05 Fakultät Informatik, Elektrotechnik und Informationstechnik
Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/6
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Item Open Access Second-order projection-based mapping methods for coupled multi-physics simulations(2022) Ariguib, BoshraData mapping describes the exchange of variables between different, usually non-matching grids for storing data. As different physics require different physical constraints, so do different simulation require different mesh properties. This makes data mapping a crucial part when coupling single-physics simulations into a multi-physics simulation. However, the tradeoff for the available computationally efficient methods is usually low accuracy order. Such a method is the nearest-neighbor mapping method, which relies on a computationally inexpensive mapping algorithm and shows a first-order accuracy, as it is based on a constant interpolation. A second-order projection-based mapping method nearest-neighbor-gradient aims to improve the accuracy order of the nearest-neighbor mapping, while preserving the low computational costs. This is achieved through the extension of the existing method by considering additional gradient data information and applying a Hermite interpolation, in order to balance out both the computational efficiency and the accuracy of the mapping. In this thesis, we implemented this method by extending the coupling library preCICE, which uses state-of-the-art algorithms for coupling partitioned multi-physics simulations in a black-box manner. We confirmed the theoretical observations of the expected second-order accuracy of the method and we found that the method shows the best convergence order by contrast with the existing mapping methods, including radial basis function mappings. It also performs just as well as the existing projection-based method in terms of computational cost and outperforms the radial basis function mapping in respect of runtime costs.Item Open Access Orchestrating data governance workloads as stateful services in cloud environments using Kubernetes Operator Framework(2022) Wang, XiaominData is becoming the core corporate asset that will determine the business’s success. As a result, it is critical for governing enterprise data. Previously, the Enterprise Content Management (ECM) system was employed by many companies to manage and process their enterprise data, which is a monolithic data governance application. As the ECM system is typically deployed on bare metal or at most in a virtualized IT infrastructure, it lacks the flexibility to support Continuous Integration (CI) and Continuous Delivery (CD) cost-effectively. Cloud computing has gained popularity as a powerful platform for application deployment, owing to perceived benefits such as elasticity to fluctuating load and reduced operational costs as compared to running in traditional data centers. Therefore, it is promising to migrate the legacy ECM system into the cloud. The goal of this thesis is to orchestrate stateful database workloads in Kubernetes that are typical for ECM systems. For our concept verification, we included a comparison and analysis between traditional and comparable cloud native Relational Database Management System (RDBMS) using IBM DB2, PostgreSQL, CockRoachDB and Google Spanner. We proposed an implementation of the Monitor-Analyze-Plan-Execute (MAPE) concept using the Kubernetes Operator framework. With our prototype implementation, we proved that the Kubernetes operator is able to deploy a cluster for DB2 consisting of a read/write primary and up to three read-only members. Various experiments carried out on the prototype have evidenced its High Availability (HA), Disaster Recovery (DR) features as well as read scalability.Item Open Access Weighted independent colorful sets in large vertex colored conflict graphs for timetriggered flow scheduling(2022) Rönsch, LorenzThe need for time sensitive communication on networks is increasing more and more, especially due to Industrial internet of things and Industry 4.0. With the appearance of graphics-based network participants in time-critical networks, such as VR glasses, the absolute amount of traffic that needs to be scheduled over the network increases strongly. The most common method to realize real time communication is using the IEEE Time-Sensitive Network (TSN) and the Time-Aware Shaper (TAS). However, the TSN schedule calculation is not standardized. There are several approaches, such as SMT solver, integer linear programming and calculating a conflict graph to calculate time-triggered flow schedules. But none of them are tackling the problem of maximizing traffic. In our work, we extend the time-triggered flow scheduling problem to include the component of maximum traffic. For this purpose, we modify an existing heuristic, called Greedy Flow Heap Heuristic, so that we can adapt the scheduling to our problem. The results that our version provides compared to the original heuristic are very promising. On all our evaluation data, we achieved an average improvement of 81.91% in terms of maximum network traffic. We also developed an alternative non-deterministic approach based on a genetic algorithm. In our work we investigate different variants of the algorithm with the goal to provide better results with different adaptations of the algorithm. In our repair version, we manage to beat our benchmark algorithm the Greedy Flow Heap Heuristic on every circle based conflict graph.Item Open Access Data-integrated methods for performance improvement of massively parallel coupled simulations(2022) Totounferoush, Amin; Schulte, Miriam (Prof. Dr.)This thesis presents data-integrated methods to improve the computational performance of partitioned multi-physics simulations, particularly on highly parallel systems. Partitioned methods allow using available single-physic solvers and well-validated numerical methods for multi-physics simulations by decomposing the domain into smaller sub-domains. Each sub-domain is solved by a separate solver and an external library is incorporated to couple the solvers. This significantly reduces the software development cost and enhances flexibility, while it introduces new challenges that must be addressed carefully. These challenges include but are not limited to, efficient data communication between sub-domains, data mapping between not-matching meshes, inter-solver load balancing, and equation coupling. In the current work, inter-solver communication is improved by introducing a two-level communication initialization scheme to the coupling library preCICE. The new method significantly speed-ups the initialization and removes memory bottlenecks of the previous implementation. In addition, a data-driven inter-solver load balancing method is developed to efficiently distribute available computational resources between coupled single-physic solvers. This method employs both regressions and deep neural networks (DNN) for modeling the performance of the solvers and derives and solves an optimization problem to distribute the available CPU and GPU cores among solvers. To accelerate the equation coupling between strongly coupled solvers, a hybrid framework is developed that integrates DNNs and classical solvers. The DNN computes a solution estimation for each time step which is used by classical solvers as a first guess to compute the final solution. To preserve DNN's efficiency during the simulation, a dynamic re-training strategy is introduced that updates the DNN's weights on-the-fly. The cheap but accurate solution estimation by the DNN surrogate solver significantly reduces the number of subsequent classical iterations necessary for solution convergence. Finally, a highly scalable simulation environment is introduced for fluid-structure interaction problems. The environment consists of highly parallel numerical solvers and an efficient and scalable coupling library. This framework is able to efficiently exploit both CPU-only and hybrid CPU-GPU machines. Numerical performance investigations using a complex test case demonstrate a very high parallel efficiency on a large number of CPUs and a significant speed-up due to the GPU acceleration.Item Open Access Investigation of self-learned zeroth-order optimization algorithms(2022) Schüttler, KilianDesigning optimization algorithms manually is a laborious process. In Addition, many optimization algorithms rely on hand-crafted heuristics and perform poorly in applications for which they are not specifically designed. Thus, automating the algorithm design process is very appealing. Moreover, learned algorithms minimize the amount of a priori assumptions and do not rely on hyperparameters after training. Several works exist that present methods to learn an optimization algorithm. In this project, we focus on the reinforcement learning perspective. Therefore, any particular optimization algorithm is represented as a policy. Evaluation of the existing methods shows, learned algorithms outperform existing algorithms in terms of convergence speed and final objective value on particular training tasks. However, the inner mechanisms of learned algorithms largely remain a mystery. A first work has discovered that learned first-order algorithms show a set of intuitive mechanisms that are tuned to the training task. We aim to explore the inner workings of learned zeroth-order algorithms and compare our discoveries to previous works. To address this issue, we study properties of learned zeroth-order algorithms to understand the relationship between what is learned and the quantitative and qualitative properties, e.g., curvature or convexity of the objective function. Furthermore, we study the generalization in relation to these properties. Moreover, we explore the feasibility of finetuning a learned zeroth-order optimization algorithm to a related objective function. Finally we provide guidelines for training and application of learned zeroth-order optimization algorithms.Item Open Access Parameter-dependent self-learning optimization(2022) Abu El Komboz, TareqManually developing optimization algorithms is a time-consuming task requiring expert knowledge. Therefore, it makes a lot of sense to automate the design process of such algorithms. Additionally, learned optimization algorithms reduce the number of a priori assumptions made about the characteristics of the underlying objective function. Numerous works discuss possibilities for learning optimization algorithms. This field of study is called learn-to-optimize. In this bachelor’s thesis, we concentrate on the reinforcement learning perspective. Consequently, optimization algorithms are represented as policies. The comparison of learned algorithms to current state-of-the-art algorithms for particular applications reveals that learned algorithms manage to perform better concerning convergence speed and final objective function value. However, most existing approaches only consider fixed sets of parameters to be optimized. Because of this, it is challenging to adapt the learned optimization algorithm to other objective functions. More importantly, it is impossible to optimize when explicit constraints on so-called “free” optimization parameters are given. We investigated the learn-to-optimize approach under various optimization parameter sets and conditions on “free” parameters to solve this problem. Furthermore, we studied the performance of learned optimizers in high-dimensional setups.Item Open Access A deep learning approach for large-scale groundwater heat pump temperature prediction(2022) Scheurer, StefaniaHeating and cooling buildings is one of the most energy-intensive aspects of modern life. To minimize the impact on global warming and decelerate climate change, more efficient and carbon emission-mitigating technologies such as openloop groundwater heat pumps (GWHP) for heating and cooling buildings are being used and quickly adopted. Nowadays, in order to guarantee their optimal use and prevent negative interactions, city planners need to optimize their placement in the urban landscape. This optimization process requires fast models that simulate the effect of a GWHP on the groundwater temperature. Considering a large domain with multiple GWHPs, this work introduces a framework for the groundwater temperature prediction. While using a learned local surrogate model, a convolutional neural network, to predict the local temperature field around every single GWHP, a physics-informed neural network (PINN) is employed afterwards to correct the global initial solution of stitched together local predictions. As the violations of the physical laws described by the underlying partial differential equation(s) are spatially unevenly distributed, two different methods for drawing sampling points, on the basis of which the training of the PINN to correct the global initial solution takes place, are investigated and compared. This work shows that it is possible for a PINN to correct the global initial solution of stitched together local predictions in a domain with multiple GWHPs. However, there are still opportunities to improve the quality and decrease the computational time of the presented framework. The best method for drawing sampling points depends on the scenario and the placement of the GWHPs. Thus, no general statement can be made, which of the two methods is more suitable. This work provides a good basis for further investigation of the presented framework.Item Open Access Context-aware data validation for machine learning pipelines(2022) Schubert, TimThese days, machine learning plays a key role in plenty of applications. Self-learning algorithms are developed in not only industrial applications, e.g., production lines, or fleet management, but also in the private sector, e.g. smart homes. The performance of these programs is significantly related to the provided training data. A major challenge is preserving high quality of the data. Therefore, the demand for good data cleaning methods has been increasing over the past few years. While existing cleaning techniques can consider constraints and dependencies in data, they can not exploit context information automatically. Thus, they usually fail to track shifts in the data distributions or the associated error profiles. To overcome these limitations, this thesis introduces a novel pipeline for automated tabular data cleaning powered by dynamic functional dependency rules extracted from a context model. This context model is a live updating ontology, representing the current state of the environment where the data originates from. The proposed concept divides the pipeline into three main steps: (i) context modeling, (ii) dependency extraction, and (iii) data cleaning. As a proof-of-concept and for evaluation purposes, a prototype has been implemented. This prototype is evaluated on two different datasets, including an IoT dataset from a smart home use case and a commonly used benchmark dataset with different metrics from hospitals in the US. The evaluation shows that the proposed concept and pipeline for the data validation process performs better than typical state-of-the-art error detection methods.Item Open Access Preserving privacy in software defined car environments(2022) Li, YunxuanModern vehicles are becoming more and more intelligent. With new sensors and software that are available for Connected Vehicles (CVs), they are capable of collecting, processing, and sharing data with various participants in connected car environments. Although they bring us lots of convenience and connectivity, they also introduce new threats, such as security, reliability, and privacy. In this thesis, we focus on the privacy aspect and analyze the privacy requirements of connected car environments. To ensure users’ privacy, we propose the Privacy for Connected Vehicle Framework. In general, our framework behaves as an access control system between source data generated in CVs and various end-point services. To protect privacy, our framework only shares data that are perturbed based on users’ privacy requirements. To ensure maximum service quality, our framework does not interfere any business logic of end-point services. In addition, our framework can provide protection for both situational privacy patterns and individual privacy demands. Moreover, our framework always puts users’ safety before privacy and can be deployed in both edge environments and fog environments.Item Open Access Design and evaluation of system concepts and protocols for lossless hardware-assisted streaming of real-time measurement data over IP networks(2022) Schürmann, LeonThe problem of ensuring reliable delivery of data over unreliable transmission media is by no means unexplored. For instance, the Transmission Control Protocol (TCP) is a protocol designed to ensure reliable in-order delivery of data over an unreliable packet-oriented network service. However, TCP and similar protocols are most commonly implemented through software within operation systems designed to run on general-purpose compute hardware. For demanding measurement devices, however, implementations using custom logic implemented in application-specific integrated circuits (ASICs) or field-programmable gate arrays (FPGAs) can typically capture and process data at a greater rate and resolution compared to software systems. This raises the question: how can measurement data be transferred from such a capture device to a remote system for storage and further processing, reliably and at a sufficient data rate? In an effort to answer the aforementioned question, this thesis analyzes preexisting mechanisms for reliable data transport over Ethernet and IP networks, as well as high-bandwidth measurement devices based on the example of a time-to-digital converter (TDC). Combining this knowledge, it presents HELIX, a network protocol and system architecture for reliable transmission of data, implemented through FPGAs. HELIX uses novel concepts and mechanisms to be efficiently implementable within FPGA-based systems, such as an integration of the transmitter's memory management architecture with the transport protocol itself.