@phdthesis{Hirth2016,
  author    = {Hirth, Matthias Johannes Wilhem},
  title     = {Modeling Crowdsourcing Platforms - A Use-Case Driven Approach},
  issn      = {1432-8801},
  doi       = {10.25972/OPUS-14072},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-140726},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2016},
  abstract  = {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.},
  subject      = {Open Innovation},
  language  = {en}
}
@phdthesis{Rygielski2017,
  author    = {Rygielski, Piotr},
  title     = {Flexible Modeling of Data Center Networks for Capacity Management},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-146235},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2017},
  abstract  = {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.},
  subject      = {Modellierung},
  language  = {en}
}
@phdthesis{Staehle2011,
  author    = {Staehle, Barbara},
  title     = {Modeling and Optimization Methods for Wireless Sensor and Mesh Networks},
  doi       = {10.25972/OPUS-4967},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-64884},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2011},
  abstract  = {Im Internet der Zukunft werden Menschen nicht nur mit Menschen, sondern auch mit „Dingen", und sogar „Dinge" mit „Dingen" kommunizieren. Zus{\"a}tzlich wird das Bed{\"u}rfnis steigen, immer und {\"u}berall Zugang zum Internet zu haben. Folglich gewinnen drahtlose Sensornetze (WSNs) und drahtlose Mesh-Netze (WMNs) an Bedeutung, da sie Daten {\"u}ber die Umwelt ins Internet liefern, beziehungsweise einfache Internet-Zugangsm{\"o}glichkeiten schaffen. In den vier Teilen dieser Arbeit werden unterschiedliche Modellierungs- und Optimierungsmethoden f{\"u}r WSNs und WMNs vorgestellt. Der Energieverbrauch ist die wichtigste Metrik, wenn es darum geht die Kommunikation in einem WSN zu optimieren. Da sich in der Literatur sehr viele unterschiedliche Energiemodelle finden, untersucht der erste Teil der Arbeit welchen Einfluss unterschiedliche Energiemodelle auf die Optimierung von WSNs haben. Aufbauend auf diesen {\"U}berlegungen besch{\"a}ftigt sich der zweite Teil der Arbeit mit drei Problemen, die {\"u}berwunden werden m{\"u}ssen um eine standardisierte energieeffiziente Kommunikations-L{\"o}sung f{\"u}r WSNs basierend auf IEEE 802.15.4 und ZigBee zu realisieren. F{\"u}r WMNs sind beide Probleme von geringem Interesse, die Leistung des Netzes jedoch umso mehr. Der dritte Teil der Arbeit f{\"u}hrt daher Algorithmen f{\"u}r die Berechnung des Max-Min fairen (MMF) Netzwerk-Durchsatzes in WMNs mit mehreren Linkraten und Internet-Gateways ein. Der letzte Teil der Arbeit untersucht die Auswirkungen des LRA-Konzeptes. Dessen grundlegende Idee ist die folgende. Falls f{\"u}r einen Link eine niedrigere Datenrate als theoretisch m{\"o}glich verwendet wird, sinkt zwar der Link-Durchsatz, jedoch ist unter Umst{\"a}nden eine gr{\"o}ßere Anzahl von gleichzeitigen {\"U}bertragungen m{\"o}glich und der Gesamt-Durchsatz des Netzes kann sich erh{\"o}hen. Mithilfe einer analytischen LRA Formulierung und einer systematischen Studie kann gezeigt werden, dass eine netzwerkweite Zuordnung robusterer Datenraten als n{\"o}tig zu einer Erh{\"o}hung des MMF Netzwerk-Durchsatzes f{\"u}hrt. Desweitern kann gezeigt werden, dass sich LRA positiv auf die Leistungsf{\"a}higkeit eines IEEE 802.11 WMNs auswirkt und f{\"u}r die Optimierung des Netzes genutzt werden kann.},
  subject      = {Drahtloses Sensorsystem},
  language  = {en}
}
@phdthesis{Zinner2012,
  author    = {Zinner, Thomas},
  title     = {Performance Modeling of QoE-Aware Multipath Video Transmission in the Future Internet},
  doi       = {10.25972/OPUS-6106},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-72324},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2012},
  abstract  = {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.},
  subject      = {Video{\"u}bertragung},
  language  = {en}
}
@phdthesis{Herrler2007,
  author    = {Herrler, Rainer},
  title     = {Agentenbasierte Simulation zur Ablaufoptimierung in Krankenh{\"a}usern und anderen verteilten, dynamischen Umgebungen},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-24483},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2007},
  abstract  = {Verteilte dynamische Systeme unter lokalen und globalen Gesichtspunkten zu optimieren ist eine schwierige Aufgabe. Zwar sind grunds{\"a}tzliche Auswirkungen einzelner Maßnahmen h{\"a}ufig bekannt, durch widerstrebende Ziele, Wechselwirkungen zwischen Prozessen und Nebenwirkungen von Maßnahmen ist ein analytisches Vorgehen bei der Optimierung nicht m{\"o}glich. Besonders schwierig wird es, wenn lokale Einheiten einerseits ihre Ziele und Autonomie behalten sollen, aber durch zentrale Vorgaben bzw. Anreize so gesteuert werden sollen, dass ein {\"u}bergeordnetes Ziel erreicht wird. Ein praktisches Beispiel dieses allgemeinen Optimierungsproblems findet sich im Gesundheitswesen. Das Management von modernen Kliniken ist stets mit dem Problem konfrontiert, die Qualit{\"a}t der Pflege zu gew{\"a}hrleisten und gleichzeitig kosteneffizient zu arbeiten. Hier gilt es unter gegeben Rahmenbedingungen und bei Respektierung der Autonomie der Funktionseinheiten, Optimierungsmaßnahmen zu finden und durchzuf{\"u}hren. Vorhandene Werkzeuge zur Simulation und Modellierung bieten f{\"u}r diese Aufgabe keine ausreichend guten Vorgehensmodelle und Modellierungsmechanismen. Die agentenbasierte Simulation erm{\"o}glicht die Abbildung solcher Systeme und die Durchf{\"u}hrung von Simulationsexperimenten zur Bewertung einzelner Maßnahmen. Es werden L{\"o}sungswege und Werkzeuge vorgestellt und evaluiert, die den Benutzer bei der Formalisierung des Wissens und der Modellierung solch komplexer Szenarien unterst{\"u}tzen und ein systematisches Vorgehen zur Optimierung erm{\"o}glichen.},
  subject      = {Simulation},
  language  = {de}
}
@phdthesis{Kluegl2000,
  author    = {Kl{\"u}gl, Franziska},
  title     = {Aktivit{\"a}tsbasierte Verhaltensmodellierung und ihre Unterst{\"u}tzung bei Multiagentensimulationen},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-2874},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2000},
  abstract  = {Durch Zusammenf{\"u}hrung traditioneller Methoden zur individuenbasierten Simulation und dem Konzept der Multiagentensysteme steht mit der Multiagentensimulation eine Methodik zur Verf{\"u}gung, die es erm{\"o}glicht, sowohl technisch als auch konzeptionell eine neue Ebene an Detaillierung bei Modellbildung und Simulation zu erreichen. Ein Modell beruht dabei auf dem Konzept einer Gesellschaft: Es besteht aus einer Menge interagierender, aber in ihren Entscheidungen autonomen Einheiten, den Agenten. Diese {\"a}ndern durch ihre Aktionen ihre Umwelt und reagieren ebenso auf die f{\"u}r sie wahrnehmbaren {\"A}nderungen in der Umwelt. Durch die Simulation jedes Agenten zusammen mit der Umwelt, in der er "lebt", wird die Dynamik im Gesamtsystem beobachtbar. In der vorliegenden Dissertation wurde ein Repr{\"a}sentationsschema f{\"u}r Multiagentensimulationen entwickelt werden, das es Fachexperten, wie zum Beispiel Biologen, erm{\"o}glicht, selbst{\"a}ndig ohne traditionelles Programmieren Multiagentenmodelle zu implementieren und mit diesen Experimente durchzuf{\"u}hren. Dieses deklarative Schema beruht auf zwei Basiskonzepten: Der K{\"o}rper eines Agenten besteht aus Zustandsvariablen. Das Verhalten des Agenten kann mit Regeln beschrieben werden. Ausgehend davon werden verschiedene Strukturierungsans{\"a}tze behandelt. Das wichtigste Konzept ist das der "Aktivit{\"a}t", einer Art "Verhaltenszustand": W{\"a}hrend der Agent in einer Aktivit{\"a}t A verweilt, f{\"u}hrt er die zugeh{\"o}rigen Aktionen aus und dies solange, bis eine Regel feuert, die diese Aktivit{\"a}t beendet und eine neue Aktivit{\"a}t ausw{\"a}hlt. Durch Indizierung dieser Regeln bei den zugeh{\"o}rigen Aktivit{\"a}ten und Einf{\"u}hrung von abstrakten Aktivit{\"a}ten entsteht ein Schema f{\"u}r eine vielf{\"a}ltig strukturierbare Verhaltensbeschreibung. Zu diesem Schema wurde ein Interpreter entwickelt, der ein derartig repr{\"a}sentiertes Modell ausf{\"u}hrt und so Simulationsexperimente mit dem Multiagentenmodell erlaubt. Auf dieser Basis wurde die Modellierungs- und Experimentierumgebung SeSAm ("Shell f{\"u}r Simulierte Agentensysteme") entwickelt. Sie verwendet vorhandene Konzepte aus dem visuellen Programmieren. Mit dieser Umgebung wurden Anwendungsmodelle aus verschiedenen Dom{\"a}nen realisiert: Neben abstrakten Spielbeispielen waren dies vor allem Fragestellungen zu sozialen Insekten, z.B. zum Verhalten von Ameisen, Bienen oder der Interaktion zwischen Bienenv{\"o}lkern und Milbenpopulationen.},
  subject      = {Agent <Informatik>},
  language  = {de}
}
@phdthesis{Steininger2023,
  author    = {Steininger, Michael},
  title     = {Deep Learning for Geospatial Environmental Regression},
  doi       = {10.25972/OPUS-31312},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-313121},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2023},
  abstract  = {Environmental issues have emerged especially since humans burned fossil fuels, which led to air pollution and climate change that harm the environment. These issues' substantial consequences evoked strong efforts towards assessing the state of our environment. Various environmental machine learning (ML) tasks aid these efforts. These tasks concern environmental data but are common ML tasks otherwise, i.e., datasets are split (training, validatition, test), hyperparameters are optimized on validation data, and test set metrics measure a model's generalizability. This work focuses on the following environmental ML tasks: Regarding air pollution, land use regression (LUR) estimates air pollutant concentrations at locations where no measurements are available based on measured locations and each location's land use (e.g., industry, streets). For LUR, this work uses data from London (modeled) and Zurich (measured). Concerning climate change, a common ML task is model output statistics (MOS), where a climate model's output for a study area is altered to better fit Earth observations and provide more accurate climate data. This work uses the regional climate model (RCM) REMO and Earth observations from the E-OBS dataset for MOS. Another task regarding climate is grain size distribution interpolation where soil properties at locations without measurements are estimated based on the few measured locations. This can provide climate models with soil information, that is important for hydrology. For this task, data from Lower Franconia is used. Such environmental ML tasks commonly have a number of properties: (i) geospatiality, i.e., their data refers to locations relative to the Earth's surface. (ii) The environmental variables to estimate or predict are usually continuous. (iii) Data can be imbalanced due to relatively rare extreme events (e.g., extreme precipitation). (iv) Multiple related potential target variables can be available per location, since measurement devices often contain different sensors. (v) Labels are spatially often only sparsely available since conducting measurements at all locations of interest is usually infeasible. These properties present challenges but also opportunities when designing ML methods for such tasks. In the past, environmental ML tasks have been tackled with conventional ML methods, such as linear regression or random forests (RFs). However, the field of ML has made tremendous leaps beyond these classic models through deep learning (DL). In DL, models use multiple layers of neurons, producing increasingly higher-level feature representations with growing layer depth. DL has made previously infeasible ML tasks feasible, improved the performance for many tasks in comparison to existing ML models significantly, and eliminated the need for manual feature engineering in some domains due to its ability to learn features from raw data. To harness these advantages for environmental domains it is promising to develop novel DL methods for environmental ML tasks. This thesis presents methods for dealing with special challenges and exploiting opportunities inherent to environmental ML tasks in conjunction with DL. To this end, the proposed methods explore the following techniques: (i) Convolutions as in convolutional neural networks (CNNs) to exploit reoccurring spatial patterns in geospatial data. (ii) Posing the problems as regression tasks to estimate the continuous variables. (iii) Density-based weighting to improve estimation performance for rare and extreme events. (iv) Multi-task learning to make use of multiple related target variables. (v) Semi-supervised learning to cope with label sparsity. Using these techniques, this thesis considers four research questions: (i) Can air pollution be estimated without manual feature engineering? This is answered positively by the introduction of the CNN-based LUR model MapLUR as well as the off-the-shelf LUR solution OpenLUR. (ii) Can colocated pollution data improve spatial air pollution models? Multi-task learning for LUR is developed for this, showing potential for improvements with colocated data. (iii) Can DL models improve the quality of climate model outputs? The proposed DL climate MOS architecture ConvMOS demonstrates this. Additionally, semi-supervised training of multilayer perceptrons (MLPs) for grain size distribution interpolation is presented, which can provide improved input data. (iv) Can DL models be taught to better estimate climate extremes? To this end, density-based weighting for imbalanced regression (DenseLoss) is proposed and applied to the DL architecture ConvMOS, improving climate extremes estimation. These methods show how especially DL techniques can be developed for environmental ML tasks with their special characteristics in mind. This allows for better models than previously possible with conventional ML, leading to more accurate assessment and better understanding of the state of our environment.},
  subject      = {Deep learning},
  language  = {en}
}
@phdthesis{Schmitt2022,
  author    = {Schmitt, Norbert},
  title     = {Measurement, Modeling, and Emulation of Power Consumption of Distributed Systems},
  doi       = {10.25972/OPUS-27658},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-276582},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2022},
  abstract  = {Today's cloud data centers consume an enormous amount of energy, and energy consumption will rise in the future. An estimate from 2012 found that data centers consume about 30 billion watts of power, resulting in about 263TWh of energy usage per year. The energy consumption will rise to 1929TWh until 2030. This projected rise in energy demand is fueled by a growing number of services deployed in the cloud. 50\% of enterprise workloads have been migrated to the cloud in the last decade so far. Additionally, an increasing number of devices are using the cloud to provide functionalities and enable data centers to grow. Estimates say more than 75 billion IoT devices will be in use by 2025. The growing energy demand also increases the amount of CO2 emissions. Assuming a CO2-intensity of 200g CO2 per kWh will get us close to 227 billion tons of CO2. This emission is more than the emissions of all energy-producing power plants in Germany in 2020. However, data centers consume energy because they respond to service requests that are fulfilled through computing resources. Hence, it is not the users and devices that consume the energy in the data center but the software that controls the hardware. While the hardware is physically consuming energy, it is not always responsible for wasting energy. The software itself plays a vital role in reducing the energy consumption and CO2 emissions of data centers. The scenario of our thesis is, therefore, focused on software development. Nevertheless, we must first show developers that software contributes to energy consumption by providing evidence of its influence. The second step is to provide methods to assess an application's power consumption during different phases of the development process and to allow modern DevOps and agile development methods. We, therefore, need to have an automatic selection of system-level energy-consumption models that can accommodate rapid changes in the source code and application-level models allowing developers to locate power-consuming software parts for constant improvements. Afterward, we need emulation to assess the energy efficiency before the actual deployment.},
  subject      = {Leistungsbedarf},
  language  = {en}
}