FG Rechnernetze und Kommunikationssysteme
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TSN Scheduler Benchmarking
(2023)
Time-Sensitive Networking (TSN) disrupts realtime communication technology by making IEEE Ethernet realtime-capable. For time-triggered, hard realtime traffic, TSN provides standardized mechanisms to reserve communication paths as well as individual transmission time slots for data frames. By leveraging these means in a precomputed network schedule, TSN allows for bounded end-to-end delays and minimal jitter. Not being part of the IEEE standard, corresponding scheduling algorithms are an active field of research. Unfortunately, due to differing model assumptions, evaluation setups, and key metrics, a fair comparison of schedulers is impossible so far. In this paper, we present a systematic and reproducible approach to benchmark TSN schedulers. First, we provide a scheduler taxonomy that enables to cluster schedulers by their characteristics. Second, we analyze interactions of input parameters and scheduler results to derive a benchmarking parcour for quantitative comparisons. Finally, we use the approach to benchmark existing schedulers and show subtle interaction effects. This way, our approach enables—for the first time-comparability between schedulers, fueled by the public availability of our benchmarking scenarios.
The majority of Web content is delivered by only a few companies that provide Content Delivery Infrastructuress (CDIss) such as Content Delivery Networkss (CDNss) and cloud hosts. Due to increasing concerns about trends of centralization, empirical studies on the extent and implications of resulting Internet consolidation are necessary. Thus, we present an empirical view on consolidation of the Web by leveraging datasets from two different measurement platforms. We first analyze Web consolidation around CDIs at the level of landing webpages, before narrowing down the analysis to a level of embedded page resources. The datasets cover 1(a) longitudinal measurements of DNS records for 166.5 M Web domains over five years, 1(b) measurements of DNS records for Alexa Top 1 M over a month and (2) measurements of page loads and renders for 4.3 M webpages, which include data on 392.3 M requested resources. We then define CDIs penetration as the ratio of CDI-hosted objects to all measured objects, which we use to quantify consolidation around CDIs. We observe that CDI penetration has close to doubled since 2015, reaching a lower bound of 15% for all .com, .net, and .org Web domains as of January 2020. Overall, we find a set of six CDIss to deliver the majority of content across all datasets, with these six CDIss being responsible for more than 80% of all 221.9 M CDI-delivered resources (56.6% of all resources in total). We find high dependencies of Web content on a small group of CDIss, in particular, for fonts, ads, and trackers, as well as JavaScript resources such as jQuery. We further observe CDIss to play important roles in rolling out IPv6 and TLS 1.3 support. Overall, these observations indicate a potential oligopoly, which brings both benefits but also risks to the future of the Web.
Characterizing the country-wide adoption and evolution of the Jodel messaging app in Saudi Arabia
(2022)
Social media is subject to constant growth and evolution, yet little is known about their early phases of adoption. To shed light on this aspect, this paper empirically characterizes the initial and country-wide adoption of a new type of social media in Saudi Arabia that happened in 2017. Unlike established social media, the studied network Jodel is anonymous and location-based to form hundreds of independent communities country-wide whose adoption pattern we compare. We take a
detailed and full view from the operators perspective on the temporal and geographical dimension on the evolution of these different communities—from their very first the first months of establishment to saturation. This way, we make the early adoption of a new type of social media visible, a process that is often invisible due to the lack of data covering the first days of a new network.
During the COVID-19 pandemic, many smaller conferences have moved entirely online and larger ones are being held as hybrid events. Even beyond the pandemic, hybrid events reduce the carbon footprint of conference travel and makes events more accessible to parts of the research community that have difficulty traveling long distances, while preserving most advantages of in-person gatherings.
While we have developed a solid understanding of how to design virtual events over the last two years, we are still learning how to properly run hybrid events. We present guidelines and considerations-spanning technology, organization and social factors-for organizing successful hybrid conferences.
This paper summarizes and extends the discussions held at the Dagstuhl seminar on "Climate Friendly Internet Research" held in July 2021.
Distributed Denial of Service (DDoS) attacks are among the most critical cybersecurity threats, jeopardizing the stability of even the largest networks and services. The existing range of mitigation services predominantly filters at the edge of the Internet, thus creating unnecessary burden for network infrastructures. Consequently, we present IXP Scrubber, a Machine Learning (ML) based system for detecting and filtering DDoS traffic at the core of the Internet at Internet Exchange Points (IXPs) which see large volumes and varieties of DDoS. IXP Scrubber continuously learns DDoS traffic properties from neighboring Autonomous Systems (ASes). It utilizes BGP signals to drop traffic for certain routes (blackholing) to sample DDoS and can thus learn new attack vectors without the operator’s intervention and on unprecedented amounts of training data. We present three major contributions: i) a method to semi-automatically generate arbitrarily large amounts of labeled DDoS training data from IXPs’ sampled packet traces, ii) the novel, controllable, locally explainable and highly precise two-step IXP Scrubber ML model, and iii) an evaluation of the IXP Scrubber ML model, including its temporal and geographical drift, based on data from 5 IXPs covering a time span of up to two years.
Industrial Control Systems (ICS) are critical systems to our society. Yet they are less studied given their closed nature and often the unavailability of data. While few studies focus on wide-area SCADA systems, e.g., power or gas distribution networks, mission critical networks that control power generation are not yet studied. To address this gap, we perform the first measurement study of Distributed Control System (DCS) by analyzing traces from all network levels from several operational power plants. We show that DCS networks feature a rather rich application mix compared to wide-area SCADA networks and that applications and sites can be fingerprinted with statistical means. While traces from operational power plants are hard to obtain, we analyze to which extent easier to access training facilities can be used as vantage points. Our study aims to shed light on traffic properties of critical industries that were not yet analyzed given the lack of data.
Geographic Differences in Social Media Interactions Exist Between Western and Middle-East Countries
(2022)
In this paper, we empirically analyze two examples of a Western (DE) versus Middle-East (SA) Online Social Messaging App. By focusing on the system interactions over time in comparison, we identify inherent differences in user engagement. We take a deep dive and shed light onto differences in user attention shifts and showcase their structural implications to the user experience. Our main findings show that in comparison to the German counterparts, the Saudi communities prefer creating content in longer conversations, while voting more conservative.
We study the extent to which emoji can be used to add interpretability to embeddings of text and emoji. To do so, we extend the POLAR-framework that transforms word embeddings to interpretable counterparts and apply it to word-emoji embeddings trained on four years of messaging data from the Jodel social network. We devise a crowdsourced human judgement experiment to study six usecases, evaluating against words only, what role emoji can play in adding interpretability to word embeddings. That is, we use a revised POLAR approach interpreting words and emoji with words, emoji or both according to human judgement. We find statistically significant trends demonstrating that emoji can be used to interpret other emoji very well.
In this paper, we study what users talk about in a plethora of independent hyperlocal and anonymous online communities in a single country: Saudi Arabia (KSA). We base this perspective on performing a content classification of the Jodel network in the KSA. To do so, we first contribute a content classification schema that assesses both the intent (why) and the topic (what) of posts. We use the schema to label 15k randomly sampled posts and further classify the top 1k hashtags. We observe a rich set of benign (yet at times controversial in conservative regimes) intents and topics that dominantly address information requests, entertainment, or dating/flirting. By comparing two large cities (Riyadh and Jeddah), we further show that hyperlocality leads to shifts in topic popularity between local communities. By evaluating votes (content appreciation) and replies (reactions), we show that the communities react differently to different topics; e.g., entertaining posts are much appreciated through votes, receiving the least replies, while beliefs & politics receive similarly few replies but are controversially voted.