David Bermbach

Ahmet-Serdar Karakaya, Thomas Ritter, Felix Biessmann et al.

In cities worldwide, cars cause health and traffic problems whichcould be partly mitigated through an increased modal share of bicycles. Many people, however, avoid cycling due to a lack of perceived safety. For city planners, addressing this is hard as they lack insights intowhere cyclists feel safe and where they do not. To gain such insights,we have in previous work proposed the crowdsourcing platform SimRa,which allows cyclists to record their rides and report near miss incidentsvia a smartphone app. In this paper, we present CycleSense, a combination of signal pro-cessing and Machine Learning techniques, which partially automatesthe detection of near miss incidents, thus making the reporting of nearmiss incidents easier. Using the SimRa data set, we evaluate CycleSenseby comparing it to a baseline method used by SimRa and show that itsignificantly improves incident detection.

Diana Baumann, Tim C. Rese, David Bermbach

Spatiotemporal data are being produced in continuously growing volumes by a variety of data sources and a variety of application fields rely on rapid analysis of such data. Existing systems such as PostGIS or MobilityDB usually build on relational database systems, thus, inheriting their scale-out characteristics. As a consequence, big spatiotemporal data scenarios still have limited support even though many query types can easily be parallelized. In this paper, we propose our vision of a native serverless data processing approach for spatiotemporal data: We break down queries into small subqueries which then leverage the near-instant scaling of Function-as-a-Service platforms to execute them in parallel. With this, we partially solve the scalability needs of big spatiotemporal data processing.

Emil Balitzki, Tobias Pfandzelter, David Bermbach

To fully exploit the benefits of the fog environment, efficient management of data locality is crucial. Blind or reactive data replication falls short in harnessing the potential of fog computing, necessitating more advanced techniques for predicting where and when clients will connect. While spatial prediction has received considerable attention, temporal prediction remains understudied. Our paper addresses this gap by examining the advantages of incorporating temporal prediction into existing spatial prediction models. We also provide a comprehensive analysis of spatio-temporal prediction models, such as Deep Neural Networks and Markov models, in the context of predictive replication. We propose a novel model using Holt-Winter's Exponential Smoothing for temporal prediction, leveraging sequential and periodical user movement patterns. In a fog network simulation with real user trajectories our model achieves a 15% reduction in excess data with a marginal 1% decrease in data availability.

Mohammadreza Malekabbasi, Minghe Wang, David Bermbach

Deploying Large Language Model (LLM) services at the edge benefits latency-sensitive and privacy-aware applications. However, the stateless nature of LLMs makes managing user context (e.g., sessions, preferences) across geo-distributed edge nodes challenging. Existing solutions, such as client-side context storage, often introduce network latency and bandwidth overhead, undermining the advantages of edge deployment. We propose DisCEdge, a distributed context management system that stores and replicates user context in tokenized form across edge nodes. By maintaining context as token sequences rather than raw text, our system avoids redundant computation and enables efficient data replication. We implement and evaluate an open-source prototype in a realistic edge environment with commodity hardware. We show DisCEdge improves median response times by up to 14.46% and lowers median inter-node synchronization overhead by up to 15% compared to a raw-text-based system. It also reduces client request sizes by a median of 90% compared to client-side context management, while guaranteeing data consistency.

F. Stricker, J. A. Peregrina, D. Bermbach et al.

Federated Learning (FL) is an upcoming technology that is increasingly applied in real-world applications. Early applications focused on cross-device scenarios, where many participants with limited resources train machine learning (ML) models together, e.g., in the case of Google's GBoard. Contrarily, cross-silo scenarios have only few participants but with many resources, e.g., in the healthcare domain. Despite such early efforts, FL is still rarely used in practice and best practices are, hence, missing. For new applications, in our case inter-organizational cross-silo applications, overcoming this lack of role models is a significant challenge. In order to ease the use of FL in real-world cross-silo applications, we here propose a scenario-based architecture for the practical use of FL in the context of multiple companies collaborating to improve the quality of their ML models. The architecture emphasizes the collaboration between the participants and the FL server and extends basic interactions with domain-specific features. First, it combines governance with authentication, creating an environment where only trusted participants can join. Second, it offers traceability of governance decisions and tracking of training processes, which are also crucial in a production environment. Beyond presenting the architectural design, we analyze requirements for the real-world use of FL and evaluate the architecture with a scenario-based analysis method.

David Bermbach, Erik Wittern

Modern applications increasingly interact with web APIs -- reusable components, deployed and operated outside the application, and accessed over the network. Their existence, arguably, spurs application innovations, making it easy to integrate data or functionalities. While previous work has analyzed the ecosystem of web APIs and their design, little is known about web API quality at runtime. This gap is critical, as qualities including availability, latency, or provider security preferences can severely impact applications and user experience. In this paper, we revisit a 3-month, geo-distributed benchmark of popular web APIs, originally performed in 2015. We repeat this benchmark in 2018 and compare results from these two benchmarks regarding availability and latency. We furthermore introduce new results from assessing provider security preferences, collected both in 2015 and 2018, and results from our attempts to reach out to API providers with the results from our 2015 experiments. Our extensive experiments show that web API qualities vary 1.) based on the geo-distribution of clients, 2.) during our individual experiments, and 3.) between the two experiments. Our findings provide evidence to foster the discussion around web API quality, and can act as a basis for the creation of tools and approaches to mitigate quality issues.