The Web has shifted towards a mobile-first ecosystem with tools, frameworks, and forums explicitly discussing and catering for the mobile users, both mobile apps and mobile web-pages. Unfortunately, much of the studies and designs are often based on analysis and findings from developed regions (e.g., N. America and Europe) or based on user-generated data (introducing bias). In this paper, we present one of the first studies to understand the interplay between hardware characteristics (e.g., cellular and mobile) on expected network and application level performance in Brazil (the largest developing region in S. America). We analyze more than 170 million measurement sessions collected from within the network of one of the largest Mobile Network Operators in Brazil. Our findings (1) illustrate limitations of existing crowdsourced measurements and inaccuracies in assumptions about adoption patterns and performance in the global south, (2) highlight the differences between recommendations made by standardization bodies and real world performance, (3) disclose a significant change pre- and post-pandemic, and (4) quantify the benefits of using both client side and network data for analysis.

The pursuit of self-driving networks is increasing pressure on adopting intelligent, edge-based networking services. However, deploying autonomous network models within operational and large-scale infrastructures entails substantial risks that require rigorous verification and validation procedures. In this context, the application of a Network Digital Twin (NDT) is emerging as a viable approach towards intelligent network decision-making based on high-fidelity models built upon digital representations of physical network devices (i.e., Digital Twins). In this paper, we take the first steps towards efficiently provisioning NDT models. To that end, we introduce the Digital Twin Network Provisioning Problem (DigiNet), which encompasses the optimal placement of NDT models and the efficient collection of telemetry data for synchronizing NDT models with their physical counterparts. We theoretically formalize DigiNet as a Mixed-Integer Linear Programming (MILP) model and present a polynomial-time heuristic. Our results show that DigiNet outperforms baseline approaches by up to 10x regarding the number of NDT models provisioned.

In-band Network Telemetry (INT) is a novel network monitoring approach mainly fostered by programmable network devices. Despite existing efforts toward the orchestration of INT, little has yet been done to provide fault-tolerant mechanisms in the data plane (e.g., to address hardware failure). In this paper, we introduce InPatching - an in-network approach to fast recover INT-based monitoring from network link failures. InPatching is implemented in the data plane and allows the application of detours in an autonomous and coordinated manner without the control plane intervention. To provide efficient detours to INT solutions, we formalize the fault-tolerant probing planning for INT by means of a MILP (Mixed-Integer Linear Programming) model. We prototype InPatching in P4 and we show that it can recover from fault conditions much faster than control plane solutions (up to 18X), while not imposing substantial overhead.

In-band Network Telemetry (INT) is a novel net-work monitoring mechanism that improves fine-grained net-work visibility. Despite the increasing research efforts towards the orchestration of INT data acquisition, little has yet been done to efficiently collect telemetry data from the network considering monitoring applications requirements. In this paper, we introduce DyPro - a dynamic probing planning for INT. In particular, DyP ro ensures that telemetry dependencies are always satisfied by monitoring application requirements. We theoretically formalize it as a Mixed-Integer Linear Programming (MILP) optimization model and propose a heuristic procedure to efficiently solve it. Results show that DyP ro can outperform state-of-the-art solutions by up to 5x regarding the percentage of monitoring applications satisfied.

Ranging from traditional video streaming to Virtual Reality (VR) videos, the demand for video applications to mobile devices is booming. In the context of mobile operators a challenging problem is how to handle the increasing video traffic while managing the interplay between infrastructure optimization and QoE. Solving this issue is remarkably difficult, and recent investigations do not consider large-scale networks. In this dissertation paper we explore the solution space of efficient video streaming over mobile networks. First, we propose a model to predict video streaming quality based on the observation of performance indicators of the underlying IP network. Second, we introduce a novel QoE-aware path deployment heuristic for large-scale SDN-based mobile networks. Third, based on the lessons learned with QoE prediction for traditional video streaming, we finally explore the VR video domain by proposing PERCEIVE and VR-EXP. PERCEIVE is a two-stage method for predicting the perceived quality of adaptive VR videos when streamed through mobile networks. In turn, VR-EXP consists of an experimentation platform that allows in-depth evaluation of state-of-the-art VR video optimization techniques. Obtained results show that the combination of the proposed methods for QoE-aware path selection outperformed state-of-the-art approaches.

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 cope with the massive bandwidth demands of Virtual Reality (VR) video streaming, both the scientific community and the industry have been proposing optimization techniques such as viewport-aware streaming and tile-based adaptive bitrate heuristics. As most of the VR video traffic is expected to be delivered through mobile networks, a major problem arises: both the network performance and VR video optimization techniques have the potential to influence the video playout performance and the Quality of Experience (QoE). However, the interplay between them is neither trivial nor has it been properly investigated. To bridge this gap, in this article, we introduce VR-EXP, an open-source platform for carrying out VR video streaming performance evaluation. Furthermore, we consolidate a set of relevant VR video streaming techniques and evaluate them under variable network conditions, contributing to an in-depth understanding of what to expect when different combinations are employed. To the best of our knowledge, this is the first work to propose a systematic approach, accompanied by a software toolkit, which allows one to compare different optimization techniques under the same circumstances. Extensive evaluations carried out using realistic datasets demonstrate that VR-EXP is instrumental in providing valuable insights regarding the interplay between network performance and VR video streaming optimization techniques.

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.

The demand of Virtual Reality (VR) video streaming to mobile devices is booming, as VR becomes accessible to the general public. However, the variability of conditions of mobile networks affects the perception of this type of high-bandwidth-demanding services in unexpected ways. In this situation, there is a need for novel performance assessment models fit to the new VR applications. In this paper, we present PERCEIVE, a two-stage method for predicting the perceived quality of adaptive VR videos when streamed through mobile networks. By means of machine learning techniques, our approach is able to first predict adaptive VR video playout performance, using network Quality of Service (QoS) indicators as predictors. In a second stage, it employs the predicted VR video playout performance metrics to model and estimate end-user perceived quality. The evaluation of PERCEIVE has been performed considering a real-world environment, in which VR videos are streamed while subjected to LTE/4G network condition. The accuracy of PERCEIVE has been assessed by means of the residual error between predicted and measured values. Our approach predicts the different performance metrics of the VR playout with an average prediction error lower than 3.7% and estimates the perceived quality with a prediction error lower than 4% for over 90% of all the tested cases. Moreover, it allows us to pinpoint the QoS conditions that affect adaptive VR streaming services the most.

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.

OWD is becoming increasingly important nowadays, as SLA agreements use it as a parameter to ensure QoS levels. However, its measurement is still a much debated topic due to the difficulty in the clock synchronization process. The paper approaches a new methodology to measure OWD, using NTP protocol with virtual clocks and bigger packets. This approach works over a measurement tool developed by the authors, called Netmetric, and is flexible enough to be applied in full-mesh networks, without lack in clock consistency during concurrent NTP synchronizations. The proposed method was validated using NTCUns simulator, tested over Vivo/Telefonica's 3G Network, and compared to OWAMP.

The necessity to keep acceptable service quality levels in IP networks is undeniable nowadays, particularly with the improvement of the next generation networks. This promotes the use and justifies the need for solutions that enable network measurement and monitoring. Network measurement and performance data acquisition is currently a target of many research and development efforts, mainly due to its applicability in different situations, including network traffic engineering. In this field, there are two measurement approaches: passive and active. Passive measurement is based on the observation of ordinary traffic of an operational network, without the introduction of any kind of traffic in the monitoring probe. On the other hand, the active approach tries to acquire performance indicators using a controlled traffic generation and transmission through the analyzed network Knowing the potentialities and deficiencies of such techniques, and specifically directing our interest to IP network traffic engineering, this paper emphasizes the active measurement particularities, proposing the project, modeling and implementation of a standardized and extensible architecture for this kind of measurement. We try to join together different metric acquisition processes in a unified system. To do so, we present the architecture entities and the conceived and implemented protocol that are used for communication between these entities, highlighting the technique used to minimize the traffic impact in the ongoing network operation. Finally, we also introduce the idea behind the architecture flexibility through its plug-in approximation.