@phdthesis{65309, author = {{Hemmrich, Simon}}, keywords = {{Reputation Systems, Rating systems, monetary ratings, incentive mechanism, systems theory, Market coordination, advanced review system}}, pages = {{347}}, publisher = {{Universität Paderborn}}, title = {{{A Design Theory for Blockchain-Based Reputation Systems : Trust and Coordination in B2B Markets}}}, doi = {{https://doi.org/10.17619/UNIPB/1-2414}}, year = {{2025}}, } @article{64002, abstract = {{The production of formaldehyde on industrial scale requires huge amounts of energy due to the involvement of reforming processes in combination with the demand in the megaton scale. Hence, a direct route for the transformation of (bio)methane to formaldehyde would decrease costs and puts less pressure on the environment. Herein, we report on the use of zinc modified silicas as possible support materials for vanadium catalysts and the resulting consequences for the performance in the selective oxidation of methane to formaldehyde. After optimization of the Zn content and reaction conditions, a remarkably high space-time yield of 12.4 kgCH2O·kgcat−1·h−1 was achieved. As a result of the extensive characterization by means of UV–vis, Raman, XANES and NMR spectroscopy it was found that vanadium is in the vicinity of highly dispersed zinc atoms which promote the formation of active vanadium species as supposed by theoretical calculations. This work presents a further step of catalyst development towards direct industrial methane conversion which may help to overcome current limitations in the future.}}, author = {{Kunkel, Benny and Seeburg, Dominik and Kabelitz, Anke and Witte, Steffen and Gutmann, Torsten and Breitzke, Hergen and Buntkowsky, Gerd and Buzanich, Ana Guilherme and Wohlrab, Sebastian}}, journal = {{Catalysis Today}}, keywords = {{Formaldehyde, Local coordination, SBA-15, Vanadium oxo species, XANES, Zinc doped silica}}, pages = {{114643}}, title = {{{Highly productive V/Zn-SiO2 catalysts for the selective oxidation of methane}}}, doi = {{10.1016/j.cattod.2024.114643}}, volume = {{432}}, year = {{2024}}, } @inproceedings{30236, abstract = {{Recent reinforcement learning approaches for continuous control in wireless mobile networks have shown impressive results. But due to the lack of open and compatible simulators, authors typically create their own simulation environments for training and evaluation. This is cumbersome and time-consuming for authors and limits reproducibility and comparability, ultimately impeding progress in the field. To this end, we propose mobile-env, a simple and open platform for training, evaluating, and comparing reinforcement learning and conventional approaches for continuous control in mobile wireless networks. mobile-env is lightweight and implements the common OpenAI Gym interface and additional wrappers, which allows connecting virtually any single-agent or multi-agent reinforcement learning framework to the environment. While mobile-env provides sensible default values and can be used out of the box, it also has many configuration options and is easy to extend. We therefore believe mobile-env to be a valuable platform for driving meaningful progress in autonomous coordination of wireless mobile networks.}}, author = {{Schneider, Stefan Balthasar and Werner, Stefan and Khalili, Ramin and Hecker, Artur and Karl, Holger}}, booktitle = {{IEEE/IFIP Network Operations and Management Symposium (NOMS)}}, keywords = {{wireless mobile networks, network management, continuous control, cognitive networks, autonomous coordination, reinforcement learning, gym environment, simulation, open source}}, location = {{Budapest}}, publisher = {{IEEE}}, title = {{{mobile-env: An Open Platform for Reinforcement Learning in Wireless Mobile Networks}}}, year = {{2022}}, } @inproceedings{21543, abstract = {{Services often consist of multiple chained components such as microservices in a service mesh, or machine learning functions in a pipeline. Providing these services requires online coordination including scaling the service, placing instance of all components in the network, scheduling traffic to these instances, and routing traffic through the network. Optimized service coordination is still a hard problem due to many influencing factors such as rapidly arriving user demands and limited node and link capacity. Existing approaches to solve the problem are often built on rigid models and assumptions, tailored to specific scenarios. If the scenario changes and the assumptions no longer hold, they easily break and require manual adjustments by experts. Novel self-learning approaches using deep reinforcement learning (DRL) are promising but still have limitations as they only address simplified versions of the problem and are typically centralized and thus do not scale to practical large-scale networks. To address these issues, we propose a distributed self-learning service coordination approach using DRL. After centralized training, we deploy a distributed DRL agent at each node in the network, making fast coordination decisions locally in parallel with the other nodes. Each agent only observes its direct neighbors and does not need global knowledge. Hence, our approach scales independently from the size of the network. In our extensive evaluation using real-world network topologies and traffic traces, we show that our proposed approach outperforms a state-of-the-art conventional heuristic as well as a centralized DRL approach (60% higher throughput on average) while requiring less time per online decision (1 ms).}}, author = {{Schneider, Stefan Balthasar and Qarawlus, Haydar and Karl, Holger}}, booktitle = {{IEEE International Conference on Distributed Computing Systems (ICDCS)}}, keywords = {{network management, service management, coordination, reinforcement learning, distributed}}, location = {{Washington, DC, USA}}, publisher = {{IEEE}}, title = {{{Distributed Online Service Coordination Using Deep Reinforcement Learning}}}, year = {{2021}}, } @inproceedings{20693, abstract = {{In practical, large-scale networks, services are requested by users across the globe, e.g., for video streaming. Services consist of multiple interconnected components such as microservices in a service mesh. Coordinating these services requires scaling them according to continuously changing user demand, deploying instances at the edge close to their users, and routing traffic efficiently between users and connected instances. Network and service coordination is commonly addressed through centralized approaches, where a single coordinator knows everything and coordinates the entire network globally. While such centralized approaches can reach global optima, they do not scale to large, realistic networks. In contrast, distributed approaches scale well, but sacrifice solution quality due to their limited scope of knowledge and coordination decisions. To this end, we propose a hierarchical coordination approach that combines the good solution quality of centralized approaches with the scalability of distributed approaches. In doing so, we divide the network into multiple hierarchical domains and optimize coordination in a top-down manner. We compare our hierarchical with a centralized approach in an extensive evaluation on a real-world network topology. Our results indicate that hierarchical coordination can find close-to-optimal solutions in a fraction of the runtime of centralized approaches.}}, author = {{Schneider, Stefan Balthasar and Jürgens, Mirko and Karl, Holger}}, booktitle = {{IFIP/IEEE International Symposium on Integrated Network Management (IM)}}, keywords = {{network management, service management, coordination, hierarchical, scalability, nfv}}, location = {{Bordeaux, France}}, publisher = {{IFIP/IEEE}}, title = {{{Divide and Conquer: Hierarchical Network and Service Coordination}}}, year = {{2021}}, } @article{21808, abstract = {{Modern services consist of interconnected components,e.g., microservices in a service mesh or machine learning functions in a pipeline. These services can scale and run across multiple network nodes on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities, changing demands, and Quality of Service (QoS) requirements into account. This challenge is usually solved with custom approaches designed by experts. While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e.g., a priori knowledge). We propose DeepCoord, a novel deep reinforcement learning approach that learns how to best coordinate services and is geared towards realistic assumptions. It interacts with the network and relies on available, possibly delayed monitoring information. Rather than defining a complex model or an algorithm on how to achieve an objective, our model-free approach adapts to various objectives and traffic patterns. An agent is trained offline without expert knowledge and then applied online with minimal overhead. Compared to a state-of-the-art heuristic, DeepCoord significantly improves flow throughput (up to 76%) and overall network utility (more than 2x) on realworld network topologies and traffic traces. It also supports optimizing multiple, possibly competing objectives, learns to respect QoS requirements, generalizes to scenarios with unseen, stochastic traffic, and scales to large real-world networks. For reproducibility and reuse, our code is publicly available.}}, author = {{Schneider, Stefan Balthasar and Khalili, Ramin and Manzoor, Adnan and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Hecker, Artur}}, journal = {{Transactions on Network and Service Management}}, keywords = {{network management, service management, coordination, reinforcement learning, self-learning, self-adaptation, multi-objective}}, publisher = {{IEEE}}, title = {{{Self-Learning Multi-Objective Service Coordination Using Deep Reinforcement Learning}}}, doi = {{10.1109/TNSM.2021.3076503}}, year = {{2021}}, } @techreport{35889, abstract = {{Network and service coordination is important to provide modern services consisting of multiple interconnected components, e.g., in 5G, network function virtualization (NFV), or cloud and edge computing. In this paper, I outline my dissertation research, which proposes six approaches to automate such network and service coordination. All approaches dynamically react to the current demand and optimize coordination for high service quality and low costs. The approaches range from centralized to distributed methods and from conventional heuristic algorithms and mixed-integer linear programs to machine learning approaches using supervised and reinforcement learning. I briefly discuss their main ideas and advantages over other state-of-the-art approaches and compare strengths and weaknesses.}}, author = {{Schneider, Stefan Balthasar}}, keywords = {{nfv, coordination, machine learning, reinforcement learning, phd, digest}}, title = {{{Conventional and Machine Learning Approaches for Network and Service Coordination}}}, year = {{2021}}, } @inproceedings{19607, abstract = {{Modern services consist of modular, interconnected components, e.g., microservices forming a service mesh. To dynamically adjust to ever-changing service demands, service components have to be instantiated on nodes across the network. Incoming flows requesting a service then need to be routed through the deployed instances while considering node and link capacities. Ultimately, the goal is to maximize the successfully served flows and Quality of Service (QoS) through online service coordination. Current approaches for service coordination are usually centralized, assuming up-to-date global knowledge and making global decisions for all nodes in the network. Such global knowledge and centralized decisions are not realistic in practical large-scale networks. To solve this problem, we propose two algorithms for fully distributed service coordination. The proposed algorithms can be executed individually at each node in parallel and require only very limited global knowledge. We compare and evaluate both algorithms with a state-of-the-art centralized approach in extensive simulations on a large-scale, real-world network topology. Our results indicate that the two algorithms can compete with centralized approaches in terms of solution quality but require less global knowledge and are magnitudes faster (more than 100x).}}, author = {{Schneider, Stefan Balthasar and Klenner, Lars Dietrich and Karl, Holger}}, booktitle = {{IEEE International Conference on Network and Service Management (CNSM)}}, keywords = {{distributed management, service coordination, network coordination, nfv, softwarization, orchestration}}, publisher = {{IEEE}}, title = {{{Every Node for Itself: Fully Distributed Service Coordination}}}, year = {{2020}}, } @inproceedings{19609, abstract = {{Modern services comprise interconnected components, e.g., microservices in a service mesh, that can scale and run on multiple nodes across the network on demand. To process incoming traffic, service components have to be instantiated and traffic assigned to these instances, taking capacities and changing demands into account. This challenge is usually solved with custom approaches designed by experts. While this typically works well for the considered scenario, the models often rely on unrealistic assumptions or on knowledge that is not available in practice (e.g., a priori knowledge). We propose a novel deep reinforcement learning approach that learns how to best coordinate services and is geared towards realistic assumptions. It interacts with the network and relies on available, possibly delayed monitoring information. Rather than defining a complex model or an algorithm how to achieve an objective, our model-free approach adapts to various objectives and traffic patterns. An agent is trained offline without expert knowledge and then applied online with minimal overhead. Compared to a state-of-the-art heuristic, it significantly improves flow throughput and overall network utility on real-world network topologies and traffic traces. It also learns to optimize different objectives, generalizes to scenarios with unseen, stochastic traffic patterns, and scales to large real-world networks.}}, author = {{Schneider, Stefan Balthasar and Manzoor, Adnan and Qarawlus, Haydar and Schellenberg, Rafael and Karl, Holger and Khalili, Ramin and Hecker, Artur}}, booktitle = {{IEEE International Conference on Network and Service Management (CNSM)}}, keywords = {{self-driving networks, self-learning, network coordination, service coordination, reinforcement learning, deep learning, nfv}}, publisher = {{IEEE}}, title = {{{Self-Driving Network and Service Coordination Using Deep Reinforcement Learning}}}, year = {{2020}}, } @inproceedings{5675, abstract = {{When responding to natural disasters, professional relief units are often supported by many volunteers which are not affiliated to humanitarian organizations. The effective coordination of these volunteers is crucial to leverage their capabilities and to avoid conflicts with professional relief units. In this paper, we empirically identify key requirements that professional relief units pose on this coordination. Based on these requirements, we suggest a decision model. We computationally solve a real-world instance of the model and empirically validate the computed solution in interviews with practitioners. Our results show that the suggested model allows for solving volunteer coordination tasks of realistic size near-optimally within short time, with the determined solution being well accepted by practitioners. We also describe in this article how the suggested decision support model is integrated in the volunteer coordination system which we develop in joint cooperation with a disaster management authority and a software development company.}}, author = {{Rauchecker, Gerhard and Schryen, Guido}}, booktitle = {{Proceedings of the 15th International Conference on Information Systems for Crisis Response and Management}}, keywords = {{Coordination of spontaneous volunteers, volunteer coordination system, decision support, scheduling optimization model, linear programming}}, location = {{Rochester, NY, USA}}, title = {{{Decision Support for the Optimal Coordination of Spontaneous Volunteers in Disaster Relief}}}, year = {{2018}}, } @article{17199, abstract = {{Research of tutoring in parent-infant interaction has shown that tutors - when presenting some action - modify both their verbal and manual performance for the learner (‘motherese’, ‘motionese’). Investigating the sources and effects of the tutors’ action modifications, we suggest an interactional account of ‘motionese’. Using video-data from a semi-experimental study in which parents taught their 8 to 11 month old infants how to nest a set of differently sized cups, we found that the tutors’ action modifications (in particular: high arches) functioned as an orienting device to guide the infant’s visual attention (gaze). Action modification and the recipient’s gaze can be seen to have a reciprocal sequential relationship and to constitute a constant loop of mutual adjustments. Implications are discussed for developmental research and for robotic ‘Social Learning’. We argue that a robot system could use on-line feedback strategies (e.g. gaze) to pro-actively shape a tutor’s action presentation as it emerges.}}, author = {{Pitsch, Karola and Vollmer, Anna-Lisa and Rohlfing, Katharina and Fritsch, Jannik and Wrede, Britta}}, issn = {{1572-0381}}, journal = {{Interaction Studies}}, keywords = {{conversation analysis, interactional coordination, adult-child-interaction, feedback, gaze, quantification, social learning, motionese, tutoring}}, number = {{1}}, pages = {{55--98}}, publisher = {{John Benjamins Publishing Company}}, title = {{{Tutoring in adult-child-interaction: On the loop of the tutor's action modification and the recipient's gaze}}}, doi = {{10.1075/is.15.1.03pit}}, volume = {{15}}, year = {{2014}}, }