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Historical maps are fascinating documents and a valuable source of information for scientists of various disciplines. Many of these maps are available as scanned bitmap images, but in order to make them searchable in useful ways, a structured representation of the contained information is desirable.
This book deals with the extraction of spatial information from historical maps. This cannot be expected to be solved fully automatically (since it involves difficult semantics), but is also too tedious to be done manually at scale.
The methodology used in this book combines the strengths of both computers and humans: it describes efficient algorithms to largely automate information extraction tasks and pairs these algorithms with smart user interactions to handle what is not understood by the algorithm. The effectiveness of this approach is shown for various kinds of spatial documents from the 16th to the early 20th century.
The success of semantic systems has been proven over the last years.
Nowadays, Linked Data is the driver for the rapid development of ever new intelligent systems.
Especially in enterprise environments semantic systems successfully support more and more business processes.
This is especially true for after sales service in the mechanical engineering domain.
Here, service technicians need effective access to relevant technical documentation in order to diagnose and solve problems and defects.
Therefore, the usage of semantic information retrieval systems has become the new system metaphor.
Unlike classical retrieval software Linked Enterprise Data graphs are exploited to grant targeted and problem-oriented access to relevant documents.
However, huge parts of legacy technical documents have not yet been integrated into Linked Enterprise Data graphs.
Additionally, a plethora of information models for the semantic representation of technical information exists.
The semantic maturity of these information models can hardly be measured.
This thesis motivates that there is an inherent need for a self-contained semantification approach for technical documents.
This work introduces a maturity model that allows to quickly assess existing documentation.
Additionally, the approach comprises an abstracting semantic representation for technical documents that is aligned to all major standard information models.
The semantic representation combines structural and rhetorical aspects to provide access to so called Core Documentation Entities.
A novel and holistic semantification process describes how technical documents in different legacy formats can be transformed to a semantic and linked representation.
The practical significance of the semantification approach depends on tools supporting its application.
This work presents an accompanying tool chain of semantification applications, especially the semantification framework CAPLAN that is a highly integrated development and runtime environment for semantification processes.
The complete semantification approach is evaluated in four real-life projects: in a spare part augmentation project, semantification projects for earth moving technology and harvesting technology, as well as an ontology population project for special purpose vehicles.
Three additional case studies underline the broad applicability of the presented ideas.
Given points in the plane, connect them using minimum ink. Though the task seems simple, it turns out to be very time consuming. In fact, scientists believe that computers cannot efficiently solve it. So, do we have to resign? This book examines such NP-hard network-design problems, from connectivity problems in graphs to polygonal drawing problems on the plane. First, we observe why it is so hard to optimally solve these problems. Then, we go over to attack them anyway. We develop fast algorithms that find approximate solutions that are very close to the optimal ones. Hence, connecting points with slightly more ink is not hard.
This dissertation focuses on the performance evaluation of all components of Software Defined Networking (SDN) networks and covers whole their architecture. First, the isolation between virtual networks sharing the same physical resources is investigated with SDN switches of several vendors. Then, influence factors on the isolation are identified and evaluated. Second, the impact of control mechanisms on the performance of the data plane is examined through the flow rule installation time of SDN switches with different controllers. It is shown that both hardware-specific and controller instance have a specific influence on the installation time. Finally, several traffic flow monitoring methods of an SDN controller are investigated and a new monitoring approach is developed and evaluated. It is confirmed that the proposed method allows monitoring of particular flows as well as consumes fewer resources than the standard approach. Based on findings in this thesis, on the one hand, controller developers can refer to the work related to the control plane, such as flow monitoring or flow rule installation, to improve the performance of their applications. On the other hand, network administrators can apply the presented methods to select a suitable combination of controller and switches in their SDN networks, based on their performance requirements
In this thesis various aspects of Quality of Experience (QoE) research are examined. The work is divided into three major blocks: QoE Assessment, QoE Monitoring, and VNF Performance Evaluation. First, prominent cloud applications such as Google Docs and a cloud-based photo album are explored. The QoE is characterized and the influence of packet loss and delay is studied. Afterwards, objective QoE monitoring for HTTP Adaptive Video Streaming (HAS) in the cloud is investigated. Additionally, by using a Virtual Network Function (VNF) for QoE monitoring in the cloud, the feasibility of an interworking of Network Function Virtualization (NFV) and cloud paradigm is evaluated. To this end, a VNF that exploits deep packet inspection technique was used to parse the video traffic. An algorithm is then designed accordingly to estimate video quality and QoE based on network and application layer parameters. To assess the accuracy of the estimation, the VNF is measured in different scenarios under different network QoS and the virtual environment of the cloud architecture. The insights show that the different geographical deployments of the VNF influence the accuracy of the video quality and QoE estimation. Various Service Function Chain (SFC) placement algorithms have been proposed and compared in the context of edge cloud networks. On the one hand, this research is aimed at cloud service providers by providing methods for evaluating QoE for cloud applications. On the other hand, network operators can learn the pitfalls and disadvantages of using the NFV paradigm for such a QoE monitoring mechanism.
A key functionality of cloud systems are automated resource management mechanisms at the infrastructure level. As part of this, elastic scaling of allocated resources is realized by so-called auto-scalers that are supposed to match the current demand in a way that the performance remains stable while resources are efficiently used.
The process of rating cloud infrastructure offerings in terms of the quality of their achieved elastic scaling remains undefined. Clear guidance for the selection and configuration of an auto-scaler for a given context is not available. Thus, existing operating solutions are optimized in a highly application specific way and usually kept undisclosed.
The common state of practice is the use of simplistic threshold-based approaches. Due to their reactive nature they incur performance degradation during the minutes of provisioning delays. In the literature, a high-number of auto-scalers has been proposed trying to overcome the limitations of reactive mechanisms by employing proactive prediction methods.
In this thesis, we identify potentials in automated cloud system resource management and its evaluation methodology. Specifically, we make the following contributions:
We propose a descriptive load profile modeling framework together with automated model extraction from recorded traces to enable reproducible workload generation with realistic load intensity variations. The proposed Descartes Load Intensity Model (DLIM) with its Limbo framework provides key functionality to stress and benchmark resource management approaches in a representative and fair manner.
We propose a set of intuitive metrics for quantifying timing, stability and accuracy aspects of elasticity. Based on these metrics, we propose a novel approach for benchmarking the elasticity of Infrastructure-as-a-Service (IaaS) cloud platforms independent of the performance exhibited by the provisioned underlying resources.
We tackle the challenge of reducing the risk of relying on a single proactive auto-scaler by proposing a new self-aware auto-scaling mechanism, called Chameleon, combining multiple different proactive methods coupled with a reactive fallback mechanism.
Chameleon employs on-demand, automated time series-based forecasting methods to predict the arriving load intensity in combination with run-time service demand estimation techniques to calculate the required resource consumption per work unit without the need for a detailed application instrumentation. It can also leverage application knowledge by solving product-form queueing networks used to derive optimized scaling actions. The Chameleon approach is first in resolving conflicts between reactive and proactive scaling decisions in an intelligent way.
We are confident that the contributions of this thesis will have a long-term impact on the way cloud resource management approaches are assessed. While this could result in an improved quality of autonomic management algorithms, we see and discuss arising challenges for future research in cloud resource management and its assessment methods: The adoption of containerization on top of virtual machine instances introduces another level of indirection. As a result, the nesting of virtual resources increases resource fragmentation and causes unreliable provisioning delays. Furthermore, virtualized compute resources tend to become more and more inhomogeneous associated with various priorities and trade-offs. Due to DevOps practices, cloud hosted service updates are released with a higher frequency which impacts the dynamics in user behavior.
Almost once a week broadcasts about earthquakes, hurricanes, tsunamis, or forest fires are filling the news. While oneself feels it is hard to watch such news, it is even harder for rescue troops to enter such areas. They need some skills to get a quick overview of the devastated area and find victims. Time is ticking, since the chance for survival shrinks the longer it takes till help is available. To coordinate the teams efficiently, all information needs to be collected at the command center. Therefore, teams investigate the destroyed houses and hollow spaces for victims. Doing so, they never can be sure that the building will not fully collapse while they
are inside. Here, rescue robots are welcome helpers, as they are replaceable and make work more secure. Unfortunately, rescue robots are not usable off-the-shelf, yet.
There is no doubt, that such a robot has to fulfil essential requirements to successfully accomplish a rescue mission. Apart from the mechanical requirements it has to be able to build
a 3D map of the environment. This is essential to navigate through rough terrain and fulfil manipulation tasks (e.g. open doors). To build a map and gather environmental information, robots are equipped with multiple sensors. Since laser scanners produce precise measurements and support a wide scanning range, they are common visual sensors utilized for mapping.
Unfortunately, they produce erroneous measurements when scanning transparent (e.g. glass, transparent plastic) or specular reflective objects (e.g. mirror, shiny metal). It is understood that such objects can be everywhere and a pre-manipulation to prevent their influences is impossible. Using additional sensors also bear risks.
The problem is that these objects are occasionally visible, based on the incident angle of the laser beam, the surface, and the type of object. Hence, for transparent objects, measurements might result from the object surface or objects behind it. For specular reflective objects, measurements might result from the object surface or a mirrored object. These mirrored objects are illustrated behind the surface which is wrong. To obtain a precise map, the surfaces need to
be recognised and mapped reliably. Otherwise, the robot navigates into it and crashes. Further, points behind the surface should be identified and treated based on the object type. Points behind a transparent surface should remain as they represent real objects. In contrast, Points behind a specular reflective surface should be erased. To do so, the object type needs to be classified. Unfortunately, none of the current approaches is capable to fulfil these requirements.
Therefore, the following thesis addresses this problem to detect transparent and specular reflective objects and to identify their influences. To give the reader a start up, the first chapters
describe: the theoretical background concerning propagation of light; sensor systems applied for range measurements; mapping approaches used in this work; and the state-of-the-art concerning detection and identification of transparent and specular reflective objects. Afterwards, the Reflection-Identification-Approach, which is the core of subject thesis is presented. It describes 2D and a 3D implementation to detect and classify such objects. Both are available as ROS-nodes. In the next chapter, various experiments demonstrate the applicability and reliability of these nodes. It proves that transparent and specular reflective objects can be detected and classified. Therefore, a Pre- and Post-Filter module is required in 2D. In 3D, classification is possible solely with the Pre-Filter. This is due to the higher amount of measurements. An
example shows that an updatable mapping module allows the robot navigation to rely on refined maps. Otherwise, two individual maps are build which require a fusion afterwards. Finally, the
last chapter summarizes the results and proposes suggestions for future work.
Understanding human navigation behavior has implications for a wide range of application scenarios. For example, insights into geo-spatial navigation in urban areas can impact city planning or public transport. Similarly, knowledge about navigation on the web can help to improve web site structures or service experience.
In this work, we focus on a hypothesis-driven approach to address the task of understanding human navigation: We aim to formulate and compare ideas — for example stemming from existing theory, literature, intuition, or previous experiments — based on a given set of navigational observations. For example, we may compare whether tourists exploring a city walk “short distances” before taking their next photo vs. they tend to "travel long distances between points of interest", or whether users browsing Wikipedia "navigate semantically" vs. "click randomly".
For this, the Bayesian method HypTrails has recently been proposed. However, while HypTrails is a straightforward and flexible approach, several major challenges remain:
i) HypTrails does not account for heterogeneity (e.g., incorporating differently behaving user groups such as tourists and locals is not possible), ii) HypTrails does not support the user in conceiving novel hypotheses when confronted with a large set of possibly relevant background information or influence factors, e.g., points of interest, popularity of locations, time of the day, or user properties, and finally iii) formulating hypotheses can be technically challenging depending on the application scenario (e.g., due to continuous observations or temporal constraints). In this thesis, we address these limitations by introducing various novel methods and tools and explore a wide range of case studies.
In particular, our main contributions are the methods MixedTrails and SubTrails which specifically address the first two limitations: MixedTrails is an approach for hypothesis comparison that extends the previously proposed HypTrails method to allow formulating and comparing heterogeneous hypotheses (e.g., incorporating differently behaving user groups). SubTrails is a method that supports hypothesis conception by automatically discovering interpretable subgroups with exceptional navigation behavior. In addition, our methodological contributions also include several tools consisting of a distributed implementation of HypTrails, a web application for visualizing geo-spatial human navigation in the context of background information, as well as a system for collecting, analyzing, and visualizing mobile participatory sensing data.
Furthermore, we conduct case studies in many application domains, which encompass — among others — geo-spatial navigation based on photos from the photo-sharing platform Flickr, browsing behavior on the social tagging system BibSonomy, and task choosing behavior on a commercial crowdsourcing platform. In the process, we develop approaches to cope with application specific subtleties (like continuous observations and temporal constraints). The corresponding studies illustrate the variety of domains and facets in which navigation behavior can be studied and, thus, showcase the expressiveness, applicability, and flexibility of our methods. Using these methods, we present new aspects of navigational phenomena which ultimately help to better understand the multi-faceted characteristics of human navigation behavior.
A complete simulation system is proposed that can be used as an educational tool by physicians in training basic skills of Minimally Invasive Vascular Interventions. In the first part, a surface model is developed to assemble arteries having a planar segmentation. It is based on Sweep Surfaces and can be extended to T- and Y-like bifurcations. A continuous force vector field is described, representing the interaction between the catheter and the surface. The computation time of the force field is almost unaffected when the resolution of the artery is increased.
The mechanical properties of arteries play an essential role in the study of the circulatory system dynamics, which has been becoming increasingly important in the treatment of cardiovascular diseases. In Virtual Reality Simulators, it is crucial to have a tissue model that responds in real time. In this work, the arteries are discretized by a two dimensional mesh and the nodes are connected by three kinds of linear springs. Three tissue layers (Intima, Media, Adventitia) are considered and, starting from the stretch-energy density, some of the elasticity tensor components are calculated. The physical model linearizes and homogenizes the material response, but it still contemplates the geometric nonlinearity. In general, if the arterial stretch varies by 1% or less, then the agreement between the linear and nonlinear models is trustworthy.
In the last part, the physical model of the wire proposed by Konings is improved. As a result, a simpler and more stable method is obtained to calculate the equilibrium configuration of the wire. In addition, a geometrical method is developed to perform relaxations. It is particularly useful when the wire is hindered in the physical method because of the boundary conditions. The physical and the geometrical methods are merged, resulting in efficient relaxations. Tests show that the shape of the virtual wire agrees with the experiment. The proposed algorithm allows real-time executions and the hardware to assemble the simulator has a low cost.
Biologically inspired self-organization methods can help to manage the access control to the shared communication medium of Wireless Sensor Networks. One lightweight approach is the primitive of desynchronization, which relies on the periodic transmission of short control messages – similar to the periodical pulses of oscillators. This primitive of desynchronization has already been successfully implemented as MAC protocol for single-hop topologies. Moreover, there are also some concepts of such a protocol formulti-hop topologies available. However, the existing implementations may handle just a certain class of multi-hop topologies or are not robust against topology dynamics. In addition to the sophisticated access control of the sensor nodes of a Wireless Sensor Network in arbitrary multi-hop topologies, the communication protocol has to be lightweight, applicable, and scalable. These characteristics are of particular interest for distributed and randomly deployed networks (e.g., by dropping nodes off an airplane).
In this work we present the development of a self-organizing MAC protocol for dynamic multi-hop topologies. This implies the evaluation of related work, the conception of our new communication protocol based on the primitive of desynchronization as well as its implementation for sensor nodes. As a matter of course, we also analyze our realization with
regard to our specific requirements. This analysis is based on several (simulative as well as real-world) scenarios. Since we are mainly interested in the convergence behavior of our
protocol, we do not focus on the "classical" network issues, like routing behavior or data rate, within this work. Nevertheless, for this purpose we make use of several real-world testbeds, but also of our self-developed simulation framework.
According to the results of our evaluation phase, our self-organizing MAC protocol for WSNs, which is based on the primitive of desynchronization, meets all our demands. In fact, our communication protocol operates in arbitrary multi-hop topologies and copes well with topology dynamics. In this regard, our protocol is the first and only MAC protocol to the best of our knowledge. Moreover, due to its periodic transmission scheme, it may be an appropriate starting base for additional network services, like time synchronization or routing.
Imagine a technology that automatically creates a full 3D thermal model of an environment and detects temperature peaks in it. For better orientation in the model it is enhanced with color information. The current state of the art for analyzing temperature related issues is thermal imaging. It is relevant for energy efficiency but also for securing important infrastructure such as power supplies and temperature regulation systems. Monitoring and analysis of the data for a large building is tedious as stable conditions need to be guaranteed for several hours and detailed notes about the pose and the environment conditions for each image must be taken. For some applications repeated measurements are necessary to monitor changes over time. The analysis of the scene is only possible through expertise and experience.
This thesis proposes a robotic system that creates a full 3D model of the environment with color and thermal information by combining thermal imaging with the technology of terrestrial laser scanning. The addition of a color camera facilitates the interpretation of the data and allows for other application areas. The data from all sensors collected at different positions is joined in one common reference frame using calibration and scan matching. The first part of the thesis deals with 3D point cloud processing with the emphasis on accessing point cloud data efficiently, detecting planar structures in the data and registering multiple point clouds into one common coordinate system. The second part covers the autonomous exploration and data acquisition with a mobile robot with the objective to minimize the unseen area in 3D space. Furthermore, the combination of different modalities, color images, thermal images and point cloud data through calibration is elaborated. The last part presents applications for the the collected data. Among these are methods to detect the structure of building interiors for reconstruction purposes and subsequent detection and classification of windows. A system to project the gathered thermal information back into the scene is presented as well as methods to improve the color information and to join separately acquired point clouds and photo series.
A full multi-modal 3D model contains all the relevant geometric information about the recorded scene and enables an expert to fully analyze it off-site. The technology clears the path for automatically detecting points of interest thereby helping the expert to analyze the heat flow as well as localize and identify heat leaks. The concept is modular and neither limited to achieving energy efficiency nor restricted to the use in combination with a mobile platform. It also finds its application in fields such as archaeology and geology and can be extended by further sensors.
The thesis focuses on Quality of Experience (QoE) of HTTP adaptive video streaming (HAS) and traffic management in access networks to improve the QoE of HAS. First, the QoE impact of adaptation parameters and time on layer was investigated with subjective crowdsourcing studies. The results were used to compute a QoE-optimal adaptation strategy for given video and network conditions. This allows video service providers to develop and benchmark improved adaptation logics for HAS. Furthermore, the thesis investigated concepts to monitor video QoE on application and network layer, which can be used by network providers in the QoE-aware traffic management cycle. Moreover, an analytic and simulative performance evaluation of QoE-aware traffic management on a bottleneck link was conducted. Finally, the thesis investigated socially-aware traffic management for HAS via Wi-Fi offloading of mobile HAS flows. A model for the distribution of public Wi-Fi hotspots and a platform for socially-aware traffic management on private home routers was presented. A simulative performance evaluation investigated the impact of Wi-Fi offloading on the QoE and energy consumption of mobile HAS.
The field of genetics faces a lot of challenges and opportunities in both research and diagnostics due to the rise of next generation sequencing (NGS), a technology that allows to sequence DNA increasingly fast and cheap.
NGS is not only used to analyze DNA, but also RNA, which is a very similar molecule also present in the cell, in both cases producing large amounts of data.
The big amount of data raises both infrastructure and usability problems, as powerful computing infrastructures are required and there are many manual steps in the data analysis which are complicated to execute.
Both of those problems limit the use of NGS in the clinic and research, by producing a bottleneck both computationally and in terms of manpower, as for many analyses geneticists lack the required computing skills.
Over the course of this thesis we investigated how computer science can help to improve this situation to reduce the complexity of this type of analysis.
We looked at how to make the analysis more accessible to increase the number of people that can perform OMICS data analysis (OMICS groups various genomics data-sources).
To approach this problem, we developed a graphical NGS data analysis pipeline aimed at a diagnostics environment while still being useful in research in close collaboration with the Human Genetics Department at the University of Würzburg.
The pipeline has been used in various research papers on covering subjects, including works with direct author participation in genomics, transcriptomics as well as epigenomics.
To further validate the graphical pipeline, a user survey was carried out which confirmed that it lowers the complexity of OMICS data analysis.
We also studied how the data analysis can be improved in terms of computing infrastructure by improving the performance of certain analysis steps.
We did this both in terms of speed improvements on a single computer (with notably variant calling being faster by up to 18 times), as well as with distributed computing to better use an existing infrastructure.
The improvements were integrated into the previously described graphical pipeline, which itself also was focused on low resource usage.
As a major contribution and to help with future development of parallel and distributed applications, for the usage in genetics or otherwise, we also looked at how to make it easier to develop such applications.
Based on the parallel object programming model (POP), we created a Java language extension called POP-Java, which allows for easy and transparent distribution of objects.
Through this development, we brought the POP model to the cloud, Hadoop clusters and present a new collaborative distributed computing model called FriendComputing.
The advances made in the different domains of this thesis have been published in various works specified in this document.
Enterprise applications in virtualized data centers are often subject to time-varying workloads, i.e., the load intensity and request mix change over time, due to seasonal patterns and trends, or unpredictable bursts in user requests. Varying workloads result in frequently changing resource demands to the underlying hardware infrastructure. Virtualization technologies enable sharing and on-demand allocation of hardware resources between multiple applications. In this context, the resource allocations to virtualized applications should be continuously adapted in an elastic fashion, so that "at each point in time the available resources match the current demand as closely as possible" (Herbst el al., 2013). Autonomic approaches to resource management promise significant increases in resource efficiency while avoiding violations of performance and availability requirements during peak workloads.
Traditional approaches for autonomic resource management use threshold-based rules (e.g., Amazon EC2) that execute pre-defined reconfiguration actions when a metric reaches a certain threshold (e.g., high resource utilization or load imbalance). However, many business-critical applications are subject to Service-Level-Objectives defined on an application performance metric (e.g., response time or throughput). To determine thresholds so that the end-to-end application SLO is fulfilled poses a major challenge due to the complex relationship between the resource allocation to an application and the application performance. Furthermore, threshold-based approaches are inherently prone to an oscillating behavior resulting in unnecessary reconfigurations.
In order to overcome the deficiencies of threshold-based
approaches and enable a fully automated approach to dynamically control the resource allocations of virtualized applications, model-based approaches are required that can predict the impact of a reconfiguration on the application performance in advance. However, existing model-based approaches are severely limited in their learning capabilities. They either require complete performance models of the application as input, or use a pre-identified model structure and only learn certain model parameters from empirical data at run-time. The former requires high manual efforts and deep system knowledge to create the performance models. The latter does not provide the flexibility to capture the specifics of complex and heterogeneous system architectures.
This thesis presents a self-aware approach to the resource management in virtualized data centers. In this context, self-aware means that it automatically learns performance models of the application and the virtualized infrastructure and reasons based on these models to autonomically adapt the resource allocations in accordance with given application SLOs. Learning a performance model requires the extraction of the model structure representing the system architecture as well as the estimation of model parameters, such as resource demands. The estimation of resource demands is a key challenge as they cannot be observed directly in most systems.
The major scientific contributions of this thesis are:
- A reference architecture for online model learning in virtualized systems. Our reference architecture is based on a set of model extraction agents. Each agent focuses on specific tasks to automatically create and update model skeletons capturing its local knowledge of the system and collaborates with other agents to extract the structural parts of a global performance model of the system. We define different agent roles in the reference architecture and propose a model-based collaboration mechanism for the agents. The agents may be bundled within virtual appliances and may be tailored to include knowledge about the software stack deployed in a specific virtual appliance.
- An online method for the statistical estimation of resource demands. For a given request processed by an application, the resource time consumed for a specified resource within the system (e.g., CPU or I/O device), referred to as resource demand, is the total average time the resource is busy processing the request. A request could be any unit of work (e.g., web page request, database transaction, batch job) processed by the system. We provide a systematization of existing statistical approaches to resource demand estimation and conduct an extensive experimental comparison to evaluate the accuracy of these approaches. We propose a novel method to automatically select estimation approaches and demonstrate that it increases the robustness and accuracy of the estimated resource demands significantly.
- Model-based controllers for autonomic vertical scaling of virtualized applications. We design two controllers based on online model-based reasoning techniques in order to vertically scale applications at run-time in accordance with application SLOs. The controllers exploit the knowledge from the automatically extracted performance models when determining necessary reconfigurations. The first controller adds and removes virtual CPUs to an application depending on the current demand. It uses a layered performance model to also consider the physical resource contention when determining the required resources. The second controller adapts the resource allocations proactively to ensure the availability of the application during workload peaks and avoid reconfiguration during phases of high workload.
We demonstrate the applicability of our approach in current virtualized environments and show its effectiveness leading to significant increases in resource efficiency and improvements of the application performance and availability under time-varying workloads. The evaluation of our approach is based on two case studies representative of widely used enterprise applications in virtualized data centers. In our case studies, we were able to reduce the amount of required CPU resources by up to 23% and the number of reconfigurations by up to 95% compared to a rule-based approach while ensuring full compliance with application SLO. Furthermore, using workload forecasting techniques we were able to schedule expensive reconfigurations (e.g., changes to the memory size) during phases of load load and thus were able to reduce their impact on application availability by over 80% while significantly improving application performance compared to a reactive controller. The methods and techniques for resource demand estimation and vertical application scaling were developed and evaluated in close collaboration with VMware and Google.
The progress which has been made in semiconductor chip production in recent years enables a multitude of cores on a single die. However, due to further decreasing structure sizes, fault tolerance and energy consumption will represent key challenges. Furthermore, an efficient communication infrastructure is indispensable due to the high parallelism at those systems. The predominant communication system at such highly parallel systems is a Network on Chip (NoC). The focus of this thesis is on NoCs which are based on deflection routing. In this context, contributions are made to two domains, fault tolerance and dimensioning of the optimal link width. Both aspects are essential for the application of reliable, energy efficient, and deflection routing based NoCs.
It is expected that future semiconductor systems have to cope with high fault probabilities. The inherently given high connectivity of most NoC topologies can be exploited to tolerate the breakdown of links and other components. In this thesis, a fault-tolerant router architecture has been developed, which stands out for the deployed interconnection architecture and the method to overcome complex fault situations. The presented simulation results show, all data packets arrive at their destination, even at high fault probabilities. In contrast to routing table based architectures, the hardware costs of the herein presented architecture are lower and, in particular, independent of the number of components in the network.
Besides fault tolerance, hardware costs and energy efficiency are of great importance. The utilized link width has a decisive influence on these aspects. In particular, at deflection routing based NoCs, over- and under-sizing of the link width leads to unnecessary high hardware costs and bad performance, respectively. In the second part of this thesis, the optimal link width at deflection routing based NoCs is investigated. Additionally, a method to reduce the link width is introduced. Simulation and synthesis results show, the herein presented method allows a significant reduction of hardware costs at comparable performance.
Content Delivery Networks (CDNs) are networks that distribute content in the Internet. CDNs are increasingly responsible for the largest share of traffic in the Internet. CDNs distribute popular content to caches in many geographical areas to save bandwidth by avoiding unnecessary multihop retransmission. By bringing the content geographically closer to the user, CDNs also reduce the latency of the services.
Besides end users and content providers, which require high availability of high quality content, CDN providers and Internet Service Providers (ISPs) are interested in an efficient operation of CDNs. In order to ensure an efficient replication of the content, CDN providers have a network of (globally) distributed interconnected datacenters at different points of presence (PoPs). ISPs aim to provide reliable and high speed Internet access. They try to keep the load on the network low and to reduce cost for connectivity with other ISPs.
The increasing number of mobile devices such as smart phones and tablets, high definition video content and high resolution displays result in a continuous growth in mobile traffic. This growth in mobile traffic is further accelerated by newly emerging services, such as mobile live streaming and broadcasting services. The steep increase in mobile traffic is expected to reach by 2018 roughly 60% of total network traffic, the majority of which will be video. To handle the growth in mobile networks, the next generation of 5G mobile networks is designed to have higher access rates and an increased densification of the network infrastructure. With the explosion of access rates and number of base stations the backhaul of wireless networks will become congested.
To reduce the load on the backhaul, the research community suggests installing local caches in gateway routers between the wireless network and the Internet, in base stations of different sizes, and in end-user devices. The local deployment of caches allows keeping the traffic within the ISPs network. The caches are organized in a hierarchy, where caches in the lowest tier are requested first. The request is forwarded to the next tier, if the requested object is not found. Appropriate evaluation methods are required to optimally dimension the caches dependent on the traffic characteristics and the available resources. Additionally methods are necessary that allow performance evaluation of backhaul bandwidth aggregation systems, which further reduce the load on the backhaul.
This thesis analyses CDNs utilizing locally available resources and develops the following evaluations and optimization approaches: Characterization of CDNs and distribution of resources in the Internet, analysis and optimization of hierarchical caching systems with bandwidth constraints and performance evaluation of bandwidth aggregation systems.
Multimodal interfaces (MMIs) are a promising human-computer interaction paradigm.
They are feasible for a wide rang of environments, yet they are especially suited if interactions are spatially and temporally grounded with an environment in which the user is (physically) situated.
Real-time interactive systems (RISs) are technical realizations for situated interaction environments, originating from application areas like virtual reality, mixed reality, human-robot interaction, and computer games.
RISs include various dedicated processing-, simulation-, and rendering subsystems which collectively maintain a real-time simulation of a coherent application state.
They thus fulfil the complex functional requirements of their application areas. Two contradicting principles determine the architecture of RISs: coupling and cohesion.
On the one hand, RIS subsystems commonly use specific data structures for multiple purposes to guarantee performance and rely on close semantic and temporal coupling between each other to maintain consistency.
This coupling is exacerbated if the integration of artificial intelligence (AI) methods is necessary, such as for realizing MMIs.
On the other hand, software qualities like reusability and modifiability call for a decoupling of subsystems and architectural elements with single well-defined purposes, i.e., high cohesion.
Systems predominantly favour performance and consistency over reusability and modifiability to handle this contradiction.
They thus accept low maintainability in general and hindered scientific progress in the long-term.
This thesis presents six semantics-based techniques that extend the established entity-component system (ECS) pattern and pose a solution to this contradiction without sacrificing maintainability: semantic grounding, a semantic entity-component state, grounded actions, semantic queries, code from semantics, and decoupling by semantics.
The extension solves the ECS pattern's runtime type deficit, improves component granularity, facilitates access to entity properties outside a subsystem's component association, incorporates a concept to semantically describe behavior as complement to the state representation, and enables compatibility even between RISs.
The presented reference implementation Simulator X validates the feasibility of the six techniques and may be (re)used by other researchers due to its availability under an open-source licence.
It includes a repertoire of common multimodal input processing steps that showcase the particular adequacy of the six techniques for such processing.
The repertoire adds up to the integrated multimodal processing framework miPro, making Simulator X a RIS platform with explicit MMI support.
The six semantics-based techniques as well as the reference implementation are validated by four expert reviews, multiple proof of concept prototypes, and two explorative studies.
Informal insights gathered throughout the design and development supplement this assessment in the form of lessons learned meant to aid future development in the area.
While teleoperation of technical highly sophisticated systems has already been a wide field of research, especially for space and robotics applications, the automation industry has not yet benefited from its results. Besides the established fields of application, also production lines with industrial robots and the surrounding plant components are in need of being remotely accessible. This is especially critical for maintenance or if an unexpected problem cannot be solved by the local specialists.
Special machine manufacturers, especially robotics companies, sell their technology worldwide. Some factories, for example in emerging economies, lack qualified personnel for repair and maintenance tasks. When a severe failure occurs, an expert of the manufacturer needs to fly there, which leads to long down times of the machine or even the whole production line. With the development of data networks, a huge part of those travels can be omitted, if appropriate teleoperation equipment is provided.
This thesis describes the development of a telemaintenance system, which was established in an active production line for research purposes. The customer production site of Braun in Marktheidenfeld, a factory which belongs to Procter & Gamble, consists of a six-axis cartesian industrial robot by KUKA Industries, a two-component injection molding system and an assembly unit. The plant produces plastic parts for electric toothbrushes.
In the research projects "MainTelRob" and "Bayern.digital", during which this plant was utilised, the Zentrum für Telematik e.V. (ZfT) and its project partners develop novel technical approaches and procedures for modern telemaintenance. The term "telemaintenance" hereby refers to the integration of computer science and communication technologies into the maintenance strategy. It is particularly interesting for high-grade capital-intensive goods like industrial robots. Typical telemaintenance tasks are for example the analysis of a robot failure or difficult repair operations. The service department of KUKA Industries is responsible for the worldwide distributed customers who own more than one robot. Currently such tasks are offered via phone support and service staff which travels abroad. They want to expand their service activities on telemaintenance and struggle with the high demands of teleoperation especially regarding security infrastructure. In addition, the facility in Marktheidenfeld has to keep up with the high international standards of Procter & Gamble and wants to minimize machine downtimes. Like 71.6 % of all German companies, P&G sees a huge potential for early information on their production system, but complains about the insufficient quality and the lack of currentness of data.
The main research focus of this work lies on the human machine interface for all human tasks in a telemaintenance setup. This thesis provides own work in the use of a mobile device in context of maintenance, describes new tools on asynchronous remote analysis and puts all parts together in an integrated telemaintenance infrastructure. With the help of Augmented Reality, the user performance and satisfaction could be raised. A special regard is put upon the situation awareness of the remote expert realized by different camera viewpoints. In detail the work consists of:
- Support of maintenance tasks with a mobile device
- Development and evaluation of a context-aware inspection tool
- Comparison of a new touch-based mobile robot programming device to the former teach pendant
- Study on Augmented Reality support for repair tasks with a mobile device
- Condition monitoring for a specific plant with industrial robot
- Human computer interaction for remote analysis of a single plant cycle
- A big data analysis tool for a multitude of cycles and similar plants
- 3D process visualization for a specific plant cycle with additional virtual information
- Network architecture in hardware, software and network infrastructure
- Mobile device computer supported collaborative work for telemaintenance
- Motor exchange telemaintenance example in running production environment
- Augmented reality supported remote plant visualization for better situation awareness
Modern software is often realized as a modular combination of subsystems for, e. g.,
knowledge management, visualization, verification, or the interaction with users. As
a result, software libraries from possibly different programming languages have to
work together. Even more complex the case is if different programming paradigms
have to be combined. This type of diversification of programming languages and
paradigms in just one software application can only be mastered by mechanisms
for a seamless integration of the involved programming languages. However, the
integration of the common logic programming language Prolog and the popular
object-oriented programming language Java is complicated by various interoperability
problems which stem on the one hand from the paradigmatic gap between the
programming languages, and on the other hand, from the diversity of the available
Prolog systems.
The subject of the thesis is the investigation of novel mechanisms for the integration
of logic programming in Prolog and object–oriented programming in Java. We are
particularly interested in an object–oriented, uniform approach which is not specific
to just one Prolog system. Therefore, we have first identified several important
criteria for the seamless integration of Prolog and Java from the object–oriented
perspective. The main contribution of the thesis is a novel integration framework
called the Connector Architecture for Prolog and Java (CAPJa). The framework is
completely implemented in Java and imposes no modifications to the Java Virtual
Machine or Prolog. CAPJa provides a semi–automated mechanism for the integration
of Prolog predicates into Java. For compact, readable, and object–oriented
queries to Prolog, CAPJa exploits lambda expressions with conditional and relational
operators in Java. The communication between Java and Prolog is based
on a fully automated mapping of Java objects to Prolog terms, and vice versa. In
Java, an extensible system of gateways provides connectivity with various Prolog
system and, moreover, makes any connected Prolog system easily interchangeable,
without major adaption in Java.
This thesis contributes to several issues in the context of SDN and NFV, with an emphasis on performance and management.
The main contributions are guide lines for operators migrating to software-based networks, as well as an analytical model for the packet processing in a Linux system using the Kernel NAPI.
Nowadays, data centers are becoming increasingly dynamic due to the common adoption of virtualization technologies. Systems can scale their capacity on demand by growing and shrinking their resources dynamically based on the current load. However, the complexity and performance of modern data centers is influenced not only by the software architecture, middleware, and computing resources, but also by network virtualization, network protocols, network services, and configuration. The field of network virtualization is not as mature as server virtualization and there are multiple competing approaches and technologies. Performance modeling and prediction techniques provide a powerful tool to analyze the performance of modern data centers. However, given the wide variety of network virtualization approaches, no common approach exists for modeling and evaluating the performance of virtualized networks.
The performance community has proposed multiple formalisms and models for evaluating the performance of infrastructures based on different network virtualization technologies. The existing performance models can be divided into two main categories: coarse-grained analytical models and highly-detailed simulation models. Analytical performance models are normally defined at a high level of abstraction and thus they abstract many details of the real network and therefore have limited predictive power. On the other hand, simulation models are normally focused on a selected networking technology and take into account many specific performance influencing factors, resulting in detailed models that are tightly bound to a given technology, infrastructure setup, or to a given protocol stack.
Existing models are inflexible, that means, they provide a single solution method without providing means for the user to influence the solution accuracy and solution overhead. To allow for flexibility in the performance prediction, the user is required to build multiple different performance models obtaining multiple performance predictions. Each performance prediction may then have different focus, different performance metrics, prediction accuracy, and solving time.
The goal of this thesis is to develop a modeling approach that does not require the user to have experience in any of the applied performance modeling formalisms. The approach offers the flexibility in the modeling and analysis by balancing between: (a) generic character and low overhead of coarse-grained analytical models, and (b) the more detailed simulation models with higher prediction accuracy.
The contributions of this thesis intersect with technologies and research areas, such as: software engineering, model-driven software development, domain-specific modeling, performance modeling and prediction, networking and data center networks, network virtualization, Software-Defined Networking (SDN), Network Function Virtualization (NFV). The main contributions of this thesis compose the Descartes Network Infrastructure (DNI) approach and include:
• Novel modeling abstractions for virtualized network infrastructures. This includes two meta-models that define modeling languages for modeling data center network performance. The DNI and miniDNI meta-models provide means for representing network infrastructures at two different abstraction levels. Regardless of which variant of the DNI meta-model is used, the modeling language provides generic modeling elements allowing to describe the majority of existing and future network technologies, while at the same time abstracting factors that have low influence on the overall performance. I focus on SDN and NFV as examples of modern virtualization technologies.
• Network deployment meta-model—an interface between DNI and other meta- models that allows to define mapping between DNI and other descriptive models. The integration with other domain-specific models allows capturing behaviors that are not reflected in the DNI model, for example, software bottlenecks, server virtualization, and middleware overheads.
• Flexible model solving with model transformations. The transformations enable solving a DNI model by transforming it into a predictive model. The model transformations vary in size and complexity depending on the amount of data abstracted in the transformation process and provided to the solver. In this thesis, I contribute six transformations that transform DNI models into various predictive models based on the following modeling formalisms: (a) OMNeT++ simulation, (b) Queueing Petri Nets (QPNs), (c) Layered Queueing Networks (LQNs). For each of these formalisms, multiple predictive models are generated (e.g., models with different level of detail): (a) two for OMNeT++, (b) two for QPNs, (c) two for LQNs. Some predictive models can be solved using multiple alternative solvers resulting in up to ten different automated solving methods for a single DNI model.
• A model extraction method that supports the modeler in the modeling process by automatically prefilling the DNI model with the network traffic data. The contributed traffic profile abstraction and optimization method provides a trade-off by balancing between the size and the level of detail of the extracted profiles.
• A method for selecting feasible solving methods for a DNI model. The method proposes a set of solvers based on trade-off analysis characterizing each transformation with respect to various parameters such as its specific limitations, expected prediction accuracy, expected run-time, required resources in terms of CPU and memory consumption, and scalability.
• An evaluation of the approach in the context of two realistic systems. I evaluate the approach with focus on such factors like: prediction of network capacity and interface throughput, applicability, flexibility in trading-off between prediction accuracy and solving time. Despite not focusing on the maximization of the prediction accuracy, I demonstrate that in the majority of cases, the prediction error is low—up to 20% for uncalibrated models and up to 10% for calibrated models depending on the solving technique.
In summary, this thesis presents the first approach to flexible run-time performance prediction in data center networks, including network based on SDN. It provides ability to flexibly balance between performance prediction accuracy and solving overhead. The approach provides the following key benefits:
• It is possible to predict the impact of changes in the data center network on the performance. The changes include: changes in network topology, hardware configuration, traffic load, and applications deployment.
• DNI can successfully model and predict the performance of multiple different of network infrastructures including proactive SDN scenarios.
• The prediction process is flexible, that is, it provides balance between the granularity of the predictive models and the solving time. The decreased prediction accuracy is usually rewarded with savings of the solving time and consumption of resources required for solving.
• The users are enabled to conduct performance analysis using multiple different prediction methods without requiring the expertise and experience in each of the modeling formalisms.
The components of the DNI approach can be also applied to scenarios that are not considered in this thesis. The approach is generalizable and applicable for the following examples: (a) networks outside of data centers may be analyzed with DNI as long as the background traffic profile is known; (b) uncalibrated DNI models may serve as a basis for design-time performance analysis; (c) the method for extracting and compacting of traffic profiles may be used for other, non-network workloads as well.
3D point clouds are a de facto standard for 3D documentation and modelling. The advances in laser scanning technology broadens the usability and access to 3D measurement systems. 3D point clouds are used in many disciplines such as robotics, 3D modelling, archeology and surveying. Scanners are able to acquire up to a million of points per second to represent the environment with a dense point cloud. This represents the captured environment with a very high degree of detail. The combination of laser scanning technology with photography adds color information to the point clouds. Thus the environment is represented more realistically. Full 3D models of environments, without any occlusion, require multiple scans. Merging point clouds is a challenging process. This thesis presents methods for point cloud registration based on the panorama images generated from the scans. Image representation of point clouds introduces 2D image processing methods to 3D point clouds. Several projection methods for the generation of panorama maps of point clouds are presented in this thesis. Additionally, methods for point cloud reduction and compression based on the panorama maps are proposed. Due to the large amounts of data generated from the 3D measurement systems these methods are necessary to improve the point cloud processing, transmission and archiving. This thesis introduces point cloud processing methods as a novel framework for the digitisation of archeological excavations. The framework replaces the conventional documentation methods for excavation sites. It employs point clouds for the generation of the digital documentation of an excavation with the help of an archeologist on-site. The 3D point cloud is used not only for data representation but also for analysis and knowledge generation. Finally, this thesis presents an autonomous indoor mobile mapping system. The mapping system focuses on the sensor placement planning method. Capturing a complete environment requires several scans. The sensor placement planning method solves for the minimum required scans to digitise large environments. Combining this method with a navigation system on a mobile robot platform enables it to acquire data fully autonomously. This thesis introduces a novel hole detection method for point clouds to detect obscured parts of a captured environment. The sensor placement planning method selects the next scan position with the most coverage of the obscured environment. This reduces the required number of scans. The navigation system on the robot platform consist of path planning, path following and obstacle avoidance. This guarantees the safe navigation of the mobile robot platform between the scan positions. The sensor placement planning method is designed as a stand alone process that could be used with a mobile robot platform for autonomous mapping of an environment or as an assistant tool for the surveyor on scanning projects.
Virtualization allows the creation of virtual instances of physical devices, such as network and processing units. In a virtualized system, governed by a hypervisor, resources are shared among virtual machines (VMs). Virtualization has been receiving increasing interest as away to reduce costs through server consolidation and to enhance the flexibility of physical infrastructures. Although virtualization provides many benefits, it introduces new security challenges; that is, the introduction of a hypervisor introduces threats since hypervisors expose new attack surfaces.
Intrusion detection is a common cyber security mechanism whose task is to detect malicious activities in host and/or network environments. This enables timely reaction in order to stop an on-going attack, or to mitigate the impact of a security breach. The wide adoption of virtualization has resulted in the increasingly common practice of deploying conventional intrusion detection systems (IDSs), for example, hardware IDS appliances or common software-based IDSs, in designated VMs as virtual network functions (VNFs). In addition, the research and industrial communities have developed IDSs specifically designed to operate in virtualized environments (i.e., hypervisorbased IDSs), with components both inside the hypervisor and in a designated VM. The latter are becoming increasingly common with the growing proliferation of virtualized data centers and the adoption of the cloud computing paradigm, for which virtualization is as a key enabling technology.
To minimize the risk of security breaches, methods and techniques for evaluating IDSs in an accurate manner are essential. For instance, one may compare different IDSs in terms of their attack detection accuracy in order to identify and deploy the IDS that operates optimally in a given environment, thereby reducing the risks of a security breach. However, methods and techniques for realistic and accurate evaluation of the attack detection accuracy of IDSs in virtualized environments (i.e., IDSs deployed as VNFs or hypervisor-based IDSs) are lacking. That is, workloads that exercise the sensors of an evaluated IDS and contain attacks targeting hypervisors are needed. Attacks targeting hypervisors are of high severity since they may result in, for example, altering the hypervisors’s memory and thus enabling the execution of malicious code with hypervisor privileges. In addition, there are no metrics and measurement methodologies
for accurately quantifying the attack detection accuracy of IDSs in virtualized environments with elastic resource provisioning (i.e., on-demand allocation or deallocation of virtualized hardware resources to VMs). Modern hypervisors allow for hotplugging virtual CPUs and memory on the designated VM where the intrusion detection engine of hypervisor-based IDSs, as well as of IDSs deployed as VNFs, typically operates. Resource hotplugging may have a significant impact on the attack detection accuracy of an evaluated IDS, which is not taken into account by existing metrics for quantifying IDS attack detection accuracy. This may lead to inaccurate measurements, which, in turn, may result in the deployment of misconfigured or ill-performing IDSs, increasing
the risk of security breaches.
This thesis presents contributions that span the standard components of any system
evaluation scenario: workloads, metrics, and measurement methodologies. The scientific contributions of this thesis are:
A comprehensive systematization of the common practices and the state-of-theart on IDS evaluation. This includes: (i) a definition of an IDS evaluation design space allowing to put existing practical and theoretical work into a common context in a systematic manner; (ii) an overview of common practices in IDS evaluation reviewing evaluation approaches and methods related to each part of the design space; (iii) and a set of case studies demonstrating how different IDS evaluation approaches are applied in practice. Given the significant amount of existing practical and theoretical work related to IDS evaluation, the presented systematization is beneficial for improving the general understanding of the topic by providing an overview of the current state of the field. In addition, it is beneficial for identifying and contrasting advantages and disadvantages of different IDS evaluation methods and practices, while also helping to identify specific requirements and best practices for evaluating current and future IDSs.
An in-depth analysis of common vulnerabilities of modern hypervisors as well as a set of attack models capturing the activities of attackers triggering these vulnerabilities. The analysis includes 35 representative vulnerabilities of hypercall handlers (i.e., hypercall vulnerabilities). Hypercalls are software traps from a kernel of a VM to the hypervisor. The hypercall interface of hypervisors, among device drivers and VM exit events, is one of the attack surfaces that hypervisors expose. Triggering a hypercall vulnerability may lead to a crash of the hypervisor or to altering the hypervisor’s memory. We analyze the origins
of the considered hypercall vulnerabilities, demonstrate and analyze possible attacks that trigger them (i.e., hypercall attacks), develop hypercall attack models(i.e., systematized activities of attackers targeting the hypercall interface), and discuss future research directions focusing on approaches for securing hypercall interfaces.
A novel approach for evaluating IDSs enabling the generation of workloads that contain attacks targeting hypervisors, that is, hypercall attacks. We propose an approach for evaluating IDSs using attack injection (i.e., controlled execution of attacks during regular operation of the environment where an IDS under test is deployed). The injection of attacks is performed based on attack models that capture realistic attack scenarios. We use the hypercall attack models developed as part of this thesis for injecting hypercall attacks.
A novel metric and measurement methodology for quantifying the attack detection accuracy of IDSs in virtualized environments that feature elastic resource provisioning. We demonstrate how the elasticity of resource allocations in such environments may impact the IDS attack detection accuracy and show that using existing metrics in such environments may lead to practically challenging and inaccurate measurements. We also demonstrate the practical use of the metric we propose through a set of case studies, where we evaluate common conventional IDSs deployed as VNFs.
In summary, this thesis presents the first systematization of the state-of-the-art on IDS evaluation, considering workloads, metrics and measurement methodologies as integral parts of every IDS evaluation approach. In addition, we are the first to examine the hypercall attack surface of hypervisors in detail and to propose an approach using attack injection for evaluating IDSs in virtualized environments. Finally, this thesis presents the first metric and measurement methodology for quantifying the attack detection accuracy of IDSs in virtualized environments that feature elastic resource provisioning.
From a technical perspective, as part of the proposed approach for evaluating IDSsthis thesis presents hInjector, a tool for injecting hypercall attacks. We designed hInjector to enable the rigorous, representative, and practically feasible evaluation of IDSs using attack injection. We demonstrate the application and practical usefulness of hInjector, as well as of the proposed approach, by evaluating a representative hypervisor-based IDS designed to detect hypercall attacks. While we focus on evaluating the capabilities of IDSs to detect hypercall attacks, the proposed IDS evaluation approach can be generalized and applied in a broader context. For example, it may be directly used to also evaluate security mechanisms of hypervisors, such as hypercall access control (AC) mechanisms. It may also be applied to evaluate the capabilities
of IDSs to detect attacks involving operations that are functionally similar to hypercalls,
for example, the input/output control (ioctl) calls that the Kernel-based Virtual Machine (KVM) hypervisor supports. For IDSs in virtualized environments featuring elastic resource provisioning, our approach for injecting hypercall attacks can be applied in combination with the attack detection accuracy metric and measurement methodology we propose. Our approach for injecting hypercall attacks, and our metric and measurement methodology, can also be applied independently beyond the scenarios considered in this thesis. The wide spectrum of security mechanisms in virtualized environments whose evaluation can directly benefit from the contributions of this thesis (e.g., hypervisor-based IDSs, IDSs deployed as VNFs, and AC mechanisms) reflects the practical implication of the thesis.
Operators of Higher Order
(1998)
Motivated by results on interactive proof systems we investigate the computational power of quantifiers applied to well-known complexity classes.
In special, we are interested in existential, universal and probabilistic bounded error quantifiers ranging over words and sets of words, i.e. oracles if we think in a Turing machine model.
In addition to the standard oracle access mechanism, we also consider quantifiers ranging over oracles to which access is restricted in a certain way.
Computer systems have replaced human work-force in many parts of everyday life, but there still exists a large number of tasks that cannot be automated, yet. This also includes tasks, which we consider to be rather simple like the categorization of image content or subjective ratings. Traditionally, these tasks have been completed by designated employees or outsourced to specialized companies. However, recently the crowdsourcing paradigm is more and more applied to complete such human-labor intensive tasks. Crowdsourcing aims at leveraging the huge number of Internet users all around the globe, which form a potentially highly available, low-cost, and easy accessible work-force.
To enable the distribution of work on a global scale, new web-based services emerged, so called crowdsourcing platforms, that act as mediator between employers posting tasks and workers completing tasks. However, the crowdsourcing approach, especially the large anonymous worker crowd, results in two types of challenges. On the one hand, there are technical challenges like the dimensioning of crowdsourcing platform infrastructure or the interconnection of crowdsourcing platforms and machine clouds to build hybrid services. On the other hand, there are conceptual challenges like identifying reliable workers or migrating traditional off-line work to the crowdsourcing environment. To tackle these challenges, this monograph analyzes and models current crowdsourcing systems to optimize crowdsourcing workflows and the underlying infrastructure. First, a categorization of crowdsourcing tasks and platforms is developed to derive generalizable properties. Based on this categorization and an exemplary analysis of a commercial crowdsourcing platform, models for different aspects of crowdsourcing platforms and crowdsourcing mechanisms are developed. A special focus is put on quality assurance mechanisms for crowdsourcing tasks, where the models are used to assess the suitability and costs of existing approaches for different types of tasks. Further, a novel quality assurance mechanism solely based on user-interactions is proposed and its feasibility is shown. The findings from the analysis of existing platforms, the derived models, and the developed quality assurance mechanisms are finally used to derive best practices for two crowdsourcing use-cases, crowdsourcing-based network measurements and crowdsourcing-based subjective user studies. These two exemplary use-cases cover aspects typical for a large range of crowdsourcing tasks and illustrated the potential benefits, but also resulting challenges when using crowdsourcing.
With the ongoing digitalization and globalization of the labor markets, the crowdsourcing paradigm is expected to gain even more importance in the next years. This is already evident in the currently new emerging fields of crowdsourcing, like enterprise crowdsourcing or mobile crowdsourcing. The models developed in the monograph enable platform providers to optimize their current systems and employers to optimize their workflows to increase their commercial success. Moreover, the results help to improve the general understanding of crowdsourcing systems, a key for identifying necessary adaptions and future improvements.
Software frameworks for Realtime Interactive Systems (RIS), e.g., in the areas of Virtual, Augmented, and Mixed Reality (VR, AR, and MR) or computer games, facilitate a multitude of functionalities by coupling diverse software modules. In this context, no uniform methodology for coupling these modules does exist; instead various purpose-built solutions have been proposed. As a consequence, important software qualities, such as maintainability, reusability, and adaptability, are impeded.
Many modern systems provide additional support for the integration of Artificial Intelligence (AI) methods to create so called intelligent virtual environments. These methods exacerbate the above-mentioned problem of coupling software modules in the thus created Intelligent Realtime Interactive Systems (IRIS) even more. This, on the one hand, is due to the commonly applied specialized data structures and asynchronous execution schemes, and the requirement for high consistency regarding content-wise coupled but functionally decoupled forms of data representation on the other.
This work proposes an approach to decoupling software modules in IRIS, which is based on the abstraction of architecture elements using a semantic Knowledge Representation Layer (KRL). The layer facilitates decoupling the required modules, provides a means for ensuring interface compatibility and consistency, and in the end constitutes an interface for symbolic AI methods.
Small satellites contribute significantly in the rapidly evolving innovation in space engineering, in particular in distributed space systems for global Earth observation and communication services. Significant mass reduction by miniaturization, increased utilization of commercial high-tech components, and in particular standardization are the key drivers for modern miniature space technology.
This thesis addresses key fields in research and development on miniature satellite technology regarding efficiency, flexibility, and robustness. Here, these challenges are addressed by the University of Wuerzburg’s advanced pico-satellite bus, realizing a generic modular satellite architecture and standardized interfaces for all subsystems. The modular platform ensures reusability, scalability, and increased testability due to its flexible subsystem interface which allows efficient and compact integration of the entire satellite in a plug-and-play manner.
Beside systematic design for testability, a high degree of operational robustness is achieved by the consequent implementation of redundancy of crucial subsystems. This is combined with efficient fault detection, isolation and recovery mechanisms. Thus, the UWE-3 platform, and in particular the on-board data handling system and the electrical power system, offers one of the most efficient pico-satellite architectures launched in recent years and provides a solid basis for future extensions.
The in-orbit performance results of the pico-satellite UWE-3 are presented and summarize successful operations since its launch in 2013. Several software extensions and adaptations have been uploaded to UWE-3 increasing its capabilities. Thus, a very flexible platform for in-orbit software experiments and for evaluations of innovative concepts was provided and tested.
Today's Internet is no longer only controlled by a single stakeholder, e.g. a standard body or a telecommunications company.
Rather, the interests of a multitude of stakeholders, e.g. application developers, hardware vendors, cloud operators, and network operators, collide during the development and operation of applications in the Internet.
Each of these stakeholders considers different KPIs to be important and attempts to optimise scenarios in its favour.
This results in different, often opposing views and can cause problems for the complete network ecosystem.
One example of such a scenario are Signalling Storms in the mobile Internet, with one of the largest occurring in Japan in 2012 due to the release and high popularity of a free instant messaging application.
The network traffic generated by the application caused a high number of connections to the Internet being established and terminated.
This resulted in a similarly high number of signalling messages in the mobile network, causing overload and a loss of service for 2.5 million users over 4 hours.
While the network operator suffers the largest impact of this signalling overload, it does not control the application.
Thus, the network operator can not change the application traffic characteristics to generate less network signalling traffic.
The stakeholders who could prevent, or at least reduce, such behaviour, i.e. application developers or hardware vendors, have no direct benefit from modifying their products in such a way.
This results in a clash of interests which negatively impacts the network performance for all participants.
The goal of this monograph is to provide an overview over the complex structures of stakeholder relationships in today's Internet applications in mobile networks.
To this end, we study different scenarios where such interests clash and suggest methods where tradeoffs can be optimised for all participants.
If such an optimisation is not possible or attempts at it might lead to adverse effects, we discuss the reasons.
Graphs are a frequently used tool to model relationships among entities. A graph is a binary relation between objects, that is, it consists of a set of objects (vertices) and a set of pairs of objects (edges).
Networks are common examples of modeling data as a graph. For example, relationships between persons in a social network, or network links between computers in a telecommunication network can be represented by a graph.
The clearest way to illustrate the modeled data is to visualize the graphs. The field of Graph Drawing deals with the problem of finding algorithms to automatically generate graph visualizations. The task is to find a "good" drawing, which can be measured by different criteria such as number of crossings between edges or the used area. In this thesis, we study Angular Schematization in Graph Drawing. By this, we mean drawings
with large angles (for example, between the edges at common vertices or at crossing points).
The thesis consists of three parts. First, we deal with the placement of boxes. Boxes are axis-parallel rectangles that can, for example, contain text.
They can be placed on a map to label important sites, or can be used to describe semantic relationships between words in a word network. In the second part of the thesis, we consider graph drawings visually guide the
viewer. These drawings generally induce large angles between edges that meet at a vertex. Furthermore, the edges are drawn crossing-free and in a way that
makes them easy to follow for the human eye. The third and final part is devoted to crossings with large angles. In drawings with crossings, it is important to have large angles between edges at their crossing point, preferably right angles.
Social interactions as introduced by Web 2.0 applications during the last decade have changed the way the Internet is used. Today, it is part of our daily lives to maintain contacts through social networks, to comment on the latest developments in microblogging services or to save and share information snippets such as photos or bookmarks online.
Social bookmarking systems are part of this development. Users can share links to interesting web pages by publishing bookmarks and providing descriptive keywords for them. The structure which evolves from the collection of annotated bookmarks is called a folksonomy. The sharing of interesting and relevant posts enables new ways of retrieving information from the Web. Users
can search or browse the folksonomy looking at resources related to specific tags or users. Ranking methods known from search engines have been adjusted to facilitate retrieval in social bookmarking systems. Hence, social bookmarking systems have become an alternative or addendum to search engines.
In order to better understand the commonalities and differences of social bookmarking systems and search engines, this thesis compares several aspects of the two systems' structure, usage behaviour and content. This includes the use of tags and query terms, the composition of the document collections and the rankings of bookmarks and search engine URLs. Searchers (recorded via session ids), their search terms and the clicked on URLs can be extracted from a search
engine query logfile. They form similar links as can be found in folksonomies where a user annotates a resource with tags. We use this analogy to build a tripartite hypergraph from query logfiles (a logsonomy), and compare structural and semantic properties of log- and folksonomies. Overall, we have found similar behavioural, structural and semantic characteristics in both systems. Driven by this insight, we investigate, if folksonomy data can be of use in web
information retrieval in a similar way to query log data: we construct training data from query logs and a folksonomy to build models for a learning-to-rank algorithm. First experiments show a positive correlation of ranking results generated from the ranking models of both systems. The research is based on various data collections from the social bookmarking systems BibSonomy and Delicious, Microsoft's search engine MSN (now Bing) and Google data.
To maintain social bookmarking systems as a good source for information retrieval, providers need to fight spam. This thesis introduces and analyses different features derived from the specific characteristics of social bookmarking systems to be used in spam detection classification algorithms. Best results can be derived from a combination of profile, activity, semantic and location-based features. Based on the experiments, a spam detection framework which identifies and eliminates spam activities for the social bookmarking system BibSonomy has been developed.
The storing and publication of user-related bookmarks and profile information raises questions about user data privacy. What kinds of personal information is collected and how do systems handle user-related items? In order to answer these questions, the thesis looks into the handling of data privacy in the social bookmarking system BibSonomy. Legal guidelines about how to deal with the private data collected and processed in social bookmarking systems are also presented. Experiments will show that the consideration of user data privacy in the process
of feature design can be a first step towards strengthening data privacy.
Within this thesis a new philosophy in monitoring spacecrafts is presented: the
unification of the various kinds of monitoring techniques used during the
different lifecylce phases of a spacecraft.
The challenging requirements being set for this monitoring framework are:
- "separation of concerns" as a design principle (dividing the steps of logging
from registered sources, sending to connected sinks and displaying of
information),
- usage during all mission phases,
- usage by all actors (EGSE engineers, groundstation operators, etc.),
- configurable at runtime, especially regarding the level of detail of logging
information, and
- very low resource consumption.
First a prototype of the monitoring framework was developed as a support library
for the real-time operating system
RODOS. This prototype was tested on dedicated hardware platforms relevant for
space, and also on a satellite demonstrator used for educational purposes.
As a second step, the results and lessons learned from the development and usage
of this prototype were transfered to a real space mission: the first satellite
of the DLR compact satellite series - a space based platform for DLR's own
research activities. Within this project, the software of the avionic subsystem
was supplemented by a powerful logging component, which enhances the traditional
housekeeping capabilities and offers extensive filtering and debugging
techniques for monitoring and FDIR needs. This logging component is the major
part of the flight version of the monitoring framework. It is completed by
counterparts running on the development computers and as well as the EGSE
hardware in the integration room, making it most valuable already in the
earliest stages of traditional spacecraft development.
Future plans in terms of adding support from the groundstation as well will lead
to a seamless integration of the monitoring framework not only into to the
spacecraft itself, but into the whole space system.
The general map-labeling problem is as follows: given a set of geometric objects to be labeled, or features, in the plane, and for each feature a set of label positions, maximize the number of placed labels such that there is at most one label per feature and no two labels overlap. There are three types of features in a map: point, line, and area features. Unfortunately, one cannot expect to find efficient algorithms that solve the labeling problem optimally.
Interactive maps are digital maps that only show a small part of the entire map whereas the user can manipulate the shown part, the view, by continuously panning, zooming, rotating, and tilting (that is, changing the perspective between a top and a bird view). An example for the application of interactive maps is in navigational devices. Interactive maps are challenging in that the labeling must be updated whenever labels leave the view and, while zooming, the label size must be constant on the screen (which either makes space for further labels or makes labels overlap when zooming in or out, respectively). These updates must be computed in real time, that is, the computation must be so fast that the user does not notice that we spend time on the computation. Additionally, labels must not jump or flicker, that is, labels must not suddenly change their positions or, while zooming out, a vanished label must not appear again.
In this thesis, we present efficient algorithms that dynamically label point and line features in interactive maps. We try to label as many features as possible while we prohibit labels that overlap, jump, and flicker. We have implemented all our approaches and tested them on real-world data. We conclude that our algorithms are indeed real-time capable.
Knowledge-based systems (KBS) face an ever-increasing interest in various disciplines and contexts. Yet, the former aim to construct the ’perfect intelligent software’ continuously shifts to user-centered, participative solutions. Such systems enable users to contribute their personal knowledge to the problem solving process for increased efficiency and an ameliorated user experience. More precisely, we define non-functional key requirements of participative KBS as: Transparency (encompassing KBS status mediation), configurability (user adaptability, degree of user control/exploration), quality of the KB and UI, and evolvability (enabling the KBS to grow mature with their users). Many of those requirements depend on the respective target users, thus calling for a more user-centered development. Often, also highly expertise domains are targeted — inducing highly complex KBs — which requires a more careful and considerate UI/interaction design. Still, current KBS engineering (KBSE) approaches mostly focus on knowledge acquisition (KA) This often leads to non-optimal, little reusable, and non/little evaluated KBS front-end solutions.
In this thesis we propose a more encompassing KBSE approach. Due to the strong mutual influences between KB and UI, we suggest a novel form of intertwined UI and KB development. We base the approach on three core components for encompassing KBSE:
(1) Extensible prototyping, a tailored form of evolutionary prototyping; this builds on mature UI prototypes and offers two extension steps for the anytime creation of core KBS prototypes (KB + core UI) and fully productive KBS (core KBS prototype + common framing functionality). (2) KBS UI patterns, that define reusable solutions for the core KBS UI/interaction; we provide a basic collection of such patterns in this work. (3) Suitable usability instruments for the assessment of the KBS artifacts. Therewith, we do not strive for ’yet another’ self-contained KBS engineering methodology. Rather, we motivate to extend existing approaches by the proposed key components. We demonstrate this based on an agile KBSE model.
For practical support, we introduce the tailored KBSE tool ProKEt. ProKEt offers a basic selection of KBS core UI patterns and corresponding configuration options out of the box; their further adaption/extension is possible on various levels of expertise. For practical usability support, ProKEt offers facilities for quantitative and qualitative data collection. ProKEt explicitly fosters the suggested, intertwined development of UI and KB. For seamlessly integrating KA activities, it provides extension points for two selected external KA tools: For KnowOF, a standard office based KA environment. And for KnowWE, a semantic wiki for collaborative KA. Therewith, ProKEt offers powerful support for encompassing, user-centered KBSE.
Finally, based on the approach and the tool, we also developed a novel KBS type: Clarification KBS as a mashup of consultation and justification KBS modules. Those denote a specifically suitable realization for participative KBS in highly expertise contexts and consequently require a specific design. In this thesis, apart from more common UI solutions, we particularly also introduce KBS UI patterns especially tailored towards Clarification KBS.
At the center of the Internet’s protocol stack stands the Internet Protocol (IP) as a common denominator that enables all communication. To make routing efficient, resilient, and scalable, several aspects must be considered. Care must be taken that traffic is well balanced to make efficient use of the existing network resources, both in failure free operation and in failure scenarios.
Finding the optimal routing in a network is an NP-complete problem. Therefore, routing optimization is usually performed using heuristics. This dissertation shows that a routing optimized with one objective function is often not good when looking at other objective functions. It can even be worse than unoptimized routing with respect to that objective function. After looking at failure-free routing and traffic distribution in different failure scenarios, the analysis is extended to include the loop-free alternate (LFA) IP fast reroute mechanism. Different application scenarios of LFAs are examined and a special focus is set on the fact that LFAs usually cannot protect all traffic in a network even against single link failures. Thus, the routing optimization for LFAs is targeted on both link utilization and failure coverage. Finally, the pre-congestion notification mechanism PCN for network admission control and overload protection is analyzed and optimized. Different design options for implementing the protocol are compared, before algorithms are developed for the calculation and optimization of protocol parameters and PCN-based routing.
The second part of the thesis tackles a routing problem that can only be resolved on a global scale. The scalability of the Internet is at risk since a major and intensifying growth of the interdomain routing tables has been observed. Several protocols and architectures are analyzed that can be used to make interdomain routing more scalable. The most promising approach is the locator/identifier (Loc/ID) split architecture which separates routing from host identification. This way, changes in connectivity, mobility of end hosts, or traffic-engineering activities are hidden from the routing in the core of the Internet and the routing tables can be kept much smaller. All of the currently proposed Loc/ID split approaches have their downsides. In particular, the fact that most architectures use the ID for routing outside the Internet’s core is a poor design, which inhibits many of the possible features of a new routing architecture. To better understand the problems and to provide a solution for a scalable routing design that implements a true Loc/ID split, the new GLI-Split protocol is developed in this thesis, which provides separation of global and local routing and uses an ID that is independent from any routing decisions.
Besides GLI-Split, several other new routing architectures implementing Loc/ID split have been proposed for the Internet. Most of them assume that a mapping system is queried for EID-to-RLOC mappings by an intermediate node at the border of an edge network. When the mapping system is queried by an intermediate node, packets are already on their way towards their destination, and therefore, the mapping system must be fast, scalable, secure, resilient, and should be able to relay packets without locators to nodes that can forward them to the correct destination. The dissertation develops a classification for all proposed mapping system architectures and shows their similarities and differences. Finally, the fast two-level mapping system FIRMS is developed. It includes security and resilience features as well as a relay service for initial packets of a flow when intermediate nodes encounter a cache miss for the EID-to-RLOC mapping.
Large volumes of data are collected today in many domains. Often, there is so much data available, that it is difficult to identify the relevant pieces of information. Knowledge discovery seeks to obtain novel, interesting and useful information from large datasets.
One key technique for that purpose is subgroup discovery. It aims at identifying descriptions for subsets of the data, which have an interesting distribution with respect to a predefined target concept. This work improves the efficiency and effectiveness of subgroup discovery in different directions.
For efficient exhaustive subgroup discovery, algorithmic improvements are proposed for three important variations of the standard setting: First, novel optimistic estimate bounds are derived for subgroup discovery with numeric target concepts. These allow for skipping the evaluation of large parts of the search space without influencing the results. Additionally, necessary adaptations to data structures for this setting are discussed. Second, for exceptional model mining, that is, subgroup discovery with a model over multiple attributes as target concept, a generic extension of the well-known FP-tree data structure is introduced. The modified data structure stores intermediate condensed data representations, which depend on the chosen model class, in the nodes of the trees. This allows the application for many popular model classes. Third, subgroup discovery with generalization-aware measures is investigated.
These interestingness measures compare the target share or mean value in the subgroup with the respective maximum value in all its generalizations. For this setting, a novel method for deriving optimistic estimates is proposed. In contrast to previous approaches, the novel measures are not exclusively based on the anti-monotonicity of instance coverage, but also takes the difference of coverage between the subgroup and its generalizations into account. In all three areas, the advances lead to runtime improvements of more than an order of magnitude.
The second part of the contributions focuses on the \emph{effectiveness} of subgroup discovery. These improvements aim to identify more interesting subgroups in practical applications. For that purpose, the concept of expectation-driven subgroup discovery is introduced as a new family of interestingness measures. It computes the score of a subgroup based on the difference between the actual target share and the target share that could be expected given the statistics for the separate influence factors that are combined to describe the subgroup.
In doing so, previously undetected interesting subgroups are discovered, while other, partially redundant findings are suppressed.
Furthermore, this work also approaches practical issues of subgroup discovery: In that direction, the VIKAMINE II tool is presented, which extends its predecessor with a rebuild user interface, novel algorithms for automatic discovery, new interactive mining techniques, as well novel options for result presentation and introspection. Finally, some real-world applications are described that utilized the presented techniques. These include the identification of influence factors on the success and satisfaction of university students and the description of locations using tagging data of geo-referenced images.
Performance Assessment of Resource Management Strategies for Cellular and Wireless Mesh Networks
(2015)
The rapid growth in the field of communication networks has been truly amazing in the last decades. We are currently experiencing a continuation thereof with an increase in traffic and the emergence of new fields of application. In particular, the latter is interesting since due to advances in the networks and new devices, such as smartphones, tablet PCs, and all kinds of Internet-connected devices, new additional applications arise from different areas. What applies for all these services is that they come from very different directions and belong to different user groups. This results in a very heterogeneous application mix with different requirements and needs on the access networks.
The applications within these networks typically use the network technology as a matter of course, and expect that it works in all situations and for all sorts of purposes without any further intervention. Mobile TV, for example, assumes that the cellular networks support the streaming of video data. Likewise, mobile-connected electricity meters rely on the timely transmission of accounting data for electricity billing. From the perspective of the communication networks, this requires not only the technical realization for the individual case, but a broad consideration of all circumstances and all requirements of special devices and applications of the users.
Such a comprehensive consideration of all eventualities can only be achieved by a dynamic, customized, and intelligent management of the transmission resources. This management requires to exploit the theoretical capacity as much as possible while also taking system and network architecture as well as user and application demands into account. Hence, for a high level of customer satisfaction, all requirements of the customers and the applications need to be considered, which requires a multi-faceted resource management.
The prerequisite for supporting all devices and applications is consequently a holistic resource management at different levels. At the physical level, the technical possibilities provided by different access technologies, e.g., more transmission antennas, modulation and coding of data, possible cooperation between network elements, etc., need to be exploited on the one hand. On the other hand, interference and changing network conditions have to be counteracted at physical level. On the application and user level, the focus should be on the customer demands due to the currently increasing amount of different devices and diverse applications (medical, hobby, entertainment, business, civil protection, etc.).
The intention of this thesis is the development, investigation, and evaluation of a holistic resource management with respect to new application use cases and requirements for the networks. Therefore, different communication layers are investigated and corresponding approaches are developed using simulative methods as well as practical emulation in testbeds. The new approaches are designed with respect to different complexity and implementation levels in order to cover the design space of resource management in a systematic way. Since the approaches cannot be evaluated generally for all types of access networks, network-specific use cases and evaluations are finally carried out in addition to the conceptual design and the modeling of the scenario.
The first part is concerned with management of resources at physical layer. We study distributed resource allocation approaches under different settings. Due to the ambiguous performance objectives, a high spectrum reuse is conducted in current cellular networks. This results in possible interference between cells that transmit on the same frequencies. The focus is on the identification of approaches that are able to mitigate such interference.
Due to the heterogeneity of the applications in the networks, increasingly different application-specific requirements are experienced by the networks. Consequently, the focus is shifted in the second part from optimization of network parameters to consideration and integration of the application and user needs by adjusting network parameters. Therefore, application-aware resource management is introduced to enable efficient and customized access networks.
As indicated before, approaches cannot be evaluated generally for all types of access networks. Consequently, the third contribution is the definition and realization of the application-aware paradigm in different access networks. First, we address multi-hop wireless mesh networks. Finally, we focus with the fourth contribution on cellular networks. Application-aware resource management is applied here to the air interface between user device and the base station. Especially in cellular networks, the intensive cost-driven competition among the different operators facilitates the usage of such a resource management to provide cost-efficient and customized networks with respect to the running applications.
The first part of this thesis deals with the approximability of the traveling salesman problem. This problem is defined on a complete graph with edge weights, and the task is to find a Hamiltonian cycle of minimum weight that visits each vertex exactly once. We study the most important multiobjective variants of this problem. In the multiobjective case, the edge weights are vectors of natural numbers with one component for each objective, and since weight vectors are typically incomparable, the optimal Hamiltonian cycle does not exist. Instead we consider the Pareto set, which consists of those Hamiltonian cycles that are not dominated by some other, strictly better Hamiltonian cycles. The central goal in multiobjective optimization and in the first part of this thesis in particular is the approximation of such Pareto sets.
We first develop improved approximation algorithms for the two-objective metric traveling salesman problem on multigraphs and for related Hamiltonian path problems that are inspired by the single-objective Christofides' heuristic. We further show arguments indicating that our algorithms are difficult to improve. Furthermore we consider multiobjective maximization versions of the traveling salesman problem, where the task is to find Hamiltonian cycles with high weight in each objective. We generalize single-objective techniques to the multiobjective case, where we first compute a cycle cover with high weight and then remove an edge with low weight in each cycle. Since weight vectors are often incomparable, the choice of the edges of low weight is non-trivial. We develop a general lemma that solves this problem and enables us to generalize the single-objective maximization algorithms to the multiobjective case. We obtain improved, randomized approximation algorithms for the multiobjective maximization variants of the traveling salesman problem. We conclude the first part by developing deterministic algorithms for these problems.
The second part of this thesis deals with redundancy properties of complete sets. We call a set autoreducible if for every input instance x we can efficiently compute some y that is different from x but that has the same membership to the set. If the set can be split into two equivalent parts, then it is called weakly mitotic, and if the splitting is obtained by an efficiently decidable separator set, then it is called mitotic. For different reducibility notions and complexity classes, we analyze how redundant its complete sets are.
Previous research in this field concentrates on polynomial-time computable reducibility notions. The main contribution of this part of the thesis is a systematic study of the redundancy properties of complete sets for typical complexity classes and reducibility notions that are computable in logarithmic space. We use different techniques to show autoreducibility and mitoticity that depend on the size of the complexity class and the strength of the reducibility notion considered. For small complexity classes such as NL and P we use self-reducible, complete sets to show that all complete sets are autoreducible. For large complexity classes such as PSPACE and EXP we apply diagonalization methods to show that all complete sets are even mitotic. For intermediate complexity classes such as NP and the remaining levels of the polynomial-time hierarchy we establish autoreducibility of complete sets by locally checking computational transcripts. In many cases we can show autoreducibility of complete sets, while mitoticity is not known to hold. We conclude the second part by showing that in some cases, autoreducibility of complete sets at least implies weak mitoticity.
Context-specific Consistencies in Information Extraction: Rule-based and Probabilistic Approaches
(2015)
Large amounts of communication, documentation as well as knowledge and information are stored in textual documents. Most often, these texts like webpages, books, tweets or reports are only available in an unstructured representation since they are created and interpreted by humans. In order to take advantage of this huge amount of concealed information and to include it in analytic processes, it needs to be transformed into a structured representation. Information extraction considers exactly this task. It tries to identify well-defined entities and relations in unstructured data and especially in textual documents.
Interesting entities are often consistently structured within a certain context, especially in semi-structured texts. However, their actual composition varies and is possibly inconsistent among different contexts. Information extraction models stay behind their potential and return inferior results if they do not consider these consistencies during processing. This work presents a selection of practical and novel approaches for exploiting these context-specific consistencies in information extraction tasks. The approaches direct their attention not only to one technique, but are based on handcrafted rules as well as probabilistic models.
A new rule-based system called UIMA Ruta has been developed in order to provide optimal conditions for rule engineers. This system consists of a compact rule language with a high expressiveness and strong development support. Both elements facilitate rapid development of information extraction applications and improve the general engineering experience, which reduces the necessary efforts and costs when specifying rules.
The advantages and applicability of UIMA Ruta for exploiting context-specific consistencies are illustrated in three case studies. They utilize different engineering approaches for including the consistencies in the information extraction task. Either the recall is increased by finding additional entities with similar composition, or the precision is improved by filtering inconsistent entities. Furthermore, another case study highlights how transformation-based approaches are able to correct preliminary entities using the knowledge about the occurring consistencies.
The approaches of this work based on machine learning rely on Conditional Random Fields, popular probabilistic graphical models for sequence labeling. They take advantage of a consistency model, which is automatically induced during processing the document. The approach based on stacked graphical models utilizes the learnt descriptions as feature functions that have a static meaning for the model, but change their actual function for each document. The other two models extend the graph structure with additional factors dependent on the learnt model of consistency. They include feature functions for consistent and inconsistent entities as well as for additional positions that fulfill the consistencies.
The presented approaches are evaluated in three real-world domains: segmentation of scientific references, template extraction in curricula vitae, and identification and categorization of sections in clinical discharge letters. They are able to achieve remarkable results and provide an error reduction of up to 30% compared to usually applied techniques.
Today knowledge base authoring for the engineering of intelligent systems is performed mainly by using tools with graphical user interfaces. An alternative human-computer interaction para- digm is the maintenance and manipulation of electronic documents, which provides several ad- vantages with respect to the social aspects of knowledge acquisition. Until today it hardly has found any attention as a method for knowledge engineering.
This thesis provides a comprehensive discussion of document-centered knowledge acquisition with knowledge markup languages. There, electronic documents are edited by the knowledge authors and the executable knowledge base entities are captured by markup language expressions within the documents. The analysis of this approach reveals significant advantages as well as new challenges when compared to the use of traditional GUI-based tools.
Some advantages of the approach are the low barriers for domain expert participation, the simple integration of informal descriptions, and the possibility of incremental knowledge for- malization. It therefore provides good conditions for building up a knowledge acquisition pro- cess based on the mixed-initiative strategy, being a flexible combination of direct and indirect knowledge acquisition. Further it turns out that document-centered knowledge acquisition with knowledge markup languages provides high potential for creating customized knowledge au- thoring environments, tailored to the needs of the current knowledge engineering project and its participants. The thesis derives a process model to optimally exploit this customization po- tential, evolving a project specific authoring environment by an agile process on the meta level. This meta-engineering process continuously refines the three aspects of the document space: The employed markup languages, the scope of the informal knowledge, and the structuring and organization of the documents. The evolution of the first aspect, the markup languages, plays a key role, implying the design of project specific markup languages that are easily understood by the knowledge authors and that are suitable to capture the required formal knowledge precisely. The goal of the meta-engineering process is to create a knowledge authoring environment, where structure and presentation of the domain knowledge comply well to the users’ mental model of the domain. In that way, the approach can help to ease major issues of knowledge-based system development, such as high initial development costs and long-term maintenance problems.
In practice, the application of the meta-engineering approach for document-centered knowl- edge acquisition poses several technical challenges that need to be coped with by appropriate tool support. In this thesis KnowWE, an extensible document-centered knowledge acquisition environment is presented. The system is designed to support the technical tasks implied by the meta-engineering approach, as for instance design and implementation of new markup lan- guages, content refactoring, and authoring support. It is used to evaluate the approach in several real-world case-studies from different domains, such as medicine or engineering for instance.
We end the thesis by a summary and point out further interesting research questions consid- ering the document-centered knowledge acquisition approach.
This dissertation presents controller design methodologies for a formation of cooperative mobile robots to perform trajectory tracking and convoy protection tasks. Two major problems related to multi-agent formation control are addressed, namely the time-delay and optimality problems. For the task of trajectory tracking, a leader-follower based system structure is adopted for the controller design, where the selection criteria for controller parameters are derived through analyses of characteristic polynomials. The resulting parameters ensure the stability of the system and overcome the steady-state error as well as the oscillation behavior under time-delay effect. In the convoy protection scenario, a decentralized coordination strategy for balanced deployment of mobile robots is first proposed. Based on this coordination scheme, optimal controller parameters are generated in both centralized and decentralized fashion to achieve dynamic convoy protection in a unified framework, where distributed optimization technique is applied in the decentralized strategy. This unified framework takes into account the motion of the target to be protected, and the desired system performance, for instance, minimal energy to spend, equal inter-vehicle distance to keep, etc.
Both trajectory tracking and convoy protection tasks are demonstrated through simulations and real-world hardware experiments based on the robotic equipment at Department of Computer Science VII, University of Würzburg.
Object six Degrees of Freedom (6DOF) pose estimation is a fundamental problem in many practical robotic applications, where the target or an obstacle with a simple or complex shape can move fast in cluttered environments. In this thesis, a 6DOF pose estimation algorithm is developed based on the fused data from a time-of-flight camera and a color camera. The algorithm is divided into two stages, an annealed particle filter based coarse pose estimation stage and a gradient decent based accurate pose optimization stage. In the first stage, each particle is evaluated with sparse representation. In this stage, the large inter-frame motion of the target can be well handled. In the second stage, the range data based conventional Iterative Closest Point is extended by incorporating the target appearance information and used for calculating the accurate pose by refining the coarse estimate from the first stage. For dealing with significant illumination variations during the tracking, spherical harmonic illumination modeling is investigated and integrated into both stages. The robustness and accuracy of the proposed algorithm are demonstrated through experiments on various objects in both indoor and outdoor environments. Moreover, real-time performance can be achieved with graphics processing unit acceleration.
In this work, a novel method for estimating the relative pose of a known object is presented, which relies on an application-specific data fusion process. A PMD-sensor in conjunction with a CCD-sensor is used to perform the pose estimation. Furthermore, the work provides a method for extending the measurement range of the PMD sensor along with the necessary calibration methodology. Finally, extensive measurements on a very accurate Rendezvous and Docking testbed are made to evaluate the performance, what includes a detailed discussion of lighting conditions.
In many cases, problems, data, or information can be modeled as graphs. Graphs can be used as a tool for modeling in any case where connections between distinguishable objects occur. Any graph consists of a set of objects, called vertices, and a set of connections, called edges, such that any edge connects a pair of vertices. For example, a social network can be modeled by a graph by
transforming the users of the network into vertices and friendship relations between users into edges. Also physical networks like computer networks or transportation networks, for example, the metro network of a city, can be seen as graphs.
For making graphs and, thereby, the data that is modeled, well-understandable for users, we need a visualization. Graph drawing deals with algorithms for visualizing graphs. In this thesis, especially the use of crossings and curves is investigated for graph drawing problems under additional constraints. The constraints that occur in the problems investigated in this thesis especially restrict the positions of (a part of) the vertices; this is done either as a hard constraint or as an optimization criterion.
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.
With the introduction of OpenFlow by the Stanford University in 2008, a process began in the area of network research, which questions the predominant approach of fully distributed network control. OpenFlow is a communication protocol that allows the externalization of the network control plane from the network devices, such as a router, and to realize it as a logically-centralized entity in software. For this concept, the term "Software Defined Networking" (SDN) was coined during scientific discourse.
For the network operators, this concept has several advantages. The two most important can be summarized under the points cost savings and flexibility. Firstly, it is possible through the uniform interface for network hardware ("Southbound API"), as implemented by OpenFlow, to combine devices and software from different manufacturers, which increases the innovation and price pressure on them. Secondly, the realization of the network control plane as a freely programmable software with open interfaces ("Northbound API") provides the opportunity to adapt it to the individual circumstances of the operator's network and to exchange information with the applications it serves. This allows the network to be more flexible and to react more quickly to changing circumstances as well as transport the traffic more effectively and tailored to the user’s "Quality of Experience" (QoE).
The approach of a separate network control layer for packet-based networks is not new and has already been proposed several times in the past. Therefore, the SDN approach has raised many questions about its feasibility in terms of efficiency and applicability. These questions are caused to some extent by the fact that there is no generally accepted definition of the SDN concept to date. It is therefore a part of this thesis to derive such a definition. In addition, several of the open issues are investigated. This Investigations follow the three aspects: Performance Evaluation of Software Defined Networking, applications on the SDN control layer, and the usability of SDN Northbound-API for creation application-awareness in network operation.
Performance Evaluation of Software Defined Networking: The question of the efficiency of an SDN-based system was from the beginning one of the most important. In this thesis, experimental measurements of the performance of OpenFlow-enabled switch hardware and control software were conducted for the purpose of answering this question. The results of these measurements were used as input parameters for establishing an analytical model of the reactive SDN approach. Through the model it could be determined that the performance of the software control layer, often called "Controller", is crucial for the overall performance of the system, but that the approach is generally viable. Based on this finding a software for analyzing the performance of SDN controllers was developed. This software allows the emulation of the forwarding layer of an SDN network towards the control software and can thus determine its performance in different situations and configurations. The measurements with this software showed that there are quite significant differences in the behavior of different control software implementations. Among other things it has been shown that some show different characteristics for various switches, in particular in terms of message processing speed. Under certain circumstances this can lead to network failures.
Applications on the SDN control layer: The core piece of software defined networking are the intelligent network applications that operate on the control layer. However, their development is still in its infancy and little is known about the technical possibilities and their limitations. Therefore, the relationship between an SDN-based and classical implementation of a network function is investigated in this thesis. This function is the monitoring of network links and the traffic they carry. A typical approach for this task has been built based on Wiretapping and specialized measurement hardware and compared with an implementation based on OpenFlow switches and a special SDN control application. The results of the comparison show that the SDN version can compete in terms of measurement accuracy for bandwidth and delay estimation with the traditional measurement set-up. However, a compromise has to be found for measurements below the millisecond range.
Another question regarding the SDN control applications is whether and how well they can solve existing problems in networks. Two programs have been developed based on SDN in this thesis to solve two typical network issues. Firstly, the tool "IPOM", which enables considerably more flexibility in the study of effects of network structures for a researcher, who is confined to a fixed physical test network topology.
The second software provides an interface between the Cloud Orchestration Software "OpenNebula" and an OpenFlow controller. The purpose of this software was to investigate experimentally whether a pre-notification of the network of an impending relocation of a virtual service in a data center is sufficient to ensure the continuous operation of that service. This was demonstrated on the example of a video service.
Usability of the SDN Northbound API for creating application-awareness in network operation: Currently, the fact that the network and the applications that run on it are developed and operated separately leads to problems in network operation. SDN offers with the Northbound-API an open interface that enables the exchange between information of both worlds during operation. One aim of this thesis was to investigate whether this interface can be exploited so that the QoE experienced by the user can be maintained on high level. For this purpose, the QoE influence factors were determined on a challenging application by means of a subjective survey study. The application is cloud gaming, in which the calculation of video game environments takes place in the cloud and is transported via video over the network to the user. It was shown that apart from the most important factor influencing QoS, i.e., packet loss on the downlink, also the type of game type and its speed play a role. This demonstrates that in addition to QoS the application state is important and should be communicated to the network. Since an implementation of such a state conscious SDN for the example of Cloud Gaming was not possible due to its proprietary implementation, in this thesis the application “YouTube video streaming” was chosen as an alternative. For this application, status information is retrievable via the "Yomo" tool and can be used for network control. It was shown that an SDN-based implementation of an application-aware network has distinct advantages over traditional network management methods and the user quality can be obtained in spite of disturbances.
Today’s Internet architecture was not designed from scratch but was driven by new services that emerged during its development. Hence, it is often described as patchwork where additional patches are applied in case new services require modifications to the existing architecture. This process however is rather slow and hinders the development of innovative network services with certain architecture or network requirements. Currently discussed technologies like Software-Defined Networking (SDN) or Network Virtualization (NV) are seen as key enabling technologies to overcome this rigid best effort legacy of the Internet. Both technologies offer the possibility to create virtual networks that accommodate the specific needs of certain services. These logical networks are operated on top of a physical substrate and facilitate flexible network resource allocation as physical resources can be added and removed depending on the current network and load situation. In addition, the clear separation and isolation of networks foster the development of application-aware networks that fulfill the special requirements of emerging applications. A prominent use case that benefits from these extended capabilities of the network is denoted with service component mobility. Services hosted on Virtual Machines (VMs) follow their consuming mobile endpoints, so that access latency as well as consumed network resources are reduced. Especially for applications like video streaming, which consume a large fraction of the available resources, is this an important means to relieve the resource constraints and eventually provide better service quality. Service and endpoint mobility both allow an adaptation of the used paths between an offered service, i.e., video streaming and the consuming users in case the service quality drops due to network problems. To make evidence-based adaptations in case of quality drops, a scalable monitoring component is required that is able to monitor the service quality for video streaming applications with reliable accuracy. This monograph details challenges that arise when deploying a certain service, i.e., video streaming, in a future virtualized network architecture and discusses possible solutions. In particular, this work evaluates the performance of mechanisms enabling service mobility and presents an optimized architecture for service mobility. Concerning endpoint mobility, improvements are developed that reduce the latency between endpoints and consumed services and ensure connectivity regardless of the used mobile access network. In the last part, a network-based video quality monitoring solution is developed and its accuracy is evaluated.
Radiation therapy today, on account of improvements in treatment procedures over the last 60 years, allows precise treatment of static tumors inside the human body. However, irradiation of moving tumors is still a challenging task as moving tumors often leave the treatment beam and the radiation dose delivered to the tumor reduces simultaneously increasing that on healthy tissue. This research work aims to push the frontiers of radiation therapy in order to enable precise treatment of moving tumors with focus on research and development of a unique real-time system enabling active motion compensation through robotic means to compensate tumor motion. During treatment, patients lie on a treatment couch which is normally used for static position corrections of patient set-up errors prior to radiation treatment. The treatment couch used, called HexaPOD, is a parallel manipulator with six degrees of freedom which can precisely position heavy loads inside a small region. Despite the HexaPOD not initially built with dynamics in mind, it is used in this work for sustained motion compensation by moving patients such that tumors stay precisely located at the center of the treatment beam during the complete course of treatment. In order to realize real-time tumor motion compensation by means of the HexaPOD, several challanges need to be addressed. Real-time aspects are covered by the adoption of a hard real-time operation system in combination with measurement and estimation of latencies of all physical quantities in the compensation system such as tumor or breathing position measurements. Accurate timing information is respected consistently in the whole system and all software-induced latencies are adaptively compensated for. This requires knowledge of future tumor positions from predictors. Several predictors for breathing and tumor motion predictions are proposed and evaluated in terms of a variety of different performance metrics. Extensions to prediction algorithms are introduced fusing both breathing and tumor position information to allow for predictions without the need of an explicit correlation model. Predictions determine the future motion path of the HexaPOD in order to compensate for tumor motion. Several control schemes are developed to enable reference tracking for the HexaPOD. Based on linear and non-linear dynamic modelling of the HexaPOD with system identification methods, a first controller is derived in the form of a model predictive controller. A second controller is proposed based on an assumption of the working principle of the HexaPOD's internal controller. Finally, a third controller is derived as combination of the first and second one. For each of these controllers, comparative results with real hardware experiments and humans in the loop as well as choices of free parameters are presented and discussed. Apart from precise tracking, emphasis is placed on patient comfort which is of crucial importance for acceptance of the system. It is demonstrated that smooth trajectories can be realized by the controllers to guarantee that patients feel comfortable while their tumor motion is compensated at sub-millimeter accuracies. Overall errors of the system are analyzed by relating them to tracking and prediction errors. By exploiting the properties of different predictors, it is shown that the startup time until tracking is reached can be reduced to only a few seconds, even in the case of an initially at-rest HexaPOD and with no initial knowledge of tumor motion. This makes the system especially suitable for the relatively short-fractionated treatment sessions for lung tumors. The tumor motion compensation system has been developed solely based on standard clinical hardware, found in most treatment rooms. With a simple and flexible design, existing treatment can be updated in a cost-efficient way to introduce motion compensation capabilities. Simultaneously, the system does not impose any constraints on state-of-the-art treatment types such as intensity modulated radiotherapy or volumetric modulated arc therapy. Supporting different compensation modes, the system can be applied to any moving tumor whether its motion is predictable (lung tumors) or unpredictable (prostate tumors). By integration of adequate tumor position determination methods, the system can be easily extended to other tumors as well.
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!
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.
Internet applications are becoming more and more flexible to support diverge user demands and network conditions. This is reflected by technical concepts, which provide new adaptation mechanisms to allow fine grained adjustment of the application quality and the corresponding bandwidth requirements. For the case of video streaming, the scalable video codec H.264/SVC allows the flexible adaptation of frame rate, video resolution and image quality with respect to the available network resources. In order to guarantee a good user-perceived quality (Quality of Experience, QoE) it is necessary to adjust and optimize the video quality accurately. But not only have the applications of the current Internet changed. Within network and transport, new technologies evolved during the last years providing a more flexible and efficient usage of data transport and network resources. One of the most promising technologies is Network Virtualization (NV) which is seen as an enabler to overcome the ossification of the Internet stack. It provides means to simultaneously operate multiple logical networks which allow for example application-specific addressing, naming and routing, or their individual resource management. New transport mechanisms like multipath transmission on the network and transport layer aim at an efficient usage of available transport resources. However, the simultaneous transmission of data via heterogeneous transport paths and communication technologies inevitably introduces packet reordering. Additional mechanisms and buffers are required to restore the correct packet order and thus to prevent a disturbance of the data transport. A proper buffer dimensioning as well as the classification of the impact of varying path characteristics like bandwidth and delay require appropriate evaluation methods. Additionally, for a path selection mechanism real time evaluation mechanisms are needed. A better application-network interaction and the corresponding exchange of information enable an efficient adaptation of the application to the network conditions and vice versa. This PhD thesis analyzes a video streaming architecture utilizing multipath transmission and scalable video coding and develops the following optimization possibilities and results: Analysis and dimensioning methods for multipath transmission, quantification of the adaptation possibilities to the current network conditions with respect to the QoE for H.264/SVC, and evaluation and optimization of a future video streaming architecture, which allows a better interaction of application and network.