduced amount of networkoverhead in networks, where available bandwidth changes very frequently. Insuch networks, reinforcement learning-based method may take longer to con-verge as it has no training phase and learns in an online manner. We also evaluate our method with different classification-based supervised machine learning techniques. Furthermore, considering the correlated changes in a network’straffic through time, we apply filtering techniques on the estimation results inorder to track the available bandwidth changes.Active probing techniques provide flexibility in designing the input struc-ture. In contrast, the vast majority of Internet traffic is Transmission ControlProtocol (TCP) flows that exhibit a rather chaotic traffic pattern. We investigatehow the theory of active probing can be used to extract relevant informationfrom passive TCP measurements. We extend our method to perform the estima-tion using only sender-side measurements of TCP data and acknowledgmentpackets. However, non-fluid cross traffic, multiple tight links, and packet lossin the reverse path may alter the spacing of acknowledgments and hence in-crease the measurement noise. To obtain reliable available bandwidth estimatesfrom noise-afflicted acknowledgment gaps we propose a neural network-basedmethod.We conduct a comprehensive measurement study in a controlled networktestbed at Leibniz University Hannover. We evaluate our proposed methodsunder a variety of notoriously difficult network conditions that have not beenincluded in the training such as randomly generated networks with multipletight links, heavy cross traffic burstiness, delays, and packet loss. Our testingresults reveal that our proposed machine learning-based techniques are able toidentify the available bandwidth with high precision from active and passivemeasurements. Furthermore, our reinforcement learning-based method without any training phase shows accurate and fast convergence to available band-width estimates.