@phdthesis{Scharnagl2022,
  author    = {Scharnagl, Julian},
  title     = {Distributed Guidance, Navigation and Control for Satellite Formation Flying Missions},
  isbn      = {978-3-945459-42-3},
  doi       = {10.25972/OPUS-28753},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-287530},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {Ongoing changes in spaceflight - continuing miniaturization, declining costs of rocket launches and satellite components, and improved satellite computing and control capabilities - are advancing Satellite Formation Flying (SFF) as a research and application area. SFF enables new applications that cannot be realized (or cannot be realized at a reasonable cost) with conventional single-satellite missions. In particular, distributed Earth observation applications such as photogrammetry and tomography or distributed space telescopes require precisely placed and controlled satellites in orbit. Several enabling technologies are required for SFF, such as inter-satellite communication, precise attitude control, and in-orbit maneuverability. However, one of the most important requirements is a reliable distributed Guidance, Navigation and Control (GNC) strategy. This work addresses the issue of distributed GNC for SFF in 3D with a focus on Continuous Low-Thrust (CLT) propulsion satellites (e.g., with electric thrusters) and concentrates on circular low Earth orbits. However, the focus of this work is not only on control theory, but control is considered as part of the system engineering process of typical small satellite missions. Thus, common sensor and actuator systems are analyzed to derive their characteristics and their impacts on formation control. This serves as the basis for the design, implementation, and evaluation of the following control approaches: First, a Model Predictive Control (MPC) method with specific adaptations to SFF and its requirements and constraints; second, a distributed robust controller that combines consensus methods for distributed system control and \$H_{\infty}\$ robust control; and finally, a controller that uses plant inversion for control and combines it with a reference governor to steer the controller to the target on an optimal trajectory considering several constraints. The developed controllers are validated and compared based on extensive software simulations. Realistic 3D formation flight scenarios were taken from the Networked Pico-Satellite Distributed System Control (NetSat) cubesat formation flight mission. The three compared methods show different advantages and disadvantages in the different application scenarios. The distributed robust consensus-based controller for example lacks the ability to limit the maximum thrust, so it is not suitable for satellites with CLT. But both the MPC-based approach and the plant inversionbased controller are suitable for CLT SFF applications, while showing again distinct advantages and disadvantages in different scenarios. The scientific contribution of this work may be summarized as the creation of novel and specific control approaches for the class of CLT SFF applications, which is still lacking methods withstanding the application in real space missions, as well as the scientific evaluation and comparison of the developed methods.},
  subject      = {Kleinsatellit},
  language  = {en}
}
@phdthesis{Grohmann2022,
  author    = {Grohmann, Johannes Sebastian},
  title     = {Model Learning for Performance Prediction of Cloud-native Microservice Applications},
  doi       = {10.25972/OPUS-26160},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-261608},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {One consequence of the recent coronavirus pandemic is increased demand and use of online services around the globe. At the same time, performance requirements for modern technologies are becoming more stringent as users become accustomed to higher standards. These increased performance and availability requirements, coupled with the unpredictable usage growth, are driving an increasing proportion of applications to run on public cloud platforms as they promise better scalability and reliability. With data centers already responsible for about one percent of the world's power consumption, optimizing resource usage is of paramount importance. Simultaneously, meeting the increasing and changing resource and performance requirements is only possible by optimizing resource management without introducing additional overhead. This requires the research and development of new modeling approaches to understand the behavior of running applications with minimal information. However, the emergence of modern software paradigms makes it increasingly difficult to derive such models and renders previous performance modeling techniques infeasible. Modern cloud applications are often deployed as a collection of fine-grained and interconnected components called microservices. Microservice architectures offer massive benefits but also have broad implications for the performance characteristics of the respective systems. In addition, the microservices paradigm is typically paired with a DevOps culture, resulting in frequent application and deployment changes. Such applications are often referred to as cloud-native applications. In summary, the increasing use of ever-changing cloud-hosted microservice applications introduces a number of unique challenges for modeling the performance of modern applications. These include the amount, type, and structure of monitoring data, frequent behavioral changes, or infrastructure variabilities. This violates common assumptions of the state of the art and opens a research gap for our work. In this thesis, we present five techniques for automated learning of performance models for cloud-native software systems. We achieve this by combining machine learning with traditional performance modeling techniques. Unlike previous work, our focus is on cloud-hosted and continuously evolving microservice architectures, so-called cloud-native applications. Therefore, our contributions aim to solve the above challenges to deliver automated performance models with minimal computational overhead and no manual intervention. Depending on the cloud computing model, privacy agreements, or monitoring capabilities of each platform, we identify different scenarios where performance modeling, prediction, and optimization techniques can provide great benefits. Specifically, the contributions of this thesis are as follows: Monitorless: Application-agnostic prediction of performance degradations. To manage application performance with only platform-level monitoring, we propose Monitorless, the first truly application-independent approach to detecting performance degradation. We use machine learning to bridge the gap between platform-level monitoring and application-specific measurements, eliminating the need for application-level monitoring. Monitorless creates a single and holistic resource saturation model that can be used for heterogeneous and untrained applications. Results show that Monitorless infers resource-based performance degradation with 97\% accuracy. Moreover, it can achieve similar performance to typical autoscaling solutions, despite using less monitoring information. SuanMing: Predicting performance degradation using tracing. We introduce SuanMing to mitigate performance issues before they impact the user experience. This contribution is applied in scenarios where tracing tools enable application-level monitoring. SuanMing predicts explainable causes of expected performance degradations and prevents performance degradations before they occur. Evaluation results show that SuanMing can predict and pinpoint future performance degradations with an accuracy of over 90\%. SARDE: Continuous and autonomous estimation of resource demands. We present SARDE to learn application models for highly variable application deployments. This contribution focuses on the continuous estimation of application resource demands, a key parameter of performance models. SARDE represents an autonomous ensemble estimation technique. It dynamically and continuously optimizes, selects, and executes an ensemble of approaches to estimate resource demands in response to changes in the application or its environment. Through continuous online adaptation, SARDE efficiently achieves an average resource demand estimation error of 15.96\% in our evaluation. DepIC: Learning parametric dependencies from monitoring data. DepIC utilizes feature selection techniques in combination with an ensemble regression approach to automatically identify and characterize parametric dependencies. Although parametric dependencies can massively improve the accuracy of performance models, DepIC is the first approach to automatically learn such parametric dependencies from passive monitoring data streams. Our evaluation shows that DepIC achieves 91.7\% precision in identifying dependencies and reduces the characterization prediction error by 30\% compared to the best individual approach. Baloo: Modeling the configuration space of databases. To study the impact of different configurations within distributed DBMSs, we introduce Baloo. Our last contribution models the configuration space of databases considering measurement variabilities in the cloud. More specifically, Baloo dynamically estimates the required benchmarking measurements and automatically builds a configuration space model of a given DBMS. Our evaluation of Baloo on a dataset consisting of 900 configuration points shows that the framework achieves a prediction error of less than 11\% while saving up to 80\% of the measurement effort. Although the contributions themselves are orthogonally aligned, taken together they provide a holistic approach to performance management of modern cloud-native microservice applications. Our contributions are a significant step forward as they specifically target novel and cloud-native software development and operation paradigms, surpassing the capabilities and limitations of previous approaches. In addition, the research presented in this paper also has a significant impact on the industry, as the contributions were developed in collaboration with research teams from Nokia Bell Labs, Huawei, and Google. Overall, our solutions open up new possibilities for managing and optimizing cloud applications and improve cost and energy efficiency.},
  subject      = {Cloud Computing},
  language  = {en}
}
@phdthesis{Hock2014,
  author    = {Hock, David Rog{\´e}r},
  title     = {Analysis and Optimization of Resilient Routing in Core Communication Networks},
  issn      = {1432-8801},
  doi       = {10.25972/OPUS-10168},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-101681},
  school      = {Universit{\"a}t W{\"u}rzburg},
  pages     = {175},
  year      = {2014},
  abstract  = {Routing is one of the most important issues in any communication network. It defines on which path packets are transmitted from the source of a connection to the destination. It allows to control the distribution of flows between different locations in the network and thereby is a means to influence the load distribution or to reach certain constraints imposed by particular applications. As failures in communication networks appear regularly and cannot be completely avoided, routing is required to be resilient against such outages, i.e., routing still has to be able to forward packets on backup paths even if primary paths are not working any more. Throughout the years, various routing technologies have been introduced that are very different in their control structure, in their way of working, and in their ability to handle certain failure cases. Each of the different routing approaches opens up their own specific questions regarding configuration, optimization, and inclusion of resilience issues. This monograph investigates, with the example of three particular routing technologies, some concrete issues regarding the analysis and optimization of resilience. It thereby contributes to a better general, technology-independent understanding of these approaches and of their diverse potential for the use in future network architectures. The first considered routing type, is decentralized intra-domain routing based on administrative IP link costs and the shortest path principle. Typical examples are common today's intra-domain routing protocols OSPF and IS-IS. This type of routing includes automatic restoration abilities in case of failures what makes it in general very robust even in the case of severe network outages including several failed components. Furthermore, special IP-Fast Reroute mechanisms allow for a faster reaction on outages. For routing based on link costs, traffic engineering, e.g. the optimization of the maximum relative link load in the network, can be done indirectly by changing the administrative link costs to adequate values. The second considered routing type, MPLS-based routing, is based on the a priori configuration of primary and backup paths, so-called Label Switched Paths. The routing layout of MPLS paths offers more freedom compared to IP-based routing as it is not restricted by any shortest path constraints but any paths can be setup. However, this in general involves a higher configuration effort. Finally, in the third considered routing type, typically centralized routing using a Software Defined Networking (SDN) architecture, simple switches only forward packets according to routing decisions made by centralized controller units. SDN-based routing layouts offer the same freedom as for explicit paths configured using MPLS. In case of a failure, new rules can be setup by the controllers to continue the routing in the reduced topology. However, new resilience issues arise caused by the centralized architecture. If controllers are not reachable anymore, the forwarding rules in the single nodes cannot be adapted anymore. This might render a rerouting in case of connection problems in severe failure scenarios infeasible.},
  subject      = {Leistungsbewertung},
  language  = {en}
}
@phdthesis{Lehrieder2013,
  author    = {Lehrieder, Frank},
  title     = {Performance Evaluation and Optimization of Content Distribution using Overlay Networks},
  doi       = {10.25972/OPUS-6420},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-76018},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2013},
  abstract  = {The work presents a performance evaluation and optimization of so-called overlay networks for content distribution in the Internet. Chapter 1 describes the importance which have such networks in today's Internet, for example, for the transmission of video content. The focus of this work is on overlay networks based on the peer-to-peer principle. These are characterized by the fact that users who download content, also contribute to the distribution process by sharing parts of the data to other users. This enables efficient content distribution because each user not only consumes resources in the system, but also provides its own resources. Chapter 2 of the monograph contains a detailed description of the functionality of today's most popular overlay network BitTorrent. It explains the various components and their interaction. This is followed by an illustration of why such overlay networks for Internet service providers (ISPs) are problematic. The reason lies in the large amount of inter-ISP traffic that is produced by these overlay networks. Since this inter-ISP traffic leads to high costs for ISPs, they try to reduce it by improved mechanisms for overlay networks. One optimization approach is the use of topology awareness within the overlay networks. It provides users of the overlay networks with information about the underlying physical network topology. This allows them to avoid inter-ISP traffic by exchanging data preferrentially with other users that are connected to the same ISP. Another approach to save inter-ISP traffic is caching. In this case the ISP provides additional computers in its network, called caches, which store copies of popular content. The users of this ISP can then obtain such content from the cache. This prevents that the content must be retrieved from locations outside of the ISP's network, and saves costly inter-ISP traffic in this way. In the third chapter of the thesis, the results of a comprehensive measurement study of overlay networks, which can be found in today's Internet, are presented. After a short description of the measurement methodology, the results of the measurements are described. These results contain data on a variety of characteristics of current P2P overlay networks in the Internet. These include the popularity of content, i.e., how many users are interested in specific content, the evolution of the popularity and the size of the files. The distribution of users within the Internet is investigated in detail. Special attention is given to the number of users that exchange a particular file within the same ISP. On the basis of these measurement results, an estimation of the traffic savings that can achieved by topology awareness is derived. This new estimation is of scientific and practical importance, since it is not limited to individual ISPs and files, but considers the whole Internet and the total amount of data exchanged in overlay networks. Finally, the characteristics of regional content are considered, in which the popularity is limited to certain parts of the Internet. This is for example the case of videos in German, Italian or French language. Chapter 4 of the thesis is devoted to the optimization of overlay networks for content distribution through caching. It presents a deterministic flow model that describes the influence of caches. On the basis of this model, it derives an estimate of the inter-ISP traffic that is generated by an overlay network, and which part can be saved by caches. The results show that the influence of the cache depends on the structure of the overlay networks, and that caches can also lead to an increase in inter-ISP traffic under certain circumstances. The described model is thus an important tool for ISPs to decide for which overlay networks caches are useful and to dimension them. Chapter 5 summarizes the content of the work and emphasizes the importance of the findings. In addition, it explains how the findings can be applied to the optimization of future overlay networks. Special attention is given to the growing importance of video-on-demand and real-time video transmissions.},
  subject      = {Leistungsbewertung},
  language  = {en}
}
@phdthesis{Baunach2012,
  author    = {Baunach, Marcel},
  title     = {Advances in Distributed Real-Time Sensor/Actuator Systems Operation - Operating Systems, Communication, and Application Design Concepts -},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-76489},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2012},
  abstract  = {This work takes a close look at several quite different research areas related to the design of networked embedded sensor/actuator systems. The variety of the topics illustrates the potential complexity of current sensor network applications; especially when enriched with actuators for proactivity and environmental interaction. Besides their conception, development, installation and long-term operation, we'll mainly focus on more "low-level" aspects: Compositional hardware and software design, task cooperation and collaboration, memory management, and real-time operation will be addressed from a local node perspective. In contrast, inter-node synchronization, communication, as well as sensor data acquisition, aggregation, and fusion will be discussed from a rather global network view. The diversity in the concepts was intentionally accepted to finally facilitate the reliable implementation of truly complex systems. In particular, these should go beyond the usual "sense and transmit of sensor data", but show how powerful today's networked sensor/actuator systems can be despite of their low computational performance and constrained hardware: If their resources are only coordinated efficiently!},
  subject      = {Eingebettetes System},
  language  = {en}
}
@phdthesis{Schmidt2011,
  author    = {Schmidt, Marco},
  title     = {Ground Station Networks for Efficient Operation of Distributed Small Satellite Systems},
  isbn      = {978-3-923959-77-8},
  doi       = {10.25972/OPUS-4984},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-64999},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2011},
  abstract  = {The field of small satellite formations and constellations attracted growing attention, based on recent advances in small satellite engineering. The utilization of distributed space systems allows the realization of innovative applications and will enable improved temporal and spatial resolution in observation scenarios. On the other side, this new paradigm imposes a variety of research challenges. In this monograph new networking concepts for space missions are presented, using networks of ground stations. The developed approaches combine ground station resources in a coordinated way to achieve more robust and efficient communication links. Within this thesis, the following topics were elaborated to improve the performance in distributed space missions: Appropriate scheduling of contact windows in a distributed ground system is a necessary process to avoid low utilization of ground stations. The theoretical basis for the novel concept of redundant scheduling was elaborated in detail. Additionally to the presented algorithm was a scheduling system implemented, its performance was tested extensively with real world scheduling problems. In the scope of data management, a system was developed which autonomously synchronizes data frames in ground station networks and uses this information to detect and correct transmission errors. The system was validated with hardware in the loop experiments, demonstrating the benefits of the developed approach.},
  subject      = {Kleinsatellit},
  language  = {en}
}