White paper on crowdsourced network and QoE measurements – definitions, use cases and challenges

The goal of the white paper at hand is as follows. The definitions of the terms build a framework for discussions around the hype topic ‘crowdsourcing’. This serves as a basis for differentiation and a consistent view from different perspectives on crowdsourced network measurements, with the goal to provide a commonly accepted definition in the community. The focus is on the context of mobile and fixed network operators, but also on measurements of different layers (network, application, user layer). In addition, the white paper shows the value of crowdsourcing for selected use cases, e.g., to improve QoE or regulatory issues. Finally, the major challenges and issues for researchers and practitioners are highlighted. This white paper is the outcome of the Würzburg seminar on “Crowdsourced Network and QoE Measurements” which took place from 25-26 September 2019 in Würzburg, Germany. International experts were invited from industry and academia. They are well known in their communities, having different backgrounds in crowdsourcing, mobile networks, network measurements, network performance, Quality of Service (QoS), and Quality of Experience (QoE). The discussions in the seminar focused on how crowdsourcing will support vendors, operators, and regulators to determine the Quality of Experience in new 5G networks that enable various new applications and network architectures. As a result of the discussions, the need for a white paper manifested, with the goal of providing a scientific discussion of the terms “crowdsourced network measurements” and “crowdsourced QoE measurements”, describing relevant use cases for such crowdsourced data, and its underlying challenges. During the seminar, those main topics were identified, intensively discussed in break-out groups, and brought back into the plenum several times. The outcome of the seminar is this white paper at hand which is – to our knowledge – the first one covering the topic of crowdsourced network and QoE measurements

Describing Subjective Experiment Consistency by pp-Value P-P Plot

There are phenomena that cannot be measured without subjective testing. However, subjective testing is a complex issue with many influencing factors. These interplay to yield either precise or incorrect results. Researchers require a tool to classify results of subjective experiment as either consistent or inconsistent. This is necessary in order to decide whether to treat the gathered scores as quality ground truth data. Knowing if subjective scores can be trusted is key to drawing valid conclusions and building functional tools based on those scores (e.g., algorithms assessing the perceived quality of multimedia materials). We provide a tool to classify subjective experiment (and all its results) as either consistent or inconsistent. Additionally, the tool identifies stimuli having irregular score distribution. The approach is based on treating subjective scores as a random variable coming from the discrete Generalized Score Distribution (GSD). The GSD, in combination with a bootstrapped G-test of goodness-of-fit, allows to construct $p$-value P-P plot that visualizes experiment's consistency. The tool safeguards researchers from using inconsistent subjective data. In this way, it makes sure that conclusions they draw and tools they build are more precise and trustworthy. The proposed approach works in line with expectations drawn solely on experiment design descriptions of 21 real-life multimedia quality subjective experiments.Comment: 11 pages, 3 figures. Accepted to 28th ACM International Conference on Multimedia (MM '20). For associated data sets, source codes and documentation, see https://github.com/Qub3k/subjective-exp-consistency-chec

Want more WANs? Comparison of traditional and GAN-based generation of wide area network topologies via graph and performance metrics

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

Do you agree? Contrasting Google's core web vitals and the impact of cookie consent banners with actual web QoE

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, and investigate whether the Core Web Vitals and web QoE agree on the perceived experience. 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. For this purpose, we developed CWeQS, a customized framework to emulate the entire web page loading process, and ask users for their experience while controlling the Core Web Vitals, which is available to the public. To properly configure CWeQS for the planned QoE study and the crowdsourcing setup, we conduct pre-studies, in which we evaluate the importance of the loading strategy of a web page and the importance of the user task. The obtained insights allow us to conduct the desired QoE studies for each of the Core Web Vitals. Furthermore, we assess the impact of cookie consent banners, which have become ubiquitous due to regulatory demands, on the Core Web Vitals and investigate their influence on web QoE. Our results suggest that the Core Web Vitals are much less predictive for web QoE than expected and that page loading times remain the main metric and influence factor in this context. We further observe that unobtrusive and acentric cookie consent banners are preferred by end-users and that additional delays caused by interacting with consent banners in order to agree to or reject cookies should be accounted along with the actual page load time to reduce waiting times and thus to improve web QoE

Share and multiply: modeling communication and generated traffic in private WhatsApp groups

Group-based communication is a highly popular communication paradigm, which is especially prominent in mobile instant messaging (MIM) applications, such as WhatsApp. Chat groups in MIM applications facilitate the sharing of various types of messages (e.g., text, voice, image, video) among a large number of participants. As each message has to be transmitted to every other member of the group, which multiplies the traffic, this has a massive impact on the underlying communication networks. However, most chat groups are private and network operators cannot obtain deep insights into MIM communication via network measurements due to end-to-end encryption. Thus, the generation of traffic is not well understood, given that it depends on sizes of communication groups, speed of communication, and exchanged message types. In this work, we provide a huge data set of 5,956 private WhatsApp chat histories, which contains over 76 million messages from more than 117,000 users. We describe and model the properties of chat groups and users, and the communication within these chat groups, which gives unprecedented insights into private MIM communication. In addition, we conduct exemplary measurements for the most popular message types, which empower the provided models to estimate the traffic over time in a chat group

Systematic Analysis of Experiment Precision Measures and Methods for Experiments Comparison

The notion of experiment precision quantifies the variance of user ratings in a subjective experiment. Although there exist measures that assess subjective experiment precision, there are no systematic analyses of these measures available in the literature. To the best of our knowledge, there is also no systematic framework in the Multimedia Quality Assessment 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 systematically analyse the performance of the measures and methods proposed. We do so both through a simulation study (varying user rating variance and bias) and by using data from four real-world Quality of Experience (QoE) subjective experiments. In the simulation study we focus on crowdsourcing QoE experiments, since they are known to generate ratings with higher variance and bias, when compared to traditional subjective experiment methodologies. We conclude that our proposed measures and related comparison methods properly capture experiment precision (both when tested on simulated and real-world data). One of the measures also proves capable of dealing with even significantly biased responses. 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 standardisation activities.Comment: 18 pages, 9 figures. Under review in IEEE Transactions on Multimedia. More results and references added. Improved style. Discussion section and appendices extende

Crowdsourced network measurements: Benefits and best practices

Network measurements are of high importance both for the operation of networks and for the design and evaluation of new management mechanisms. Therefore, several approaches exist for running network measurements, ranging from analyzing live traffic traces from campus or Internet Service Provider (ISP) networks to performing active measurements on distributed testbeds, e.g., PlanetLab, or involving volunteers. However, each method falls short, offering only a partial view of the network. For instance, the scope of passive traffic traces is limited to an ISP’s network and customers’ habits, whereas active measurements might be biased by the population or node location involved. To complement these techniques, we propose to use (commercial) crowdsourcing platforms for network measurements. They permit a controllable, diverse and realistic view of the Internet and provide better control than do measurements with voluntary participants. In this study, we compare crowdsourcing with traditional measurement techniques, describe possible pitfalls and limitations, and present best practices to overcome these issues. The contribution of this paper is a guideline for researchers to understand when and how to exploit crowdsourcing for network measurements

HORST -Home Router Sharing based on Trust

Abstract-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