Measuring available bandwidth and capacity represents an essential requirement for a multitude of network applications spanning from traffic engineering and admission control to network security. Measurement techniques frequently presume to know capacity a priori, but this constitutes a weak premise in a number of modern scenarios due to conditions such as abstractions in infrastructure virtualization, dynamic demands in resource sharing and fluctuations in interference, all of which can affect capacity in short time spans. Despite consistent efforts, currently employed techniques struggle to balance accuracy, intrusion and freshness, depending on either substantial intrusion, onerous processing or unfeasible deployment. Recent developments on data plane programmability have breathed new life into this undertaking, allowing observation points to be more efficiently distributed and programmable packet methods to be executed in-situ. This paper proposes CAPEST, a passive capacity and available bandwidth measurement method for the data plane, employing packet dispersion and autocorrelation. The method is evaluated regarding its parametrization sensitivity, its intrusion and freshness in comparison to state-of-the-art techniques and its performance in the real-world application of video routing. CAPEST was found to incur substantially (80%) less intrusion and achieve 10% better accuracy, all the while providing an order of magnitude improvement in freshness.

To deal with the massive traffic produced by video applications, mobile operators rely on offloading technologies such as Small Cells, Content Delivery Networks and, shortly, Cloud Edge and 5G Device to Device communications. Although these techniques are fundamental for improving network efficiency, they produce a multitude of paths onto which the user traffic can be forwarded. Thus, a critical problem arises about how to handle the increasing video traffic while managing the interplay between infrastructure optimization and the user's Quality of Experience (QoE). Solving this problem is remarkably difficult, and recent investigations do not consider the large-scale context of mobile operator networks. To address this issue, we present a novel QoE-aware path deployment scheme for large-scale SDN-based mobile networks. The scheme relies on both a polynomial-time algorithm for composing multiple QoS metrics and a scalable QoS to QoE translation strategy. Considering real mobile operator network and video traffic traces, we show that the proposed algorithm outperformed state-of-the-art approaches by reducing impaired videos in aggregate MOS by at least 37% and lowering accumulated video stall length four times.

Due to the fact that video streaming is the current "killer" application and for competitiveness, telecommunication service providers need to be able to answer a fundamental question: to which extent is the available network infrastructure able to successfully provide users with a satisfactory experience when running video streaming applications? Answering this question is far from trivial because existing techniques are neither scalable nor accurate enough. To address this issue, we propose a model to predict video streaming quality based on the observation of performance indicators of the underlying IP network. To accomplish this objective, the proposed model - created using LTE networks as case study - leverages low network consumption active measurements and machine learning techniques. Obtained results show that the proposed solution produces accurate estimates (average error of less than 10%) while keeping intrusiveness around twenty times lower than traditional techniques.