Global mobile connectivity depends on international roaming, which enables subscribers to use their service abroad through agreements ensuring traffic exchange between home and visited networks. Poor interconnect performance over the IPX network can degrade user experience and cause revenue loss, yet roaming remains underexplored due to proprietary systems and limited transparency. We present a large-scale study based on nine months of active probing from gateways across seven global regions, yielding nearly four billion latency measurements spanning 173 countries and 399 operators. This dataset enables the analysis of temporal trends, regional disparities, anomalies, and routing inefficiencies in IPX-based roaming. Our findings show how interconnection choices, carrier selection, and routing detours impact performance, advancing our understanding of global roaming trade-offs and allowing future optimization.

The transition from LTE to 5G, and therefore from a monolithic network architecture to a service based architecture, introduces a myriad of new challenges. Most significantly, the decomposition of core components into individual services introduces an extensive signalling traffic overhead within the 5G core network. Additionally, there is a general lack of observability for the 5G core, hindering advancements in modelling and optimization for the system as a whole as well as the individual network functions. To address this, we present a measurement methodology to increase observability within the 5G core system and extract crucial information for the signalling traffic for specific user equipments within the individual network functions. Furthermore, we use our proposed methodology to conduct a case study on the performance of the attachment procedure in Open5GS, a commonly used open source implementation, under different loads. Lastly, we investigate the stability of the Access and Mobility Management Function under heavy load.

Mobile Instant Messaging (MIM) applications such as WhatsApp transformed human communication by enabling global exchange of various message types, such as text, image, video, or voice, at any time. Network providers are facing a substantial user base and network load which is especially high in group chats where each message needs to be distributed to each member. Due to end-to-end encryption, network operators must obtain knowledge about the communication and the resulting load on the network by other means, which makes it necessary to model the network traffic of MIM. In this work, we therefore present an approach to source traffic modeling for MIM. After identifying the building blocks of a Source Traffic Model (STM) for MIM, we address existing gaps through studies on MIM communication networks, contact proximity, media compression and payload size, as well as media file size distribution. Combining existing literature and our work, we present and implement a modular STM approach which can be used for developing STMs for MIM. Using an exemplary STM, we evaluate the daily network traffic per user. With this, we provide a comprehensive description of MIM in the network researching context and enable consideration of MIM in future network design.

The demands on networks are increasing at a fast pace. In particular, real-time applications have very strict network requirements. However, setting up a network that hosts real-time applications is a cost-intensive endeavor, especially for experimental systems such as testbeds. Systems that provide guaranteed real-time networking capabilities usually work with expensive, high-rate software-defined switches. In contrast, real-time networking systems based on low-cost hardware face the limitation of lower link speeds. This paper fills this gap and presents Low-Cost Deterministic Networking (LCDN), a system designed to work with inexpensive, common off-the-shelf switches and devices. LCDN works at Gigabit speed and enables powerful testbeds to host real-time applications with strict delay guarantees. LCDN’s performance is similar to industrial- and production-grade solutions. This paper also provides an evaluation of the determinism of a low-cost switch and a Raspberry Pi used as an end-device to demonstrate the applicability of LCDN for inexpensive, low-power systems.

Explainable Artificial Intelligence (XAI) is essential for the acceptance of machine learning (ML) models, especially in critical domains like network security. Administrators need interpretable explanations to validate decisions, yet existing XAI methods often suffer from low consensus, where different techniques yield conflicting explanations. A key factor contributing to this issue is the presence of correlated features, which allows multiple equivalent but divergent explanations. While decorrelation techniques, such as Principal Component Analysis (PCA), can mitigate this, they often reduce interpretability by abstracting original features into complex combinations. This work investigates whether feature decorrelation via decomposition techniques can improve consensus among post-hoc XAI methods in the context of ML-based network intrusion detection (ML-NIDS). Using both NIDS and synthetic data, we analyze the effect of decorrelation across different models and preprocessing. We find that decorrelation can significantly improve consensus, but its effectiveness is highly dependent on the underlying model, preprocessing, and dataset characteristics. We also explore sparsityinducing variants of PCA to partially recover interpretability, though results vary depending on the level of sparsity enforced.

Video-based communication is central to today’s digital society. While YouTube and Netflix once dominated video traffic, traditional broadcasters now run their own streaming services, and live-streaming platforms are expanding. Users expect high-resolution video with minimal buffering and latency, especially for live content. Meeting these demands requires infrastructure that balances performance, cost, energy, and resources, supported by comprehensive traffic monitoring and analysis. Artificial Intelligence plays a key role, particularly in encrypted traffic classification and quality prediction, with treebased Machine Learning models like Random Forests (RF) widely used. However, research often emphasizes small accuracy gains while overlooking the raising energy and resource costs of such models, an increasing conflict with sustainability goals. To study this trade-off, we implement RF models with varying feature counts and complexities to predict video re-buffering and buffer health, two key quality indicators. Using a dataset of more than 11,000 YouTube sessions, we analyze how prediction performance scales with model complexity and energy consumption across the full pipeline, revealing several unexpected results.

Delivering popular short video streaming services like TikTok, Instagram Reels, or YouTube Shorts, poses substantial challenges for service providers and network operators. This is not only due to high download volumes but also due to high-volume pre-loading strategies that cause high bandwidth demand variations. These strategies, designed to reduce initial delays, can additionally lead to bandwidth excess when users swipe quickly through videos and consume only a fraction of downloaded content. This creates inefficiencies and unbalanced network resource utilization, particularly in competitive bandwidth environments. To address these challenges, we investigate the effectiveness of bandwidth capping in this paper, i.e., limiting the throughput of video flows in the network. We conduct measurement studies to analyze the impact of capping on network interactions and Quality of Experience (QoE) of three popular short video services in different scenarios. We find that capping substantially reduces download volume (15% -- 45% median reduction) and bandwidth excess (18% -- 52% mean reduction), while bandwidth utilization fairness improves. Meanwhile, QoE surprisingly remains nearly unaffected in most cases, with only minor statistical differences in a few scenarios.

A key focus of 6G development is ensuring sustainable operation of Information and Communication Technology systems, with the major goal of achieving Net-Zero carbon emissions. Although vendors promote the energy efficiency of their latest products, overall energy consumption in the sector continues to rise due to the increasing number of devices and data traffic. The absence of a standardized energy efficiency metric further complicates meaningful comparisons. Energy intensity-defined as the energy required to process or transmit a bit or byte of data-has recently gained attention as a potential metric. However, a comprehensive analysis of energy intensity across the diverse scenarios, technologies, and devices in the Internet of Things (IoT) ecosystem for 6G networks remains unexplored. This study addresses this gap by evaluating energy intensity as a key metric in a 6G IoT context, comparing measured and simulated access network technologies. It also identifies the limitations of energy intensity and proposes alternative metrics for assessing energy efficiency.

The ongoing and evolving use of communication networks presents two critical challenges for current and future networks that require special attention: (1) managing the enormous and continuously growing data traffic efficiently, and (2) handling the large number of end devices resulting from the advent of the Internet of Things in the control and data plane. In addition to these challenges, there is an urgent need to reduce energy consumption, make more efficient use of resources, and optimize processes without compromising service quality. We are addressing these efforts comprehensively by focusing on the monitoring and quality assessment of streaming applications, which significantly contribute to global Internet traffic, and conduct a general performance analysis for a Long Range Wide Area Network, one of the fastest-growing Low Power Wide Area Network solutions.

High-quality communication technology is essential in our globalized, digital world. However, network issues and poor mobile connectivity still impact service quality and user experience across various applications. The roll-out of 5G networks promised improvements in network quality including lower latency and better throughput. But questions whether these improvements have materialized, about regional differences in mobile network quality, and whether the current network infrastructure is prepared to support today's, currently emerging, and future applications in a high quality remain. To address these questions, we analyzed over 225 million network quality measurements from 2022 and 2023 provided by the company OpenSignal to assess mobile network quality as perceived by an end user along all roadways in the German state Bavaria. By examining network generations, throughput, and latency metrics, we provide valuable insights for network planning, helping both engineers and the public in understanding the current state of mobile connectivity on Germany's roads.

Global connectivity demands extensive cellular network coverage, enabling instant data transmission to users worldwide. The growing need for faster, low-latency networks drives the global 5G rollout, aiming to cover one-third of the population by 2025. While 5G promises enhanced Quality of Service, network providers must balance customer expectations with political pressure and the costs of expansion. However, it remains unclear whether current 5G deployments adequately address connectivity challenges and deliver consistently high Quality of Experience. To investigate this, we analyze mobile network quality across Bavaria using over 225 million measurements from Opensignal. By correlating key performance metrics like throughput, latency, jitter, and packet loss with network generation and provider, we evaluate technology and service impacts on network quality. These findings are mapped to Bavaria's population to explore urbanization effects on performance and identify potential digital divides between urban and rural areas.

Video conferencing has become indispensable in both professional and social contexts. From a networking point of view, timely distribution of media to all clients participating in a video conference is required. However, fluctuating network conditions pose a challenge, so that a high Quality of Experience (QoE) might not always be achieved. When assessing how network conditions impact the QoE of video conferencing, previous QoE studies focused on one-to-one settings and suffered from small numbers of participants due to their costly laboratory setups. In this work, we investigate crowdsourcing-based QoE studies for video conferencing in order to obtain a higher number and more globally distributed and diverse participants. For this, we consider multi-party video conferencing and design a web-based study framework which provides a high realism to online study participants by involving themselves in the presented video call. Finally, we conduct a QoE study with our framework to demonstrate the suitability of crowdsourcing for assessing the QoE impact of network-level and application-level degradations of video conferencing applications.

Short-form video (SFV) services, such as TikTok and Instagram Reels, have rapidly gained widespread popularity, accumulating billions of users. However, evaluating Quality of Experience (QoE) for these services poses challenges as they are typically consumed in mobile and interactive settings. In this paper, we introduce a novel QoE evaluation framework for SFV, which allows for a controlled presentation of stimuli and a reliable collection of valid QoE ratings in an unsupervised setting, while maintaining the authenticity of the mobile, interactive SFV experience. We use our framework to conduct two QoE studies on the impact of waiting times, i.e., initial delay and stalling, on the QoE of SFV via crowdsourcing. Our findings reveal that initial delay results in a three times higher probability that users swipe to the next video within the first ten seconds compared to stalling. In contrast, the Mean Opinion Score (MOS) of stalling is up to 0.4 lower than initial delay for the same waiting time conditions. These insights provide valuable guidelines for optimizing SFV content delivery to enhance user satisfaction, but also highlight the need for novel QoE models, which can describe not only perceived QoE but also resulting user engagement and behavior.

The popularity of short-form video requires careful planning by service providers and network operators aiming to deliver high Quality of Experience (QoE) while minimizing bandwidth wastage from excessive preloading and unpredictable swiping behavior. Service providers utilize preloading strategies to optimize these trade-offs against their target metrics. In this work, we introduce a Markov model that estimates key quality indicators (KQIs) and bandwidth wastage for common preloading strategies, network conditions, and swiping behaviors. We compare two preloading strategies and show that the number of preloaded segments per video results in a trade-off between initial delay and stalling. Further, we highlight the key impact of swiping behavior on both KQIs and bandwidth wastage.

Modern network monitoring applications often rely on traditional machine learning models conceived for specific analysis tasks, which require extensive feature engineering, retraining for different use cases, and struggle with generalization. This lack of adaptability makes the deployment of AI/ML solutions in network monitoring a daunting task, as each new scenario requires significant reconfiguration, manual tuning, and retraining efforts, undermining the broader adoption of AI/ML for network traffic analysis. Time Series Foundation Models (TSFMs), pre-trained on vast and diverse time-series datasets, offer a promising alternative in the network monitoring realm by enabling zero-shot and few-shot adaptability across different monitoring scenarios. In this work, we explore the potential of TSFMs for network monitoring by evaluating their performance in a challenging analysis task: estimating video streaming Quality of Experience (QoE) from encrypted network traffic. Our study assesses the zero-shot and few-shot capabilities of state-of-the-art TSFMs, the impact of time-series granularity, and the role of common traffic features in performance. Using real-world video streaming QoE datasets, we show that TSFMs achieve competitive results in a zero-shot setting – plug-and-play approach, and that their performance can be easily and cost-effectively improved through few-shot learning techniques, even when applied on NetFlow-like features with coarse granularity. Beyond the specific video streaming QoE monitoring application, our findings demonstrate the viability and broader applicability of TSFMs to network monitoring tasks, opening the door to more scalable and generalizable network management solutions.

Autoscaling is a core capability in cloud computing with significant impact on service quality and cost. Modern applications, like microservices and serverless functions, consist of many containers that enable fine-grained, component-wise scaling. Effective autoscaling across large, heterogeneous service landscapes remains challenging. As cloud adoption increases, workloads have become more diverse, exhibiting highly variable request patterns, payload characteristics, and response time requirements. This limits the effectiveness of conventional autoscalers, whose fixed intervals and cooldown periods restrict responsiveness. At the same time, the growing number of services and frequent updates strain approaches based on predefined models, motivating more adaptive solutions.

The increasing complexity and frequency of cyber attacks require Network Intrusion Detection Systems (NIDS) that can adapt to evolving threats. Artificial intelligence (AI), particularly machine learning (ML), has gained increasing popularity in detecting sophisticated attacks. However, their potential lack of interpretability remains a significant barrier to their widespread adoption in practice, especially in security-sensitive areas. In response, various explainable AI (XAI) methods have been proposed to provide insights into the decision-making process. This paper investigates whether these XAI methods, including SHAP, LIME, Tree Interpreter, Saliency, Integrated Gradients, and DeepLIFT, produce similar explanations when applied to ML-NIDS. By analyzing consensus among these methods across different datasets and ML models, we explore whether an agreement exists that could simplify the practical adoption of XAI in cybersecurity, as similar explanations would eliminate the need for rigorous selection processes. Our findings reveal varying degrees of consensus among the methods, suggesting that while some align closely, others diverge significantly, highlighting the need for careful selection and combination of XAI tools to enhance trustworthiness in real-world applications.

Artificial Intelligence (AI), particularly Machine Learning (ML), has become prominent in network monitoring, yet its practical adoption, such as for anomaly and intrusion detection, remains limited. Standard AI/ML methods often exclude experts, reducing trust and hindering practical implementations. Active Learning (AL) allows to integrate admins and their expert knowledge into the ML loop by leveraging expert-labeled data. Together with self-training and automated decisions, AL can enhance model performance, trust, and the ability to adapt to system changes. In this work, we evaluate uncertainty-based AL in network monitoring, offering a comprehensive parameter study for best practices in real-world AI/ML adoption. To this end, we evaluate stream-based and pool-based AL across four datasets for various monitoring use cases and conduct a parameter study on ten uncertainty measures, thereby identifying scenarios benefiting from self-training. By analyzing the impact of admin competence on model performance, we offer actionable guidelines towards the practical implementation of AL.

Video streaming has dominated Internet traffic, pushing network providers to ensure high-quality services to avoid customer churn. However, predicting streaming quality is challenging due to traffic encryption, requiring extensive network monitoring. While several prediction approaches have been studied, they often overlook resource and energy demands. To address this, we analyze existing methods, quantifying monitoring efficiency to predict video quality degradation. Finally, we highlight significant differences in efficiency, driven by data requirements and the prediction approach, offering insights for providers to select a suitable method for their needs.

5G New Radio (NR) is designed to support diverse services, shifting from a fixed smartphone-centric infrastructure to flexible deployment options tailored for verticals like Ultra-Reliable Low-Latency Communications (URLLC) and Machine-Type Communications (MTC). To optimize the QoS of 5G NR to the service needs, understanding the impact of the introduced configuration parameters is critical. This paper investigates the configuration space of 5G NR using open-source 5G standalone deployments based on OpenAirInterface (OAI) and srsRAN (SRS). We conduct a detailed study on the impact of Next Generation NodeB (gNB) configurations on uplink and downlink throughput and latency, we compare the 5G NR implementations of OAI and SRS, and we investigate reproducibility across different testbeds at the University of Wuerzburg and NTNU. Our datasets are made publicly available.

Time-Sensitive Networking can provide a wide array of QoS guarantees that can be leveraged in a variety of use cases, ranging from industrial applications to autonomous driving. While the control plane can be configured following either a distributed or a centralized paradigm, it is difficult to make general assertions about the affinity of different topologies and use cases towards these configuration methods. In this work, we propose a hybrid simulation of the Resource Allocation Protocol to evaluate the reservation performance across a wide range of network configurations. Results show distinct behaviors between the configuration paradigms, making a case for decentralized control when scalability is critical. In addition, we make our implementation of the developed hybrid model publicly available.

Continuous advancements in smart solutions are consistently making their way into the market. To address the growing demand for connectivity across the globe, the deployment of large-scale cellular networks is a viable option. However, in many cases, basic monitoring applications or devices do not require high bandwidth or extremely low latency. An excellent alternative to provide coverage to such end devices is LoRaWAN, which significantly increased in popularity in recent years. Yet, LoRaWAN employs a random channel access method for message transmissions, which can lead to sensor interference, message collisions, and, in the worst-case, message loss. Consequently, researchers study alternatives to random channel access, sometimes overlooking LoRaWAN regulations. In this study, we close this gap in literature by conducting a theoretical investigation of a time scheduled channel access approach for LoRaWAN. This approach remains compliant with network regulations for various adjustable network and message parameters. Furthermore, we extend the simple time scheduled approach by introducing variable slot lengths, thereby increasing flexibility and highlighting its potential benefits.

Time-sensitive applications are increasingly prevalent in various network domains, such as industrial, medical, and vehicular communications, imposing substantial demands on network infrastructure. Consequently, ensuring low latency has become a crucial requirement for future networks, particularly through the implementation of deterministic latency network controllers. However, it is essential to recognize that the network controller represents just one facet of network performance management. The configuration of the network’s topology also significantly influences its overall performance. This study, therefore, investigates the impact of different topologies on network performance, specifically focusing on deterministic latency guarantees. Our analysis shows the correlation between graph metrics characterizing the topology and its performance. This correlation facilitates a straightforward ranking of topology performance during critical phases like network planning or expansion. We introduce a readily obtainable graph metric that enables relative performance ranking without the need for exhaustive simulations or emulations. The metric exhibits a Spearman Ranking correlation coefficient exceeding 0.93.

Network and service providers actively analyze and derive metrics for Quality of Experience (QoE) within their systems. This requires appropriate monitoring approaches. Objective QoE monitoring involves translating QoS parameters into QoE scores by applying appropriate mapping functions, e.g., to obtain Mean Opinion Scores (MOS). Alternatively, individual QoE monitoring allows direct assessment of user QoE based on user’s self-reports. We investigate whether individual and objective QoE monitoring yield similar conclusions on expected QoE and QoE fairness. A theoretical framework is introduced and simulation studies provide numerical results to highlight the differences. This is the first work which analyzes and compares individual and objective QoE monitoring from the provider’s perspective. It closes a major gap in the current QoE research.

The integration of web and video applications as dominant Internet content has underscored the importance of Quality of Experience (QoE) for user satisfaction, retention, and digital service success. While current research has extensively studied QoE for individual stimuli, such as web page loading or video streaming, there exists a significant gap in understanding and quantifying QoE for mixed web browsing and video streaming sessions. This paper addresses the critical need to evaluate session QoE when web and video stimuli are combined within a single web session. Employing a crowdsourcing methodology, we investigate the impact of session length, content type, and individual stimuli QoE on the overall session QoE through a full factorial design with both unimpaired and impaired stimuli. Based on these results, we evaluate the applicability of various models to accurately estimate session QoE from information about individual stimuli, offering insights into optimizing the subjective experience in web sessions.

This paper explores the relationship between loading times and Quality of Experience (QoE) in Mobile Instant Messaging (MIM) applications. Using a web application that mimics MIM interfaces, we conducted a QoE study in which participants engaged with a virtual chat partner. We controlled image loading times during chatting and evaluated their impact on QoE, annoyance, and acceptance ratings. Although the results show no difference in the QoE ratings, they clearly show that longer delays lead to greater annoyance and lower user acceptance. These findings underscore the importance for MIM app providers to minimize loading times in order to increase user satisfaction and retention.

In this paper, we present QoEXplainer, a QoE dashboard for supporting humans in understanding the internals of an explainable, data-driven Quality of Experience model. This tool leverages Large Language Models and the concept of Mediators to convey relevant explanations to the user in an understandable, chatbot-like fashion. For this purpose, our tool QoEXplainer integrates a data-driven video streaming QoE model and techniques from Explainable Artificial Intelligence. The resulting data-driven model explanations are illustrated in the dashboard and users can interact with the chatbot to ask questions about the data and QoE model and control the dashboard to enhance model understanding. With this hybrid demo, we aim to conduct a live study at QoMEX 2024 to evaluate Mediators in the context of (data-driven) QoE modelling with domain experts.

The growing popularity of LoRa-based solutions, such as weather, environmental, and climate monitoring, has resulted in larger LoRaWAN deployments and increased network load. However, the random channel access method used in LoRaWAN leads to higher message collision probabilities and potential data loss, diminishing the networks energy efficiency. This paper addresses this issue by proposing a hardware-independent model that assesses energy consumption and efficiency in different LoRaWAN channel access approaches. Through simulation and analysis of real energy values, we provide insights and recommendations for optimizing the network performance and energy usage. This study fills a research gap and contributes to the development of more robust and energy-efficient LoRaWAN systems for various application areas.

Markov reward models are commonly used in the analysis of systems by integrating a reward rate to each system state. Typically, rewards are defined based on system states and reflect the system’s perspective. From a user’s point of view, it is important to consider the changing system conditions and dynamics while the user consumes a service. The key contributions of this paper are proper definitions for (i) system-centric reward and (ii) user-centric reward of the Erlang loss model M/M/n-0 and M/M(x)/n with state-dependent service rates, as well as (iii) the analysis of the relationships between those metrics. Our key result allows a simple computation of the user-centric rewards. The differences between the system-centric and the user-centric rewards are demonstrated for a real-world cloud gaming use case. To the best of our knowledge, this is the first analysis showing the relationship between user-centric rewards and system-centric rewards. This work gives relevant and important insights in how to integrate the user’s perspective in the analysis of Markov reward models and is a blueprint for the analysis of other services beyond cloud gaming while also considering user engagement.

Wide Area Network (WAN) research benefits from the availability of realistic network topologies, e. g., as input to simulations, emulators, or testbeds. With the rise of Machine Learning (ML) and particularly Deep Learning (DL) methods, this demand for topologies, which can be used as training data, is greater than ever. However, public datasets are limited, thus, it is promising to generate synthetic graphs with realistic properties based on real topologies for the augmentation of existing data sets. As the generation of synthetic graphs has been in the focus of researchers of various application fields since several decades, we have a variety of traditional model-dependent and model-independent graph generators at hand, as well as DL-based approaches, such as Generative Adversarial Networks (GANs). In this work, we adapt and evaluate these existing generators for the WAN use case, i. e., for generating synthetic WANs with realistic geographical distances between nodes. We investigate two approaches to improve edge weight assignments: a hierarchical graph synthesis approach, which divides the synthesis into local clusters, as well as sophisticated attributed sampling. Finally, we compare the similarity of synthetic and real WAN topologies and discuss the suitability of the generators for data augmentation in the WAN use case. For this, we utilize theoretical graph metrics, as well as practical, communication network-centric performance metrics, obtained via OMNeT++ simulation.

IoT plays an important role in cellular networks, and its need for global connectivity is driving the rise of Global IoT Providers. These provide service by aggregating multiple mobile providers through roaming, complicating the understanding of the overall mobile ecosystem. This calls for lightweight monitoring solutions, which are crucial to meet the quality demanded by IoT services, and of automatic means to analyze the data, with the final goal to carry out economic and management activities. This paper provides insights from the study of two commercial, widespread IoT providers. We show how monitoring signaling traffic between mobile networks offers a unique opportunity to understand both the IoT customers’ characteristics and the network functioning. Leveraging clustering, we offer the first data-driven methodology to examine large IoT signaling datasets. By analyzing over 1.3 billion signaling dialogues across two providers, we identify common signaling profiles that depend on the specific IoT vertical, likely misconfigured devices, and sudden changes that indicate potential problems. This provides actionable insights for network management decisions and service improvements, and lays the groundwork for future research on IoT traffic modeling.

Network operators require real-time traffic monitoring insights to provide high performance and security to their customers. It has been shown that artificial intelligence and machine learning (ML) can improve the visibility of telemetry systems, especially with encrypted traffic. However, current solutions cannot cope with high traffic rates and volumes in large-scale networks. To realize the ML-driven network intelligence paradigm at terabit scale, we design Marina, a system that spreads monitoring over a highly efficient data plane, which can extract traffic statistics at line rate, and a powerful ML server, which can run monitoring inference using complex ML models. We apply temporal microaggregation into sub-second time slots and extract moment-based statistics. These allow to flexibly obtain accurate ML-based monitoring decisions during the next time slot. To demonstrate the scalability of our design, we implement and evaluate a Marina data plane prototype on a Barefoot Wedge 100BF-65X P4 switch, which can monitor more than 520,000 concurrent flows at full switching capacity of 6.4 Tbps. We validate the analytics capabilities enabled by our Marina implementation for four ML-driven real-time monitoring tasks with a broad set of standard ML models, achieving comparable or better than state-of-the-art results.

Reaching close-to-optimal bandwidth utilization in dynamic adaptive streaming over HTTP (DASH) systems can, in theory, be achieved with a small discrete set of bit rate representations. This includes typical bit rate ladders used in state-of-the-art DASH systems. In practice, however, we demonstrate that bandwidth utilization, and consequently the quality of experience (QoE), can be improved by offering a continuous set of bit rate representations, i.e., a continuous bit rate slide (COBIRAS). Moreover, we find that the buffer fill behavior of different standard adaptive bit rate (ABR) algorithms is sub-optimal in terms of bandwidth utilization. To overcome this issue, we leverage COBIRAS’ flexibility to request segments with any arbitrary bit rate and propose a novel ABR algorithm MinOff , which helps maximizing bandwidth utilization by minimizing download off-phases during streaming. To avoid extensive storage requirements with COBIRAS and to demonstrate the feasibility of our approach, we design and implement a proof-of-concept DASH system for video streaming that relies on just-in-time encoding ( JITE ), which reduces storage consumption on the DASH server. Finally, we conduct a performance evaluation on our testbed and compare a state-of-the-art DASH system with few bit rate representations and our JITE DASH system, which can offer a COBIRAS, in terms of bandwidth utilization and video QoE for different ABR algorithms.

The steady growth of IoT networks and the rise of LoRa and LoRaWAN as a wireless communication protocol in smart city deployments invoke the need for energy efficient and performance focused channel access mechanisms. Since LoRa makes use of the random access protocol ALOHA, the expected collision probability is directly related to the density of deployed IoT devices. In order to alleviate this scalability issue of LoRa deployments, and the energy losses caused by rising packet collisions, this work proposes a novel channel access mechanism that aims to mitigate high collision probabilities as a result of dense, bursty periods of transmissions, without the need for centralized control instances or dedicated control messages. Based on simulation results, we show that the proposed mechanism is able to mitigate between 30% and 100% of collisions compared to pure ALOHA, depending on factors such as system load and device density.

Software-based network security solutions using SDN/NFV provide high flexibility and short development cycles, but may impose a bottleneck onto the network due to their lack of ASIC-based hardware packet processing. To overcome this limitation, several frameworks have emerged to enable flexible high speed packet processing in software, e.g., NAPI, XDP, or DPDK, or on programmable data planes in hardware, e.g., P4. Despite aiming for a common goal, the design principles of these technologies diverge, which raises the question of their suitability for critical security-related network functions, such as firewalls. In this work, we implement a stateful firewall, which is capable of tracking TCP state and sequence numbers, for each of the four aforementioned high speed packet processing technologies and make the firewall modules publicly available. We integrate multithreading strategies, where applicable, and discuss the impact of each packet processing technology during the development process. Finally, we evaluate and compare their performance in terms of throughput in two scenarios following the guidelines of RFC3511 in a 100 Gbps testbed.

Modern services like video conferencing, video streaming or cloud gaming, but also technologies like the Internet of Things, are all relying on the availability of reliable, high speed network connectivity. The resulting requirements imposed on the underlying network infrastructure are highly complex, leading to the emergence of network softwarization with its instances Software-Defined Networking (SDN) and Network Function Virtualization (NFV). In this work, we develop mechanisms to assess the performance of software-based network components and architectures by means of measurements, simulation and numerical analysis. We develop tools and concepts to monitor, evaluate and optimize the performance of both single network components and complex interconnected systems.

Video streaming generates most network traffic in today’s Internet. For that reason, video on demand streaming is researched heavily in recent years, and many traffic monitoring mechanisms, flow and stream models, and models to predict the user perceived quality are well established. However, the quickly growing live streaming sector is not considered in these Quality of Experience models and not even the relation between network traffic and playback quality has been studied so far, forming a gap in literature. For that reason, we investigate Twitch.tv streaming as one of the largest live streaming platforms based on a large dataset and investigate the possibility to predict live streaming quality based on uplink request information. We apply approaches that are well studied for on demand streaming to predict quality changes, playback quality, and video interruption events as the most important metrics impairing user perceived quality. In this context, we answer whether these models are suitable for live streaming, if small changes are sufficient for satisfactory prediction results, or if fundamental changes and new models are required.

Today, video streaming is responsible for about 50 % of all Internet traffic worldwide. To cope with this massive amount of video streaming data, a major concern of network providers is the development of efficient traffic monitoring and management techniques. However, fast and efficient monitoring which leads to intelligent management decisions is becoming highly resource intense and complex, due to the steady increase of the number of streamed videos and the quality of the streamed content. Considering HTTP adaptive streaming applications, we present a simple machine learning free, uplink request based approach to estimate drops in the video playback butter. These drops are the first indicator leading to quality impairment events like downwards quality changes or stalling. With our approach, instead of analyzing thousands of encrypted packets in the network, we only need to consider one single packet every 5 s-10s on average, depending on the video chunk size and independently of the played resolution. Nevertheless, we are able to detect nearly all stalling events or consider a trade-off between stalling detection recall and false positives. Our approach can be implemented completely moving average based, thus not requiring any parameter setup or other expert knowledge. Due to its simplicity, it can be deployed on any access point to collect streaming quality information that is useful for active network management and intelligent resource provisioning but also in a data center to analyze a massive number of parallel video flows.

This paper uses a crowdsourced dataset of online video streaming sessions to investigate opportunities to reduce the power consumption while considering QoE. For this, we base our work on prior studies which model both the end-user's QoE and the end-user device's power consumption with the help of high-level video features such as the bitrate, the frame rate, and the resolution. On top of existing research, which focused on reducing the power consumption at the same QoE optimizing video parameters, we investigate potential power savings by other means such as using a different playback device, a different codec, or a predefined maximum quality level. We find that based on the power consumption of the streaming sessions from the crowdsourcing dataset, devices could save more than 55% of power if all participants adhere to low-power settings.

The notion of experiment precision quantifies the variance of user ratings in a subjective experiment. Although there exist measures that assess subjective experiment precision, to the best of our knowledge, there is no systematic framework in the Multimedia Quality Assessment (MQA) field for comparing subjective experiments in terms of their precision. Therefore, the main idea of this paper is to propose a framework for comparing subjective experiments in the field of MQA based on appropriate experiment precision measures. We present three experiment precision measures and three related experiment precision comparison methods. We analyze the performance of the measures by using data from real-world Quality of Experience (QoE) subjective experiments. We believe our experiment precision assessment framework will help compare different subjective experiment methodologies. For example, it may help decide which methodology results in more precise user ratings. This may potentially inform future standardization activities.

Data-driven QoE modelling using Machine Learning (ML) allows to reduce the modelling bias and to continuously integrate new QoE results into the QoE model, which can improve its generalizability. The downside is that the majority of ML models are black-box models, which prevent to obtain insights about QoE influence factors and their fundamental relationships that are highly relevant for researchers and providers of services and networks. However, recent advances in the field of eX-plainable Artificial Intelligence (XAI) resolve these issues. Thus, XAI allows to benefit from data-driven QoE modelling to obtain generalizable QoE models, and at the same time to understand what QoE factors are relevant and how they affect the QoE score. In this work, we showcase the feasibility of explainable data-driven QoE modelling for video streaming, since video streaming QoE has been well researched, and thus, allows us to validate our results. Finally, we discuss opportunities and challenges of deploying XAI for QoE modelling.

The rapid growth of connected devices has led to the implementation of Internet of Things (IoT)-centric platforms designed to provide connectivity for machine-to-machine-type communication. Specifically, mobile virtual network operators (MVNOs) provide global service for IoT use cases by leveraging roaming in readily deployed physical networks. This global deployment of connected devices poses a significant challenge regarding the operation of centralized core networks with respect to dimensioning, scaling, as well as system survivability in case of signaling incidents. Overload control mechanisms for IoT mobile networks offer a proactive solution for mitigating excessive signaling traffic from IoT devices in mobile core networks. However, global scaling and a heterogeneous composition of devices present network operators with additional challenges. To address these challenges, this work presents a detailed, protocol-level simulation framework of a real-world IoT MVNO core network. We present both models for the expected IoT signaling load and the processing of signaling messages based on measurements in a live, production environment as well as a dedicated testbed. Finally, we present a case study on various overload mechanisms and identify critical performance characteristics to compare their performance. The results of this study categorize overload control mechanisms and shed light on the necessity for MVNOs to deal with the upcoming IoT traffic by defining appropriate overload control mechanisms in mobile core networks to ensure network survivability under unforeseen conditions.

Wide Area Network (WAN) research benefits from the availability of realistic network topologies, e.g., as input to simulations, emulators, or testbeds. With the rise of Machine Learning (ML) and particularly Deep Learning (DL) methods, this demand for topologies, which can be used as training data, is greater than ever. However, public datasets are limited, thus, it is promising to generate synthetic graphs with realistic properties based on real topologies for the augmentation of existing data sets. As the generation of synthetic graphs has been in the focus of researchers of various applications fields since several decades, we have a variety of traditional model-dependent and model-independent graph generators at hand, as well as DL-based approaches, such as Generative Adversarial Networks (GANs). In this work, we adapt and evaluate these existing generators for the WAN use case, i.e., for generating synthetic WANs with realistic geographical distances between nodes. Moreover, we investigate a hierarchical graph synthesis approach, which divides the synthesis into local clusters. Finally, we compare the similarity of synthetic and real WAN topologies and discuss the suitability of the generators for data augmentation in the WAN use case.

Applications on mobile devices face varying network conditions in cellular networks. The connected radio cell is often changing, especially with moving devices. Different access technologies, varying signal strengths, or distance to the connected radio tower influence the Quality of Service (QoS) of mobile applications. Existing technologies like buffering or adaptive video streaming work reactive, i.e., they react to a decreasing download bitrate. In contrast, these technologies and mobile applications in general could benefit from early knowledge of the expected connection quality.This work investigates the predictability of QoS metrics in cellular networks based on the experience of previous measurements. For this, we developed an Android app to measure download bitrates with minimal data consumption. We performed over 90 000 measurements using a single network operator and analyzed how precise QoS indicators like packet round trip times and download bitrates can be predicted. We developed a methodology to predict the expected download bitrate along a route and present our approach of aggregating measurements into hexagons of dynamic size. The core contributions of this work are (i) a methodology and implementation of systematic measurement data collection, (ii) an open data publication of our measurement data set, and (iii) an approach for predicting QoS metrics in cellular networks based on aggregated measurements. Our results show, that our approach is able to predict the downlink bitrate, the packet round trip time (ping), or DNS query duration along a given route.

The recent rise of the Internet of Things (IoT) has given way to numerous challenges and research questions. One of the most critical issues in the area of low powered devices is the question of energy efficiency. Here, technologies like LoRa or Zigbee emerged, promising low power consumption while maintaining adequate performance. However, even when using these tailor made technologies, several configuration aspects need to be taken into account to provide high performance, energy efficient operation. To this end, we propose a generic model to compute the energy efficiency of wireless sensors under the assumption of perfect CSMA/CA channel access. We present numerical results for a typical LoRa device and highlight extensions towards other channel access mechanisms. Finally, we apply Kleinrock’s power metric to obtain ideal system configurations for varying load parameters.

With increasing digitization and the emergence of the Internet of Things, more and more devices communicate with each other, resulting in a drastic growth of communication networks. Consequently, managing these networks, too, becomes harder and harder. Thus, Software-defined Networking (SDN) is employed, simplifying the management and configuration of networks by introducing a central controlling entity, which makes the network programmable via software and ultimately more flexible. As the SDN controller may impose scalability and elasticity issues, distributed controller architectures are utilized to combat this potential performance bottleneck. However, these distributed architectures introduce the need for constant synchronization to keep a centralized network view, and controller instances need to be placed in appropriate locations. As a result, thoroughly designing SDN-enabled networks with respect to a multitude of performance metrics, e. g., latency and induced traffic, is a challenging task. To assist in this process, we train a performance prediction model based on properties which are available during the network planning phase. We utilize a simulation-based approach for data collection to cover a large parameter space, simulating a variety of networks and controller placements for two opposing SDN architectures. On basis of this dataset, we apply Machine Learning (ML) to solve the performance prediction as a regression problem.

Future Internet of Things deployments generate a multitude of new challenges and opportunities. While 5G networks promise massive throughput rates with ultra low delays, many verticals in the areas of Industry 4.0, Smart City, or Smart Agriculture only transmit tiny amounts of data and instead demand high energy efficiency. This is addressed by access technologies like Low Power Wide Area Networks being complementary to high performance 5G networks. Especially Long Range Wide Area Network (LoRaWAN) promises transmissions across long distances with low energy requirements with the drawback of unreliable transmission due to potential message collisions through random channel access. For that reason, a broad parameter study simulation for LoRaWAN channel access with slotted Aloha is presented for real world scenarios and influences like clock drifts or cross traffic. The key impact factors of this channel access approach are studied with focus on the collision probability in various scenarios and simulation results are compared to the current state of the art. The contribution of this work are guidelines for parameter settings in a LoRaWAN with slotted Aloha channel access and performance comparisons for different settings. This information is crucial to scale and operate future LoRaWANs.

Internet of Things (IoT) and Machine-to-Machine (M2M) devices have seen a significant growth in usage and deployment over the last years. Gaining insight into device and data usage behavior exhibited by the participants of mobile networks is elementary for Mobile Network Operator (MNO) and Mobile Virtual Network Operator (MVNO) to scale their networks and provide a reliable service. This work aims to make use of its first of a kind dataset, spanning multiple countries and MNOs, to provide a detailed characterization of GPRS Tunneling Protocol (GTP) tunnels and devices. To this end, general statistics are used to describe the observed data traffic focusing on the distribution of the tunnel duration and volume as well as periodic device behavior. An approximate metric is introduced to analyze the periodicity and synchronicity of IoT devices. Lastly, we publish the data investigated in this work and provide a large scale dataset on the data usage behavior of IoT devices to interested researchers.

The development and adoption speed of new Internet of Things (IoT) devices and applications is constantly increasing. In particular in mobile networks, however, constant connectivity for all newly developed, globally spread, and occasionally geographically mobile devices is challenging. While the main business of current network operators is in particular, not to roam these new device generation between different networks, whereas new so called Mobile Virtual Network Operators (MVNOs) arise with exactly this business case. They use already available Radio Access Networks and deploy SIM cards that are able to establish data connection everywhere on the basis of international roaming agreements. However, because of the complex interconnection of different systems, additional challenges arise. Outages in one network component might impact the whole system.In this work, we study these signaling anomalies called incidents based on a dataset received from a global MVNO. The goal of this work is to determine traffic characteristics during these incidents towards a better understanding of signaling traffic in IoT networks. Thus, this work can be seen as a fundamental basis to help operators in a characterization, and in particular prediction and avoidance of signaling incidents.

This work presents a novel concept for learning embeddings from encrypted network traffic. In contrast to existing approaches, we evaluate the feasibility of hierarchical embeddings by iteratively aggregating packet embeddings to flow embeddings, and flow embeddings to trace embeddings. The hierarchical embedding concept was designed to especially consider complex dependencies of Internet traffic on different time scales. We describe this novel embedding concept for the domain of network traffic in full detail, and evaluate its performance for the downstream task of website fingerprinting, i.e., identifying websites from encrypted traffic, which is relevant for network management, e.g., as a prerequisite for QoE monitoring or for intrusion detection. Our evaluation reveals that embeddings are a promising solution for website fingerprinting as our model correctly labels up to 99.8% of traces from 500 target websites.

Streaming has become the dominant source of media consumption, which not only applies to the widely researched field of video streaming, but also to music streaming. Here, previous studies so far have only researched the impact of streaming aspects, such as stalling events or initial loading times, on the QoE of music streaming. However, when using a music streaming application, users are already facing waiting times along the click path before they can start the actual streaming. These waiting times are caused by browsing delays, e.g., during searching for songs or scrolling through playlists, and can potentially deteriorate the QoE of the music streaming application. In this work, we conduct an online QoE study to quantify the impact of these browsing delays with the support of an emulated mobile music streaming web app. We found that browsing delays have no impact on the music streaming QoE, which shows that users are able to clearly distinguish between the two main functionalities of such apps, namely, browsing and streaming. However, browsing delays significantly reduce the QoE of the entire music streaming application, to a similar extent as if QoE degradations happen during the actual streaming. This shows that both browsing and streaming are equally important and have to be considered when designing music streaming annlications.

Providing sophisticated web Quality of Experience (QoE) has become paramount for web service providers and network operators alike. Due to advances in web technologies (HTML5, responsive design, etc.), traditional web QoE models focusing mainly on loading times have to be refined and improved. In this work, we relate Google's Core Web Vitals, a set of metrics for improving user experience, to the loading time aspects of web QoE. To this end, we first perform objective measurements in the web using Google's Lighthouse. To close the gap between metrics and experience, we complement these objective measurements with subjective assessment by performing multiple crowdsourcing QoE studies. In these studies, we use CWeQS, a customized framework to emulate the entire web page loading process, and ask users for their experience while controlling the Core Web Vitals. Our results suggest that the Core Web Vitals have less predictive value for web QoE than expected and that page loading times remain the main influence factor in this context.

LoRaWAN is one of the most promising Internet of Things technologies with regard to low energy consumption. However, the currently used random channel access has much potential for improvement. Thus, current literature studies alter-native channel access approaches, but the energy consumption is often not taken into consideration. For that reason, we present a generic model to quantify energy consumption in LoRaWAN for different channel access mechanisms based on a state machine. With our model, we can describe the energy consumption for specific access mechanisms or for the complete network. Our model shows that random access only performs best if no additional receive windows are opened. A simple improvement is Listen before Talk. For networks with high load, improvements are achieved by a more complex scheduled MAC. Our model serves as a basis for future energy consumption studies, conducted through measurements or simulations.

Long Range Wide Area Network (LoRaWAN) is one of the fastest growing Internet of Things (IoT) access network solutions. One major challenge in current LoRaWAN planning is message collision due to the random channel access. To study the collision behavior in LoRaWAN, we present LoRaPlan in this demonstration. LoRaPlan is a software to evaluate gateway placement decisions by studying the collision probability. Therefore, sensor and gateway locations can be imported to create a LoRaWAN. Furthermore, existing networks can be extended by additional gateways. Based on this setup, network coverage, network quality with regard to the number of sensors a single gateway has to manage, and also transmission quality and collision probabilities can be studied. In this demonstration, first the process of network creation with gateway and sensor import and additional manual gateway placement in LoRaPlan is presented. Different adjustable parameters are shown and the influence on network coverage and collision probability is discussed. At the end of this demonstration, it is possible for the audience to place their own gateways and evaluate own placements by means of collision probability and the coverage.

Web browsing has become the most important application of the Internet for the end user. When it comes to mobile devices, web services are mainly accessed through apps. This paper tackles the problem of Web Quality of Experience (QoE) in mobile devices, with a specific focus on apps QoE monitoring and analysis, using in-network (encrypted) traffic measurements. Measuring apps QoE is complex, not only from an instrumentation point of view, but also from the heterogeneity of user interactions which might realize substantially different user experience. To this end, we conduct a feasibility study on four specific and popular Android apps and their corresponding web services. Our test automation framework emulates and measures different user interactions commonly executed during an app session, including the app startup, clicking, scrolling, and searching. The resulting traffic is characterized on different dimensions, and machine learning models are trained to identify web services, apps, and user interactions, and to infer their QoE. The proposed models can correctly identify the specific web service and app in 86% of the cases and accurately estimate the associated QoE with small errors. Our preliminary study represents a first step towards an in-network, web QoE monitoring solution for mobile-device apps.

Today's communication networks process an increasing amount of traffic, while simultaneously providing services to a larger and more diverse quantity of devices. This enhances the complexity of the network and imposes a larger attack space, impacting network management and security efforts. Deployed hardware middle-boxes, like firewalls and Intrusion Detection Systems (IDSs) often lack the flexibility to adapt to this dynamic environment, which Network Function Virtualization (NFV) addresses by implementing these services in software. Yet, this may impose a bottleneck, due to the absence of hardware acceleration. To mitigate this drawback, the functionality can be offloaded to programmable hardware, using P4. In this work we implement an IDS, capable of operating in core and backbone networks up to 100Gbps. This is achieved by using the hardware acceleration of P4-enabled Intel©Tofino™ switches for high performance metadata extraction, in order to train an ML-based detection engine. The system is evaluated regarding its throughput and obtainable aggregation levels as well as its accuracy for detecting a variety of network attacks.

In industrial applications continuous wireless connectivity of mobile clients can rarely by guaranteed. Lack of communication negatively impacts the performance of industrial automation systems, e.g. Automated Guided Vehicle (AGV) fleets. Utilizing industrial Mobile Ad-hoc NETworks (MANETs) and adaptive positioning systems can reduce the number of disconnections in these AGV fleets. Therefore the performance of the mobile systems (e.g. AGV fleet) is improved and factory efficiency increased.

Motivated by new use cases such as industrial automation and in-vehicle communication, Time-Sensitive Networking is further improving the layer 2 standards for real-time communication in Ethernet networks. As periodic traffic is a common requirement in such scenarios, the Time Aware Shaper (TAS) is a highly popular mechanism that allows the realization of real-time guarantees in synchronized networks. Most applications in literature suggest to configure the TAS for full stream isolation such that the lowest delays can be achieved. However, this “zero-queuing” strategy requires a high amount of computational resources and time, as the configuration of the gate control lists of each port is a complex task, which is not suitable for scenarios with dynamically changing requirements. This paper presents a novel approach with shared time windows. In combination with stream reservation techniques that are commonly applied for asynchronous mechanisms, the network is able to accept new streams during operation without complex reconfiguration, while still benefiting from some degree of isolation compared to conventional asynchronous shapers. The evaluation shows that, given the right circumstances, the number of accepted real-time streams can be improved significantly.

Crowdsourcing offers a faster, cheaper, and more scalable approach than the traditional laboratory quality assessment tests. However, participants perform the test in their own working environment, using their own hardware and without direct supervision of a test moderator, leading to different types of biases on the ratings. In this paper, we compare several reliability metrics that are commonly applied to the subjective ratings in terms of their sensitivity to identify typical issues of crowdsourced media quality tests. Following the subject bias theory, we simulate the ratings of different user groups with different bias and various magnitudes of uncertainty, while also considering the presence of unreliable raters. We apply traditional reliability metrics on the ratings and compare their sensitivity in identifying the severity of the raters' biases and uncertainties. Our results show that the average Spearman's rank correlation coefficient between raters can serve as a strong indicator for issues with the crowdsourcing study. This means that scoring too low for this metric should encourage researchers to revisit their study design in order to eventually improve the reliability of results from crowdsourcing-based quality studies.

Crowdsourced measurements solve the problem of being able to assess the performance of a communication network from an end-user perspective, but the new characteristics of the data pose new challenges for QoE modeling. In contrast to existing laboratory or network measurements, this type of measurement at the end user device primarily involves taking a large number of short sample measurements, which, however, are rich in measured parameters, including many user-, application-, and device-related parameters. To test the applicability and to facilitate the integration of such data, we applied four QoE models from the literature to 290k worldwide video streaming measurements from a commercial data set from August to October 2020. In this work, we will therefore first describe the crowdsourcing video streaming data set to provide insights into the properties of video streaming KPIs in the real world. Second, we run four popular QoE models using this data set, compare the resulting QoE scores, and derive the impact of individual KPIs for each model. We show that the models assess the QoE at least differently, but sometimes with contradicting statements. Reading this paper, it becomes evident that more work and subjective studies, based on real-world data like the one we have shown, are needed to extend the current QoE models.

The adaptation of Internet of Things in our everyday life shows different new demands and challenges. One of the fastest growing technologies in this context are Low Power Wide Area Networks with LoRaWAN as one of their most prominent representatives. The promise of this technology is to transmit sensor data over large distances with very little energy consumption. But transmission behavior, and especially overhead and collision probability, must be studied in detail to improve transmission quality and energy consumption due to the random access nature of LoRaWAN. Because of that, this work investigates the LoRaWAN channel capacity by analyzing the transmission overhead, the collision probability, and the data loss. At the end, an energy consumption comparison is done. The contribution is a novel approach based on aggregation and retransmission that shows a decreased data loss of up to 20% compared to the currently used random channel access.

The implementation of Internet-of-Things (IoT) applications faces several challenges in practice, such as compliance with Quality-of-Service requirements, resource constraints, and energy consumption. In this context, the joint edge–cloud paradigm for IoT applications can resolve some of the issues arising in pure cloud computing scenarios, such as those related to latency, energy, or privacy. Therefore, an edge–cloud environment could be promising for resource and energy-efficient IoT applications that implement virtual network functions (VNFs) bound together into service function chains (SFCs). However, a resource and energy-efficient SFC placement requires smart SFC embedding mechanisms in the edge–cloud environment, as several challenges arise, such as IoT service chain modeling and evaluation, the tradeoff between resource allocation, energy efficiency and performance, and the resource dynamics. In this article, we address issues in modeling resource and energy utilization for IoT applications in edge–cloud environments. A smart traffic monitoring IP camera system is deployed as a use case for a realistic modeling of a service chain. The system is implemented in our testbed, which is designed and developed specifically to model and investigate the resource and energy utilization of SFC embedding strategies. A resource and energy-aware SFC strategy in the edge–cloud environment for IoT applications is then proposed. Our algorithm is able to cope with dynamic load and resource situations emerging from dynamic SFC requests. The strategy is evaluated systematically in terms of the acceptance ratio of SFC requests, resource efficiency and utilization, power consumption, and VNF migrations depending on the offered system load. Results show that our strategy outperforms some existing approaches in terms of resource and energy efficiency, thus it overcomes the relevant challenges from practice and meets the demands of IoT applications.

HTTP Adaptive Streaming has become the de facto choice for multimedia delivery. However, the quality of adaptive video streaming may fluctuate strongly during a session due to throughput fluctuations. So, it is important to evaluate the quality of a streaming session over time. In this article, we propose a model to estimate the cumulative quality for HTTP Adaptive Streaming. In the model, a sliding window of video segments is employed as the basic building block. Through statistical analysis using a subjective dataset, we identify four important components of the cumulative quality model, namely the minimum window quality, the last window quality, the maximum window quality, and the average window quality. Experiment results show that the proposed model achieves high prediction performance and outperforms related quality models. In addition, another advantage of the proposed model is its simplicity and effectiveness for deployment in real-time estimation. Our subjective dataset as well as the source code of the proposed model have been made publicly available at https://sites.google.com/site/huyenthithanhtran1191/cqmdatabase .

In recent years Software-defined Networking (SDN) has gained increased popularity by reducing the complexity of management operations and increasing the performance of net-works. As a result, the number of purchasable SDN devices and their deployment in real world network constantly rises, making a thorough understanding of their behaviors and interactions even more important. In this work we analyze distributed controller architectures via simulation, identify Key Performance Indicators (KPIs), classify various network topologies with respect to their impact on SDN, as well as create a prediction model to estimate the overall performance of the overall SDN ecosystem, which can be used for network planning.

Web browsing is one of the key applications of the Internet, if not the most important one. We address the problem of Web Quality-of-Experience (QoE) monitoring from the ISP perspective, relying on in-network, passive measurements. As a proxy to Web QoE, we focus on the analysis of the well-known SpeedIndex (SI) metric. Given the lack of application-level-data visibility introduced by the wide adoption of end-to-end encryption, we resort to machine-learning models to infer the SI and the QoE level of individual web-page loading sessions, using as input only packet- and flow-level data. In this paper, we study the impact of different end-user device types (e.g., smartphone, desktop, tablet) on the performance of such models. Empirical evaluations on a large, multi-device, heterogeneous corpus of Web-QoE measurements for the most popular websites demonstrate that the proposed solution can infer the SI as well as estimate QoE ranges with high accuracy, using either packet-level or flow-level measurements. In addition, we show that the device type adds a strong bias to the feasibility of these Web-QoE models, putting into question the applicability of previously conceived approaches on single-device measurements. To improve the state of the art, we conceive cross-device generalizable models operating at both packet and flow levels, offering a feasible solution for Web-QoE monitoring in operational, multi-device networks. To the best of our knowledge, this is the first study tackling the analysis of Web QoE from encrypted network traffic in multi-device scenarios.

In the context of QoE management, network and service providers commonly rely on models that map system QoS conditions (e.g., system response time, paket loss, etc.) to estimated end user QoE values. Observable QoS conditions in the system may be assumed to follow a certain distribution, meaning that different end users will experience different conditions. On the other hand, drawing from the results of subjective user studies, we know that user diversity leads to distributions of user scores for any given test conditions (in this case referring to the QoS parameters of interest). Our previous studies have shown that to correctly derive various QoE metrics (e.g., Mean Opinion Score (MOS), quantiles, probability of users rating “good or better”, etc.) in a system under given conditions, there is a need to consider rating distributions obtained from user studies, which are often times not available. In this paper we extend these findings to show how to approximate user rating distributions given a QoS-to-MOS mapping function and second order statistics. Such a user rating distribution may then be combined with a QoS distribution observed in a system to finally derive corresponding distributions of QoE scores. We provide two examples to illustrate this process: 1) analytical results using a Web QoE model relating waiting times to QoE, and 2) numerical results using measurements relating packet losses to video stall pattern, which are in turn mapped to QoE estimates.

Today’s traffic projections speak of almost 58% video traffic across the Internet. Nearly all video traffic is encrypted, accounting for more than 50% encrypted traffic worldwide. To analyze video traffic today, or even estimate its quality in the network, a deep look into the traffic characteristics has to be done. But then, important quality metrics from the traffic behavior can be derived. Based on extensive measurements we show in this work how to measure and estimate video stalls for mobile adaptive streaming. The underlying dataset includes more than 900 hours of video footage from the native YouTube app, measured over 18 different videos in 56 network scenarios in two cities in Europe. We outline a possible approach to estimate the video playback buffer size based on uplink video chunk requests in real-time to break down the video stalls. This work is intended as a tool for network operators to receive further knowledge of the characteristics of video streaming traffic to quantify the most important QoE degradation factors of one of the most important applications today.

In recent years, an increasing number of Internet-based applications have been released that use virtual reality for education, training, gaming, and various forms of entertainment. When transmitting an omnidirectional 360° video over the Inter-net, it consumes considerably more data compared to traditional video streaming. To overcome the high network requirements and still reach a high Quality of Experience, HTTP adaptive streaming technology is considered for 360° video streaming. In contrast to traditional streaming, the adaptation logic considers not only the current network conditions, but also the viewport of the user, i.e., which part of the 360° sphere the user is currently focusing on. However, as the viewport of the user might change anytime, the adaptation logic is required to accurately predict the viewport in the next seconds of the playback to allow for an efficient and smooth streaming with a high visual quality.In this paper, we present novel linear programs that determine the optimal visual quality, which is reachable in a given network scenario using different approaches for viewport prediction. Our results are an important contribution for designing adaptation logics for 360° video streaming, which allow for efficient data transmission in the network while reaching a high QoE in VR applications.

In most companies, enterprise applications, such as office products or databases, are heavily used by employees during work hours. Impairments and performance issues not only slow down business processes, but might also increase the frustration of the workforce. While Quality of Experience (QoE) has been widely studied for personal multimedia applications, such as video streaming, its application to the business usage domain is still in its infancy. Due to several reasons, e.g., the high complexity of IT infrastructure, classical QoE studies can hardly be transferred to business applications. These studies are often independent from the context of usage and actively poll ratings from their participants. This work contrasts the commonly used “pull” method for collecting user ratings with a self-motivated “push” approach. This approach is inspired by complaint systems, in which users can directly report problems with a technical system as soon as they notice them. Therefore, performance assessments of a business application from employees of a cooperating company are collected with both rating systems during a time span of 1.5 years. Besides the analysis of the interaction of users with the “push” system, differences between the two methods are discussed. Further, QoE models for the monitored business application are derived based on the self-motivated “push” ratings.

Today's software offers diverse functionalities that need to be made accessible to humans in an easy to understand and quick to learn way. This is not a new challenge and already well addressed in usability research. Yet, similar challenges arise in crowdsourcing tasks. While task interfaces are often less complicated than complete software products, crowdsourcing workers have only a minimal amount of time to familiarize them with the task interface. Additionally, due to the repetitiveness of the tasks, workers are required to work with this interface to solve a few hundred tasks. Unfortunately, often little attention is paid to optimize the task interfaces. Even if recent studies have shown evidence of the negative effects of poorly designed interfaces on the work performance, there is less knowledge about the importance of the usability from the worker's point of view and their personal preferences. This work aims to fill this gap in two steps. First, we analyze the relevance of good usability and interface design in relation to other task properties like payment or joyfulness by conducting a survey on two popular crowdsourcing platforms. Second, we identify interface properties that are of importance for the crowdsourcing workers and discuss both consenting and dissenting opinions of different worker groups. The results show that all workers place an essential role in the usability of a task interface when selecting tasks, but do not agree on coherent design preferences within and between the platforms.

The subjective quality of transmitted speech is traditionally assessed in a controlled laboratory environment according to ITU-T Rec. P.800. In turn, with crowdsourcing, crowdworkers participate in a subjective online experiment using their own listening device, and in their own working environment. Despite such less controllable conditions, the increased use of crowdsourcing micro-task platforms for quality assessment tasks has pushed a high demand for standardized methods, resulting in ITU-T Rec. P.808. This work investigates the impact of the number of judgments on the reliability and the validity of quality ratings collected through crowdsourcing-based speech quality assessments, as an input to ITU-T Rec. P.808 . Three crowdsourcing experiments on different platforms were conducted to evaluate the overall quality of three different speech datasets, using the Absolute Category Rating procedure. For each dataset, the Mean Opinion Scores (MOS) are calculated using differing numbers of crowdsourcing judgements. Then the results are compared to MOS values collected in a standard laboratory experiment, to assess the validity of crowdsourcing approach as a function of number of votes. In addition, the reliability of the average scores is analyzed by checking inter-rater reliability, gain in certainty, and the confidence of the MOS. The results provide a suggestion on the required number of votes per condition, and allow to model its impact on validity and reliability.

Streaming made a lasting effect on the way our society consumes media in the last decade. While due to streaming the way we listen to music and podcasts has changed drastically, there are very few studies about its Quality of Experience (QoE) and possible influence factors. From video QoE studies, we know that, for example, undesirable stops of the stream (stalling events) have a significant impact on QoE. However, the way in which music and video streaming is consumed differs significantly, as music is often played in the background, and thus, the influence of stalling could be significantly different. Thus, this work evaluates the impact of stalling on music streaming QoE. Therefore, we conduct two crowdsourced user studies: In the first study, users have to rate four songs with different stalling patterns and evaluate the degree of impairments. Afterwards, we compare the ratings to the results of a lab study and show that they are highly correlated, and that crowdsourcing is a suitable way of measuring music streaming QoE. In addition, we conduct a second crowdsourcing study to investigate the influence of the user's attentiveness on QoE. Here, participants have to listen to one song with two stalling events, while one half of them had to transcribe a handwritten text with music playing in the background. The attentiveness shows no influence on the perceived streaming quality, but it shows a significant influence on the perceived quality degradation due to stalling events. Furthermore, considerably more stalling events were missed for workers who focused on the transcription. These results are an important step towards establishing new methods for investigating QoE in multimedia.

Modern photogrammetric methods as well as laser measurement systems make it easy to collect large 3D point clouds that sample objects or environments. As the recorded point clouds can be used to render computer-generated images and models, they are of particular interest in the domains of geographical and architectural engineering, as well as for computer graphics (e.g., games or virtual reality). However, point clouds have a huge storage demand, thus, point clouds shall be reduced by removing some of the points. This will inevitably also reduce the Quality of Experience (QoE) of media, which is rendered from the reduced point clouds. In this work, the impact of two different reduction methods on the QoE of rendered images is investigated from two point of views, i.e., based on ratings from both naive crowdworkers as well as point cloud experts.

When conducting video QoE studies participants are usually asked to rate the QoE of prepared test videos. However, participants are given no choice to select content, which they like or in which they are interested. This may cause annoyance or frustration when conducting the QoE study, which eventually might affect the QoE results of the study. The consequent question is whether the content liking has a direct impact on the submitted ratings by the participants and whether the freedom of choosing the video content in QoE studies results in better ratings. To investigate this research question, CroQoE, an existing framework for crowdsourced video testing, is extended and used in a pilot field study. In this work, the results of a QoE study with individual and dynamic content selection are compared to a QoE study with pre-selected contents. Moreover, this work includes a comparison to a previous QoE study for validation. As the previous study was conducted on desktop PCs, the CroQoE study further allows to identify differences in the stalling perception between studies on desktop PCs and mobile devices.

Responding fluently to user requests is important to keep them immersed. In this paper, we are presenting an extensive dataset to study the response time of YouTube's mobile video streaming service on Android. We illustrate the application of our dataset by studying YouTube's initial delay for a subset of 9 videos in 75 network scenarios. We find that in 41% of the cases, YouTube exceeds the attention span of a typical user, while deep immersion is only reached in 15% of the cases. Our factor analysis implies that the allocation of the initial CDN node is the critical link in this delay chain. Since our dataset includes a large variety of factors, we are describing setup, methodology, and data structure in detail. Our dataset and measurement tools are publicly available at [8].

Evaluating the QoE of video streaming and its influence factors has become paramount for streaming providers, as they want to maintain high satisfaction for their customers. In this context, crowdsourced user studies became a valuable tool to evaluate different factors which can affect the perceived user experience on a large scale.In general, we observed that most of these crowdsourcing studies either use an in vivo or an in vitro design. In vivo design means that the study participant has to rate the QoE of a video that is embedded in an application similar to a real streaming service, e.g., YouTube or Netflix. In vitro design refers to a setting, in which the video stream is separated from a specific service and thus, the video plays on a plain background. Although these designs vary widely, the results are often compared and generalized.Therefore, in this work, we investigate the influence of these two study design alternatives on the perceived QoE. In crowdsourced user studies, participants rate the video streaming with respect to different stalling patterns (no stalling, different positions) and study designs (in vivo or in vitro). Contrary to our expectations, the results indicate that there is statistically no significant influence of the study design on the perceived video QoE and acceptance. In addition, we found that the in vivo design does not reduce the test takers' attentiveness.

In the context of subjective user studies conducted to derive relationships between influence factors and QoE, user diversity leads to distributions of user scores for test conditions. Such models are commonly exploited by service/network providers to derive various QoE metrics in their system, such as expected QoE, or the percentage of users rating above a certain threshold. The question arises as to how to combine a) user rating distributions obtained from subjective studies, and b) system performance condition distributions, so as to obtain the actual observed QoE distribution in the system? Moreover, how can various QoE metrics of interest in the system be derived? We prove a fundamental relationship showing that the expected system QoE is equal to the expected Mean Opinion Score (MOS) in the system. While subjective user studies commonly report only QoS-to-MOS mapping functions, we show that to derive additional QoE metrics in the system, it is necessary to use corresponding QoS-to-QoE metric mapping functions (beyond only QoS-to-MOS) as derived from user rating distributions in subjective studies. The results of the paper provide important insights for deriving QoE metrics from a systems perspective.

Mobile video streaming has gained a lot of popularity in recent year with the introduction of large data plans for mobile phones. While users in non-mobile scenarios have become accustomed to high quality and few stalling events, this is not the case in mobile environments. People are more likely to tolerate stalling since they know high throughput coverage cannot always be guaranteed. Their behavior and their engagement are drastically different when watching videos on mobile devices. In this paper, we characterize mobile phone users who use a video streaming application. We present a data set of over 6,000 video views from a crowdsourced video measurement study. We investigate user activity and engagement during video streaming and how such metrics are correlated with each other. This is the first study that goes beyond user engagement and investigates the direct behavior of mobile video users outside the lab which is an important step towards mobile QoE management.

With the widespread use of encryption in Over the Top (OTT) traffic, Internet Service Providers (ISPs) for the most part lack insights into application performance, as well as into Quality of Experience (QoE) perceived by end users. Addressing challenges related to encryption, ISPs are looking into machine learning (ML) based solutions that can detect application performance solely from statistical properties of the traffic. On the other hand, OTT service providers are not willing to share service performance and content-related information with ISPs. While related work on OTT-ISP collaboration scenarios has addressed architectural aspects, business models, and to a certain extent incentives for sharing data, the focus of this paper is on the exchanged data itself. We investigate to what extent the performance of in-network ML-based QoE estimation models for HTTP adaptive video streaming could be improved with the availability of certain context data provided by OTT providers. We motivate OTT-ISP collaboration through more accurate in-network QoE monitoring and potential improvement of user experience, which is of interest to both sides.

As the Internet of Things (IoT) continues to gain traction in telecommunication networks, a very large number of devices are expected to be connected and used in the near future. In order to appropriately plan and dimension the network, as well as the back-end cloud systems and the resulting signaling load, traffic models are employed. These models are designed to accurately capture and predict the properties of IoT traffic in a concise manner. To achieve this, Poisson process approximations, based on the Palm–Khintchine theorem, have often been used in the past. Due to the scale (and the difference in scales in various IoT networks) of the modeled systems, the fidelity of this approximation is crucial, as, in practice, it is very challenging to accurately measure or simulate large-scale IoT deployments. The main goal of this paper is to understand the level of accuracy of the Poisson approximation model. To this end, we first survey both common IoT network properties and network scales as well as traffic types. Second, we explain and discuss the Palm–Khintiche theorem, how it is applied to the problem, and which inaccuracies can occur when using it. Based on this, we derive guidelines as to when a Poisson process can be assumed for aggregated periodic IoT traffic. Finally, we evaluate our approach in the context of an IoT cloud scaler use case.

Crowdworker online communities — operating in fora like mTurkForum and TurkerNation — are an important actor in microwork markets. Albeit central to market dynamics, how the behavior of crowdworker communities and the dynamics of online marketplaces influence each other is yet to be understood. To provide quantitative evidence of such influence, we performed an analysis on 6-years worth of mTurk market activities and community discussions in six fora. We investigated the nature of the relationships that exist between activities in fora, tasks published in mTurk, requesters for such tasks, and task completion speed. We validate -- and expand upon — results from previous work by showing that (i) there are differences between market demand and community activities that are specific to fora and task types; (ii) the temporal progression of HIT availability in the market is predictive of the upcoming amount of crowdworker discussions, with significant differences across fora and discussion categories; (iii) activities in fora can have a significant positive impact on the completion speed of tasks available in the market.

In 2012, Google introduced the Speed Index (SI) metric to quantify the speed of the Web page visual completeness for the actually displayed above-the-fold (ATF) portion of a Web page. In Web browsing a page might appear to the user to be already fully rendered, even though further content may still be retrieved, resulting in the Page Load Time (PLT). This happens due to the browser progressively rendering all objects, part of which can also be located below the browser window's current viewport. The SI metric (and variants) thereof have since established themselves as a de facto standard in Web page and browser testing. While SI is a step in the direction of including the user experience into Web metrics, the actual meaning of the metric and especially its relationship between Speed Index and Web QoE is however far from being clear. The contributions of this paper are thus to first develop an understanding of the SI based on a theoretical analysis and second, to analyze the interdependency between SI and MOS values from an existing public dataset. Specifically, our analysis is based on two well established models that map the user waiting time to a user ACR-rating of the QoE. The analysis show that ATF-based metrics are more appropriate than pure PLT as input to Web QoE models.

QoE has received much attention over the past years and has become a prominent issue for delivering services and applications. A significant amount of research has been devoted to understanding, measuring, and modeling QoE for a variety of media services. In this position paper we provide an overview of state-of-the-art findings and discuss emerging concepts and the challenges they raise with respect to managing QoE for networked media services. We address the implications of this evolution in our understanding of QoE in terms of new approaches in QoE modeling that are necessary for achieving a more comprehensive QoE management paradigm.

The quality of adaptive streaming may fluctuate strongly during a session. Therefore, a challenge is how to evaluate the quality of a session over time. In this study, a cumulative quality model is proposed for HTTP adaptive streaming. The model is based on the concept of a sliding window of video segments over a session. It is found that the minimum window quality, the last window quality, and the average window quality are key components of the cumulative quality model. In addition, the proposed model is applicable to real-time quality monitoring, which is especially important for cost-effective evaluation of streaming technologies and as basis for adaptive quality mechanisms.

The experience of web services depends on multiple factors such as the usability of the service itself as well as influences on Quality of Experience (QoE) like waiting times. Existing studies so far have investigated both experimental factors (usability and QoE) in isolation, thus it is not possible to investigate dependencies between the two. In this study, we manipulated the usability and QoE of a news web page in the same experiment. Participants had to solve 9 tasks (three usability level and three QoE level, all combinations). Data (N=44) from two different sessions with different QoE manipulations were recorded: Simulation of initial page display delay via 1) a complete overlay and 2) a semi-transparent overlay. Our preliminary results show that the usability manipulation resulted in significant effects along multiple dimensions (QoE, aesthetic, and usability). Furthermore, also the implementation of the QoE manipulation had an effect on, e.g., performance parameters. Surprisingly, our results reveal that participants seem to be unable to rate the constructs usability and QoE independently from each other. Even after adding pronounced explanations of the constructs to the introductory phase of the second session of the experiment, the results still show a strong correlation between the two constructs. We thus recommend for similar future experiments to check beforehand whether Quality of Experience and Usability can be reliably judged and distinguished by naive participants.

Quality of experience (QoE) models predict the subjective quality of multimedia based on the relevant quality of service (QoS) factors. Due to the large space of QoS factors and the high costs of conducting subjective tests, efficient sampling strategies are required to determine which QoS configurations are to be queried, that is, evaluated by subjects. In this study, we extend the IQX model proposed by [M. Fiedler, T. Hoßfeld, and P. Tran-Gia, “A generic quantitative relationship between quality of experience and quality of service,”IEEE Netw., vol. 24, no. 2, pp. 36–41, Mar./Apr.2010.] toward a multidimensional QoS–QoE model (MIQX). To explore the complicated interaction between QoS factors more efficiently, we develop active learning algorithms for the multidimensional QoE model. Then, we conduct comprehensive experiments to compare the effectiveness of applying different sampling methods to crowdsourced video quality assessment tasks. In offline experiments that assume annotators give the same scores after changing the querying order, we demonstrate that active learning performs best and that a space-filling algorithm performs significantly better than random sampling. However, when we analyze the performance of the active sampling approaches more deeply using a novel field experiment, we observe that the active learning algorithms, which have been shown to be effective in the offline setting, can fail due to the habituation effect and individual differences of annotators. The active learning methods can also succeed when these issues are mitigated. These findings suggest that simply simulating the sample acquisition order, which is widely adopted in previous active learning literature[2]–[5], is not sufficient for multimedia quality assessment tasks.

editorial Free Access Share on Guest Editorial: Special Issue on “QoE Management for Multimedia Services” Editors: Lea Skorin-Kapov University of Zagreb, Faculty of Electrical Engineering and Computing, Croatia University of Zagreb, Faculty of Electrical Engineering and Computing, CroatiaView Profile , Martín Varela callstats.io, Finland callstats.io, FinlandView Profile , Tobias Hoßfeld Chair of Communication Networks, Computer Science, University of Würzburg, Germany Chair of Communication Networks, Computer Science, University of Würzburg, GermanyView Profile , Kuan-Ta Chen Academia Sinica, Taiwan Academia Sinica, TaiwanView Profile Authors Info & Claims ACM Transactions on Multimedia Computing, Communications, and ApplicationsVolume 14Issue 2sApril 2018 Article No.: 28pp 1–3https://doi.org/10.1145/3192332Published:25 April 2018Publication History 5citation269DownloadsMetricsTotal Citations5Total Downloads269Last 12 Months29Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

Quality of Experience (QoE) has received much attention over the past years and has become a prominent issue for delivering services and applications. A significant amount of research has been devoted to understanding, measuring, and modelling QoE for a variety of media services. The next logical step is to actively exploit that accumulated knowledge to improve and manage the quality of multimedia services, while at the same time ensuring efficient and cost-effective network operations. Moreover, with many different players involved in the end-to-end service delivery chain, identifying the root causes of QoE impairments and finding effective solutions for meeting the end users’ requirements and expectations in terms of service quality is a challenging and complex problem. In this article, we survey state-of-the-art findings and present emerging concepts and challenges related to managing QoE for networked multimedia services. Going beyond a number of previously published survey articles addressing the topic of QoE management, we address QoE management in the context of ongoing developments, such as the move to softwarized networks, the exploitation of big data analytics and machine learning, and the steady rise of new and immersive services (e.g., augmented and virtual reality). We address the implications of such paradigm shifts in terms of new approaches in QoE modeling and the need for novel QoE monitoring and management infrastructures.

Offloading mobile Internet data via WiFi has emerged as an omnipresent trend. WiFi networks are already widely deployed by many private and public institutions (e.g., libraries, cafes, restaurants) but also by commercial services to provide alternative Internet access for their customers and to mitigate the load on mobile networks. Moreover, smart cities start to install WiFi infrastructure for current and future civic services, e.g., based on sensor networks or the Internet of Things. A simple model for the distribution of WiFi hotspots in an urban environment is presented. The hotspot locations are modeled with a uniform distribution of the angle and an exponential distribution of the distance, which is truncated to the city limits. We compare the characteristics of this model in detail to the real distributions. Moreover, we show the applicability and the limitations of this model, and the results suggest that the model can be used in scenarios, which do not require an accurate spatial collocation of the hotspots, such as offloading potential, coverage, or signal strength.

Most Quality of Experience (QoE) studies report only the mean opinion scores (MOS) and existing models typically map Quality of Service (QoS) parameters to the MOS. However, service providers may be interested in the share of users that are not at all satisfied, and their corresponding QoE levels. From the QoE management point of view, the circumstances leading to the QoE levels perceived by a certain percentage of users, e.g. the 10% most annoyed users, are of utmost importance. Proper metrics are the 10%-quantiles of QoE values. Knowledge of those quantiles helps service providers to estimate the need for countermeasures in order to prevent annoyed users from churning on one hand, and to avoid overprovisioning on the other hand. The contribution of this paper is the derivation of quantiles from existing MOS-QoS relations. This allows to reuse existing subjective MOS results and MOS models without rerunning the experiments. We consider exemplary the IQX model (describing the MOS-QoS relation) for the derivation of the quantile-QoS relation. A practical guideline for the computation of the quantiles is provided.

Peer-to-Peer (P2P) systems measurements are still a relevant research topic, since insights in large swarm sizes and churn are not yet available for the BitTorrent network. To improve existing measurement methodology, this work here tackles the aspect of swarm size estimation and complete collection in the BitTorrent network. For this purpose the Coupon Collector Problem is modified and formulated as the BitTorrent Peer Collector (BTPC) Problem. Thus, (a) simulations are used to test simple and maximum likelihood estimation for hidden swarm sizes, (b) an analytical solution to the BTPC problem is presented, and (c) measurements are used to evaluate estimators of the BTPC model. Obtained results show that this estimation works well for classical trackers and that churn constantly influences measurements. Those results show that more peers use the Main-line DHT than a single tracker, however, client implementations challenge those models working well for trackers.

Managing QoE is one of the most interesting direct applications of workable QoE models. Indeed, being able to predict how users perceive the quality of a service allows the service provider(s) to optimize its delivery, based on several possible criteria. It has been argued, however, that the MOS is ill-suited for this type of application, and that different measures — e.g., rating distributions or quantiles — are better suited for the task. In this paper we build on these ideas by adding the notions of QoE fairness (as opposed to QoS fairness) and user diversity, and discuss how the choice of measures used, the importance of fairness, and how the variations between users can affect the optimal QoE management choices for service providers.

Video streaming provided by Over-The-Top (OTT) service providers through a cellular network is a common usage scenario for many people today. While video streaming will work reasonably well in a stationary scenario, several issues arise for mobile users. For instance, travelling through short areas without cellular coverage, such as an automobile tunnel, will often result in quality degradation or video stalling. To combat this, this paper provides an analytical investigation of the video quality degradation problem as it is experienced by mobile users and suggests a context-aware HTTP Adaptive Streaming (HAS) strategy to prevent stalling and minimize the impact on Quality of Experience (QoE). This provides a solution that can completely prevent stalling when appropriate context information (such as positional information from satellite navigation) is present. The collected evaluation results encourage further research on how context-awareness can be exploited to further enhance video service provisioning by OTT service providers.

Adaptive crowdsourcing is a new approach to crowdsourced Quality of Experience (QoE) studies, which aims to improve the certainty of resulting QoE models by adaptively distributing a fixed budget of user ratings to the test conditions. The main idea of the adaptation is to dynamically allocate the next rating to a condition, for which the submitted ratings so far show a low certainty. This paper investigates the effects of statistical adaptation on the distribution of ratings and the goodness of the resulting QoE models. Thereby, it gives methodological advice how to select test conditions for future crowdsourced QoE studies.

HTTP Adaptive Streaming (HAS) adapts the video quality to the current network condition by switching between different quality layers. As HAS was shown to perform better than classical video streaming, it is becoming increasingly popular. Recent research showed that quality switch amplitude and time on layer have an impact on the Quality of Experience (QoE) of HAS. However, those studies focused only on adaptation between two layers so far. This work extends these findings by taking adaptation between three layers into account. Thereby, especially the impact of an intermediate layer on user perceived quality is investigated. Crowdsourcing experiments were conducted in order to collect subjective ratings for adaptation between three layers. The results indicate that the quality of each layer and the time on each layer are important QoE parameters. This encourages the usage of temporal pooling approaches for QoE prediction and QoE-aware traffic management. Therefore, mean pooling of per-frame metrics will be applied and its performance will be validated with the subjective crowdsourcing results.

Today's video streaming platforms offer videos in a variety of quality settings in order to attract as many users as possible. But even though a sufficiently dimensioned network can not always be provided for the best experience, users are asking for high QoE. Users consume the content of a video streaming platform in different ways, while video delivery platforms currently do not account for these scenarios and thus ensure at best mediocre QoE. In this paper, we develop a queuing model and provide a mean-value analysis to investigate the impact of user profiles on the QoE of HTTP Video Streaming for typical user scenarios. Our results show that the user profile and particularly the scenario have to be respected when dimensioning the buffer. Further, we present recommendations on how to adapt player parameters in order to optimize the QoE for individual users profiles and viewing habits. The provided model leads to relevant insights that are required to build a system that guarantees each user the best attainable QoE.

Over the last decade or so, significant research has focused on defining Quality of Experience (QoE) of Multimedia Systems and identifying the key factors that collectively determine it. Some consensus thus exists as to the role of System Factors, Human Factors and Context Factors. In this paper, the notion of context is broadened to include information gleaned from simultaneous out-of-band channels, such as social network trend analytics, that can be used if interpreted in a timely manner, to help further optimise QoE. A case study involving simulation of HTTP adaptive streaming (HAS) and load balancing in a content distribution network (CDN) in a flash crowd scenario is presented with encouraging results.

The popularity of the crowdsourcing for performing various tasks online increased significantly in the past few years. The low cost and flexibility of crowdsourcing, in particular, attracted researchers in the field of subjective multimedia evaluations and Quality of Experience (QoE). Since online assessment of multimedia content is challenging, several dedicated frameworks were created to aid in the designing of the tests, including the support of the testing methodologies like ACR, DCR, and PC, setting up the tasks, training sessions, screening of the subjects, and storage of the resulted data. In this paper, we focus on the web-based frameworks for multimedia quality assessments that support commonly used crowdsourcing platforms such as Amazon Mechanical Turk and Microworkers. We provide a detailed overview of the crowdsourcing frameworks and evaluate them to aid researchers in the field of QoE assessment in the selection of frameworks and crowdsourcing platforms that are adequate for their experiments.

Quality of Experience (QoE) in multimedia applications is closely linked to the end users' perception and therefore its assessment requires subjective user studies in order to evaluate the degree of delight or annoyance as experienced by the users. QoE crowdtesting refers to QoE assessment using crowdsourcing, where anonymous test subjects conduct subjective tests remotely in their preferred environment. The advantages of QoE crowdtesting lie not only in the reduced time and costs for the tests, but also in a large and diverse panel of international, geographically distributed users in realistic user settings. However, conceptual and technical challenges emerge due to the remote test settings. Key issues arising from QoE crowdtesting include the reliability of user ratings, the influence of incentives, payment schemes and the unknown environmental context of the tests on the results. In order to counter these issues, strategies and methods need to be developed, included in the test design, and also implemented in the actual test campaign, while statistical methods are required to identify reliable user ratings and to ensure high data quality. This contribution therefore provides a collection of best practices addressing these issues based on our experience gained in a large set of conducted QoE crowdtesting studies. The focus of this article is in particular on the issue of reliability and we use video quality assessment as an example for the proposed best practices, showing that our recommended two-stage QoE crowdtesting design leads to more reliable results.

Today's Internet services are increasingly accessed from mobile devices, thus being responsible for growing load in mobile networks. At the same time, more and more WiFi routers are deployed such that a dense coverage of WiFi is available. Results from different related works suggest that there is a high potential of reducing load on the mobile networks by offloading data to WiFi networks, thereby improving mobile users' quality of experience (QoE) with Internet services. Additionally, the storage of the router could be used for content caching and delivery close to the end user, which is more energy efficient compared to classical content servers, and saves costs for network operators by reducing traffic between autonomous systems. Going one step beyond, we foresee that merging these approaches and augmenting them with social information from online social networks (OSNs) will result both in even less costs for network operators and increased QoE of end users. Therefore, we propose home router sharing based on trust (HORST) - a socially-aware traffic management solution which targets three popular use cases: data offloading to WiFi, content caching/prefetching, and content delivery.

YouTube, the killer application of today's Internet, is changing the way ISPs and network operators manage quality monitoring and provisioning on their IP networks. YouTube is currently the most consumed Internet application, accounting for more than 30% of the overall Internet's traffic worldwide. Coupling such an overwhelming traffic volume with the ever intensifying competition among ISPs is pushing operators to integrate Quality of Experience (QoE) paradigms into their traffic management systems. The need for automatic QoE assessment solutions becomes even more critical in mobile broadband networks, where over-provisioning solutions can not be foreseen and bad user experience translates into churning clients. This paper presents a complete study on the problem of YouTube Quality of Experience monitoring and assessment in mobile networks. The paper considers not only the QoE analysis, modeling and assessment based on real users' experience, but also the passive monitoring of the quality provided by the ISP to its end-customers in a large mobile broadband network.

Over the last couple of years, the scope of Quality of Experience (QoE) research has been constantly extended, most recently to the field of Web QoE in the context of HTTP-based applications. In this paper, we address the question whether it is sufficient to reduce typical Web QoE assessment scenarios to the temporal aspects of waiting for task completion, which would allow to attribute the resulting logarithmic laws to well-known psychological insights on human time perception. We demonstrate that while this attribution is valid for simple waiting tasks which are typical for simple data services like e.g. file downloads, the case of interactive web browsing is much more complex. We show that this is not only because technical issues prevent bandwidth and download time from being directly correlated with each other in a simple manner, but also because user perceived web page load times strongly deviate from technical page load times. Consequently, existing approaches towards assessment and modeling of web browsing QoE have to be critically reviewed and redesigned.

Peer-to-peer file-sharing systems are responsible for a significant share of the traffic between Internet service providers (ISPs) in the Internet. In order to decrease their peer-to-peer-related transit traffic costs, many ISPs have deployed caches for peer-to-peer traffic in recent years. We consider how the different types of peer-to-peer caches—caches already available on the market and caches expected to become available in the future—can possibly affect the amount of inter-ISP traffic. We develop a fluid model that captures the effects of the caches on the system dynamics of peer-to-peer networks and show that caches can have adverse effects on the system dynamics depending on the system parameters. We combine the fluid model with a simple model of inter-ISP traffic and show that the impact of caches cannot be accurately assessed without considering the effects of the caches on the system dynamics. We identify scenarios when caching actually leads to increased transit traffic. Motivated by our findings, we propose a proximity-aware peer-selection mechanism that avoids the increase of the transit traffic and improves the cache efficiency. We support the analytical results by extensive simulations and experiments with real BitTorrent clients.

This paper addresses the challenge of assessing and modeling Quality of Experience (QoE) for online video services that are based on TCP-streaming. We present a dedicated QoE model for You Tube that takes into account the key influence factors (such as stalling events caused by network bottlenecks) that shape quality perception of this service. As second contribution, we propose a generic subjective QoE assessment methodology for multimedia applications (like online video) that is based on crowd sourcing - a highly cost-efficient, fast and flexible way of conducting user experiments. We demonstrate how our approach successfully leverages the inherent strengths of crowd sourcing while addressing critical aspects such as the reliability of the experimental data obtained. Our results suggest that, crowd sourcing is a highly effective QoE assessment method not only for online video, but also for a wide range of other current and future Internet applications.

Peer-to-peer file-sharing systems are responsible for a significant share of the traffic between Internet service providers (ISPs) in the Internet. In order to decrease their peer-to-peer related transit traffic costs, many ISPs have deployed caches for peer-to-peer traffic in recent years. We consider how the different types of peer-to-peer caches - caches already available on the market and caches expected to become available in the future - can possibly affect the amount of inter-ISP traffic. We develop a fluid model that captures the effects of the caches on the system dynamics of peer-to-peer networks, and show that caches can have adverse effects on the system dynamics depending on the system parameters. We combine the fluid model with a simple model of inter-ISP traffic and show that the impact of caches cannot be accurately assessed without considering the effects of the caches on the system dynamics. We identify scenarios when caching actually leads to increased transit traffic. Our analytical results are supported by extensive simulations and experiments with real BitTorrent clients.

Locality-awareness is considered as a promising approach to increase the efficiency of content distribution by peer-to-peer (P2P) networks, e.g., BitTorrent. It is intended to reduce the inter-domain traffic which is costly for Internet service providers (ISPs) and simultaneously increase the performance from the viewpoint of the P2P users, i.e, shorten download times. This win-win situation should be achieved by a preferred exchange of information between peers which are located closely to each other in the underlying network topology. A set of studies shows that these approaches can lead to a win-win situation under certain conditions, and to a win-no lose situation in most cases. However, the scenarios used assume mostly homogeneous peer distributions and that all peers have the same access speed. This is not the case in practice according to several measurement studies. Therefore, we extend previous work in this paper by studying scenarios with real-life, skewed peer distributions and heterogeneous access bandwidths of peers. We show that even a win-no lose situation is difficult to achieve under those conditions and that the actual impact for a specific peer depends heavily on the used locality-aware peer selection and the concrete scenario. Therefore, we conclude that current proposals need to be refined so that users of P2P networks can be sure that they also benefit from their use. Otherwise, a broad acceptance of the concept of locality-awareness in the user community of P2P networks will not take place.

Locality promotion in P2P content distribution networks is currently a major research topic. One of the goals of all discussed approaches is to reduce the interdomain traffic that causes high costs for ISPs. However, the focus of the work in this field is generally on the type of locality information that is provided to the overlay and on the entities that exchange this information. An aspect that is mostly neglected is how this information is used by the peers. In this paper, we consider the predominant approach of Biased Neighbor Selection and compare it with Biased Unchoking, which is an alternative locality aware peer selection strategy that we propose in this paper. We show that both mechanisms complement each other for the BitTorrent file sharing application and achieve the best performance when combined.

Introducing location-awareness in overlay networks allows for a higher efficiency of the offered services, e.g., shorter search times or higher throughput. This is especially important for business applications supported by overlays. In this paper, we will illustrate how such an increase in efficiency causes congestion in an otherwise well-dimensioned locality-aware overlay using a Distributed Hash Table (DHT). In particular, we will use a Vertical Handover (VHO) support system using a Pastry substrate as an example. We will describe the occuring congestion problem and identify the underlying reasons. Thus, we describe the basic trade-off between efficiency and load-inequality. Furthermore, we introduce a solution for the specific issue observed, and conduct a performance evaluation identifying the major influence factors and optimization potential for future work.

Peers in peer-to-peer file-sharing systems cannot effectively share their files if they are poorly described. Terms one user employs to describe an instance of a file may not be those that are commonly associated with the file, making this instance difficult to locate. To alleviate this problem, a server can ask its peers for help in improving the description of files they have in common. We consider the design of a fully distributed, automatic system for the exchange of descriptive metadata. Experimental results show that the proposed techniques are effective in improving search accuracy with reasonable cost.

One of the most important design goals of current peer-to-peer (P2P) technology is to be able to offer its service to an arbitrary large number of users. Discrete event simulation is often applied to quantitatively and qualitatively evaluate the performance and scalability of such systems before they are deployed. However, the number of users, processes and events which can be simulated is limited by both the central memory and the time available. In this paper we present compact data structures and event design algorithms, which are intended to be a further step towards efficient simulation of large scale P2P systems. In particular, we give guidelines on how to increase the number of peers which can be simulated and show how to find a good tradeoff between computational time and memory consumption in large scale P2P simulation

The ongoing process of globalization leads to a huge demand for highly scalable applications that are able to deal with millions of participants distributed all over the world. Peer-to-peer (P2P) technology enables an arbitrary large number of users to participate in distributed services like content distribution or collaboration tools. In order to verify a new protocol's performance and scalability simulation is a commonly used tool. First, predicting the network and peer behavior in the real world is only feasible if the simulation, i.e. all applied models as well as the peer state, is as realistic as possible. Second, many properties of the system only become observable when the number of participants is sufficiently large. Therefore, verifying the scalability of a system requires simulating huge worldwide networks. Due to limited processing power, central memory and availabe time, both requirements can only be fullfilled if the applied models are very efficient. In this paper we take a closer look at the network layer. We compare the most commonly-used network models and present a very efficient model for applying real-world network transmission times in large scale simulations

The performance of peer-to-peer (P2P) content distribution networks depends highly on the coordination of the peers. Sophisticated cooperation strategies, such as the multiple source download, are the foundation for efficient file exchange. The detailed performance of the strategies are determined by the peer characteristics and the peer behaviour, such as the number of parallel upload connections, the selfishness, or the altruistic re-distribution of data. The purpose of this work is to evaluate and investigate different cooperation strategies for multiple source download and select the best one for a scenario for even leeching peers, i.e. peers which depart as soon as they have finished their download. The question arises whether the cooperation strategy can smoothen the overall performance degradation caused by a selfish peer behaviour. As performance indicator the evolution of the numbers of copies of a chunk and the experienced download times of files is applied. The considered scenarios comprise best-case (altruistic peers) and worst-case scenarios (selfish peers). We further propose a new cooperation strategy to improve the file transfer even when mainly selfish peers are present, the CygPriM (cyclic priority masking) strategy. The strategy allows an efficient P2P based content distribution using ordered chunk delivery with only local information available at a peer

The Universal Mobile Telecommunication System (UMTS) offers rate controlled radio bearers for best effort traffic. The purpose of rate control is to maximize resource utilization and concurrently to provide best-effort users with an acceptable grade-of-service. Due to the user behaviour or the used application of a mobile subscriber we distinguish between two types of best-effort users. The time-based users stay for a certain time within the network and download an arbitrary amount of data. Volume-based users leave the network after they have downloaded a specific data volume. The contribution of this work is an evaluation of time-based and volume-based best-effort traffic over rate-controlled DCHs by analytic means and with a detailed packet-level simulation. The results of both approaches are qualitative equal: while the rate control mechanism works effectively for time-based users and allows any rate, the assigned rates for the volume-based users take values between two extremes, either the minimal or the maximal rate

Vertical handovers (VHO) are expected to be a key feature in Beyond 3G (B3G) networks. This paper presents a CAN-based P2P overlay network for supporting vertical handover in B3G networks. The P2P overlay is used to quickly locate attachment points (APs) for mobile entities and to retrieve rapidly the configuration and coverage information of these APs. The advantage of the P2P-based solution is its distributed nature, its scalability, and its self-organizing capability. We show by means of simulation the efficiency and the scalability of our approach in comparison to a standard CAN implementation

The Global System for Mobile Communications (GSM) is the most popular standard for mobile phones in the world with about 1.57 billion customers. The short message service (SMS) in GSM allows amongst others the transmission of short text messages to mobile phones. In case of an emergency, the SMS can be used to warn a large number of individuals. However, the short message delivery is performed on best-effort basis and no quality of service (QoS) agreement is given, i.e., there is no guarantee how long it takes a message to reach the recipient or that the message will be successfully delivered. In this paper, we investigate the message loss probability and the transmission time by performing measurements with different scenarios in a public GSM network. Then, we identify problems according to the requirements for an emergency warning system. We present a solution based on the cell broadcast in GSM and show numerical results regarding the delay and the capacity of the SMS

Vertical handovers (VHO) are expected to be a key feature in beyond 3G (B3G) networks. This paper presents and evaluates a Pastry-based P2P overlay network for supporting vertical handover in B3G networks. The P2P overlay is used to quickly locate attachment points (APs) for mobile entities and to retrieve rapidly the configuration and coverage information of these APs. The advantage of the P2P-based solution is its distributed nature, its scalability, and its self-organizing capability. The performance of the P2P-based VHO support architecture is evaluated in terms of the search time for APs, the scalability of the algorithm, and for homogenous and heterogeneous network layouts. In addition, the solution is compared with the conventional client-server approach

Peer-to-peer (P2P) file-sharing has become a major application in the Internet with respect to traffic volume which is even surpassing Web usage. This characteristic makes P2P commercially attractive to network operators interested in increased traffic. In parallel, the demand for wireless services has caused wireless networks to grow enormously. We assume that P2P file-sharing will be mapped onto mobile environments by its users. This results in a mobile P2P file-sharing service, which we denote as mobile P2P. In this paper, we examine the feasibility of the eDonkey file-sharing service in GPRS networks, detect problems of the interaction between P2P and the mobile network, and find solutions to overcome them. Furthermore, this paper measures and analyzes the characteristics of mobile P2P and gives first empirical performance values. Summarizing, the goal is the analysis of feasibility for an Internet-based file-sharing application in a mobile network and to provide first measurements from two real-world networks.