443 research outputs found

    Encountering distributed denial of service attack utilizing federated software defined network

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    This research defines the distributed denial of service (DDoS) problem in software-defined-networks (SDN) environments. The proposes solution uses Software defined networks capabilities to reduce risk, introduces a collaborative, distributed defense mechanism rather than server-side filtration. Our proposed network detection and prevention agent (NDPA) algorithm negotiates the maximum amount of traffic allowed to be passed to server by reconfiguring network switches and routers to reduce the ports' throughput of the network devices by the specified limit ratio. When the passed traffic is back to normal, NDPA starts network recovery to normal throughput levels, increasing ports' throughput by adding back the limit ratio gradually each time cycle. The simulation results showed that the proposed algorithms successfully detected and prevented a DDoS attack from overwhelming the targeted server. The server was able to coordinate its operations with the SDN controllers through a communication mechanism created specifically for this purpose. The system was also able to determine when the attack was over and utilize traffic engineering to improve the quality of service (QoS). The solution was designed with a sophisticated way and high level of separation of duties between components so it would not be affected by the design aspect of the network architecture

    ABC: Adaptive, Biomimetic, Configurable Robots for Smart Farms - From Cereal Phenotyping to Soft Fruit Harvesting

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    Currently, numerous factors, such as demographics, migration patterns, and economics, are leading to the critical labour shortage in low-skilled and physically demanding parts of agriculture. Thus, robotics can be developed for the agricultural sector to address these shortages. This study aims to develop an adaptive, biomimetic, and configurable modular robotics architecture that can be applied to multiple tasks (e.g., phenotyping, cutting, and picking), various crop varieties (e.g., wheat, strawberry, and tomato) and growing conditions. These robotic solutions cover the entire perception–action–decision-making loop targeting the phenotyping of cereals and harvesting fruits in a natural environment. The primary contributions of this thesis are as follows. a) A high-throughput method for imaging field-grown wheat in three dimensions, along with an accompanying unsupervised measuring method for obtaining individual wheat spike data are presented. The unsupervised method analyses the 3D point cloud of each trial plot, containing hundreds of wheat spikes, and calculates the average size of the wheat spike and total spike volume per plot. Experimental results reveal that the proposed algorithm can effectively identify spikes from wheat crops and individual spikes. b) Unlike cereal, soft fruit is typically harvested by manual selection and picking. To enable robotic harvesting, the initial perception system uses conditional generative adversarial networks to identify ripe fruits using synthetic data. To determine whether the strawberry is surrounded by obstacles, a cluster complexity-based perception system is further developed to classify the harvesting complexity of ripe strawberries. c) Once the harvest-ready fruit is localised using point cloud data generated by a stereo camera, the platform’s action system can coordinate the arm to reach/cut the stem using the passive motion paradigm framework, as inspired by studies on neural control of movement in the brain. Results from field trials for strawberry detection, reaching/cutting the stem of the fruit with a mean error of less than 3 mm, and extension to analysing complex canopy structures/bimanual coordination (searching/picking) are presented. Although this thesis focuses on strawberry harvesting, ongoing research is heading toward adapting the architecture to other crops. The agricultural food industry remains a labour-intensive sector with a low margin, and cost- and time-efficiency business model. The concepts presented herein can serve as a reference for future agricultural robots that are adaptive, biomimetic, and configurable

    Resilient and Scalable Forwarding for Software-Defined Networks with P4-Programmable Switches

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    Traditional networking devices support only fixed features and limited configurability. Network softwarization leverages programmable software and hardware platforms to remove those limitations. In this context the concept of programmable data planes allows directly to program the packet processing pipeline of networking devices and create custom control plane algorithms. This flexibility enables the design of novel networking mechanisms where the status quo struggles to meet high demands of next-generation networks like 5G, Internet of Things, cloud computing, and industry 4.0. P4 is the most popular technology to implement programmable data planes. However, programmable data planes, and in particular, the P4 technology, emerged only recently. Thus, P4 support for some well-established networking concepts is still lacking and several issues remain unsolved due to the different characteristics of programmable data planes in comparison to traditional networking. The research of this thesis focuses on two open issues of programmable data planes. First, it develops resilient and efficient forwarding mechanisms for the P4 data plane as there are no satisfying state of the art best practices yet. Second, it enables BIER in high-performance P4 data planes. BIER is a novel, scalable, and efficient transport mechanism for IP multicast traffic which has only very limited support of high-performance forwarding platforms yet. The main results of this thesis are published as 8 peer-reviewed and one post-publication peer-reviewed publication. The results cover the development of suitable resilience mechanisms for P4 data planes, the development and implementation of resilient BIER forwarding in P4, and the extensive evaluations of all developed and implemented mechanisms. Furthermore, the results contain a comprehensive P4 literature study. Two more peer-reviewed papers contain additional content that is not directly related to the main results. They implement congestion avoidance mechanisms in P4 and develop a scheduling concept to find cost-optimized load schedules based on day-ahead forecasts

    Software defined networking for radio telescopes: a case study on the applicability of SDN for MeerKAT

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    Scientific instruments like radio telescopes depend on high-performance networks for internal data exchange. The high bandwidth data exchange between the components of a radio telescope makes use of multicast networking. Complex multicast networks are hard to maintain and grow, and specific installations require modified network switches. This study evaluates Software Defined Networking (SDN) for use in the MeerKAT radio telescope to alleviate the management complexity and allow for a vendor-neutral implementation. The purpose of this dissertation is to verify that an SDN multicast network can produce suitable paths for data flow through the network and to see if such an implementation is easier to maintain and grow. There is little literature regarding SDN for radio telescope networks; however, there is considerable work where different aspects of SDN are discussed and demonstrated for video streaming. SDN with multicast for video streaming, although simpler, forms the background research. Considerable work was put into understanding and documenting the different aspects of a radio telescope affecting the data network. The telescope network controller generates the OpenFlow rules required by the SDN controller and is a new concept introduced in this work. The telescope network controller is fitted with two placement algorithms to demonstrate its flexibility. Both algorithms are suitable for the expected workload, but they produce very different traffic patterns. The two algorithms are not compared to one another, they were created to demonstrate the ease of adding domain specific knowledge to an SDN. The telescope network controller makes it easy to introduce and use new flow placement algorithms, thus making traffic engineering feasible for the radio telescope. Complex multicast networks are easier to maintain and grow with SDN. SDN allows customised packet forwarding rules typically unattainable with standard routing and other standard network protocols and implementations. A radio telescope with a software-defined data network is resilient, easier to maintain, vendor-neutral, and possesses advanced traffic engineering mechanisms

    Reconstruçãao 3D de subestações de energia para monitoramento remoto com arquitetura Edge-fog com estudo de integração ao contexto 5G

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    The need for automation and remote monitoring in power substations has been a growing topic in the literature and practical applications, often dealing with a tedious task in an energized environment, at risk to the operator’s health, or even difficult to access. One way to approach this problem is to reproduce the scenario and its components in a virtual environment, through 3D reconstruction of the real entities of the substation. Currently, this scanning is performed largely in a non-optimized way, relying on embedded processing or only data acquisition to be worked offline in the cloud. These applications reduce, or even make it impossible to request, in realtime, the state of the substation components, in addition to not being scalable and efficient. Faced with this challenge, this work proposes two solution fronts: the use of classical techniques optimized by the parallel processing of point cloud data through an iterative octree; and an edge-fog computing architecture, which allows distribution of computational load and storage among all the entities in the solution, which in turn are allocated in processing layers (edge and fog), something unprecedented in the literature for this specific application. The 5G network application is presented along with a brief discussion on how this technology can benefit and allow the overall distributed processing. A robot is developed to collect data from images and point clouds. All algorithmic and mathematical processes are allocated and detailed for both layers. As a result, the work brings a complete analysis of the benefits of using architecture against a local and traditional approach, based on edge. The scalability of the solution is demonstrated at the end, in a real application environment, and we conclude with how much a private 5G network would benefit this solution in the required remote monitoring scenario.A necessidade de automa¸c˜ao e monitoramento remoto em subesta¸c˜oes de energia vem sendo um t´opico crescente na literatura e aplica¸c˜oes pr´aticas, muitas vezes se tratando de uma tarefa tediosa em um ambiente energizado, de risco `a sa´ude do operador, ou at´e mesmo de dif´ıcil acesso. Uma forma de abordagem para solu¸c˜ao deste problema consiste na reprodu¸c˜ao do cen´ario e seus componentes em ambiente virtual, por meio de reconstru¸c˜ao 3D dos entes reais da subesta¸c˜ao. Atualmente, esse escaneamento ´e realizado em grande parte de forma n˜ao otimizada, contando com processamento embarcado ou somente aquisi¸c˜ao de dados para serem trabalhados de maneira offline na nuvem. Essas aplica¸c˜oes reduzem, ou at´e mesmo inviabilizam a requisi¸c˜ao em tempo real do estado dos componentes da subesta¸c˜ao, al´em de n˜ao serem escal´aveis e eficientes. Para solucionar esse desafio, este trabalho prop˜oe duas frentes de solu¸c˜ao: o uso de t´ecnicas cl´assicas otimizadas pelo processamento paralelo de dados de nuvem de pontos por meio de um Algoritmo de Octree Iterativa; e uma arquitetura de computa¸c˜ao edge-fog, a qual permite distribui¸c˜ao de carga computacional e armazenamento entre todos os entes da solu¸c˜ao, estes por sua vez alocados em camadas, algo in´edito na literatura para esta aplica¸c˜ao espec´ıfica. O uso de rede de comunica¸c˜ao 5G ´e apresentado com uma breve discuss˜ao em como essa tecnologia pode beneficiar o processamento distribu´ıdo como um todo. Um robˆo foi desenvolvido para coletar os dados de imagens e nuvens de pontos. Todos os processos algor´ıtmicos e matem´aticos s˜ao alocados e detalhados para ambas as camadas edge e fog. Como resultado, o trabalho traz uma an´alise completa dos benef´ıcios do uso da arquitetura frente a uma abordagem local e tradicional, baseada em edge. A escalabilidade da solu¸c˜ao ´e demonstrada ao final, em ambiente real de aplica¸c˜ao, e n´os conclu´ımos o quanto uma rede de comunica¸c˜ao 5G iria beneficiar a solu¸c˜ao neste cen´ario requerido para monitoramento remoto

    Liquid cooled micro-scale gradient system for magnetic resonance

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    Schaltbare magnetische Feldgradientspulen sind ein geeignetes Werkzeug für die Modulation der Kernspinpräzession in der gepulsten Kernspinresonanzspektroskopie und Bildgebung. Die Magnetresonanztomographie von mikroskopischen Proben benötigt starke, schnell schaltbare Magnetfeldgradienten, um diffusionsbedingte Artefakte zu unterdrücken, Suszeptibilitätseffekte abzuschwächen und um die Messzeit zu verkürzen. Verschiedene Techniken können eingesetzt werden, um eine hohe Gradientenintensität zu erreichen, wie zum Beispiel die Erhöhung der Stromstärke oder die Steigerung der Windungsdichte der Feldspule. Ein weiterer, geeigneter technischer Ansatz besteht darin, die Gradientenspulen näher an der Probe zu platzieren. Als Konsequenz wird aber die durch die Joule-Erwärmung verursachte Wärmeentwicklung zu einem zentralen Problem. In dieser Arbeit wird ein neuartiges Design, ein Mikroherstellungsprozess und eine Kernspin-Evaluierung eines Feldgradientenchips präsentiert. Die Gradientenspulen wurden besonders hoch miniaturisiert und durch den Einsatz von verbesserten und neuartigen Strukturierungsverfahren entwickelt. Zuerst wird ein Fertigungsverfahren zur Herstellung einer kompakten Hochfrequenzspule vorgestellt. Durch den Einsatz einer maskenlosen Rückseitenlithographie konnte die Prozesskomplexität reduziert werden. Dieses Verfahren wurde durch Tintenstrahldruck mit Nanopartikeln realisiert, wobei die gedruckten Strukturen selbst als lithographische Maske für die Herstellung einer galvanischen Form dienen. Somit werden die Seitenwände der galvanischen Form durch die gedruckte Seed-Schicht optimal selbst ausgerichtet. Dies ermöglichte eine anisotrope Galvanisierung, um eine höhere elektrische Leitfähigkeit der gedruckten Leiterbahnen zu erzielen. Aus den Erkenntnissen der ausgearbeiteten Herstellungsprozesse wurde ein optimiertes Spulendesign für ein-axiale sowie drei-axiale linearen Gradientenchips entwickelt. Die einachsige lineare zz-Gradientenspule wurde mit der Stream-Function-Methode berechnet, wobei die Optimierung darauf abgestimmt wurde, eine minimale Verlustleistung zu erzielen. Die Gradientenspulen wurden auf zwei Doppellagen implementiert, die mittels Cu-Galvanik in Kombination mit fotodefinierbaren Trockenfilm-Laminaten aufgebracht wurden. Bei dem hier vorgestellten Herstellungsverfahren diente die erste Metallisierungschicht gleichzeitig dazu, Widerstands-Temperaturdetektoren zu integrieren. Um niederohmige Spulen zu realisieren wurde der Galvanisierungsprozess soweit angepasst, um eine hohe Schichtdicke zu erzielen. Die Chipstruktur beinhaltet ein aktives Kühlsystem, um dem Aufheizen der Spulen entgegenzuwirken. Thermographische Aufnahmen in Kombination mit den eingebetteten Temperatursensoren ermöglichen es, die Erhitzung der Spule zu analysieren, um die Strombelastbarkeit zu ermitteln. Die Gradientenspule wurde mit einer Hochfrequenz-Mikrospule in einer Flip-Chip-Konfiguration zusammengebaut, und mit diesem Aufbau wurde ein eindimensionales Kernspinexperiment durchgeführt. Es wurde eine Gradienteneffizienz von 3.15 Tm1A1T\,m^{−1}\,A^{−1} bei einer Profillänge von 1.2 mmmm erreicht

    Characterizing Bulk Signals in an Inverted Coaxial Point-Contact Detector to Inform Rare-Event Searches

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    A novel HPGe Compton scanner was constructed at the Max Planck Institute for Physics in Munich. In this apparatus, highly penetrating gamma-rays deposit energy in the Ge lattice, inducing a signal coincident with that of a position-and-energy-sensitive camera. Position reconstruction in large-volume Ge detectors with a millimeter level resolution and reasonable detector scanning times was demonstrated. The scanning apparatus was employed to characterize the bulk of a 2 kg Inverted Coaxial Point-Contact detector. It was hypothesized that in such large volume detectors, deep hole trapping in the bulk could lead to the severe energy degradation of signals. At 95 K, no evidence of significant deep hole trapping was found. This is of importance for the next generation of Ge-based neutrinoless double-beta decay experiments, where at the tonne-scale even small underperforming volumes could populate the signal window with energy degraded signals. Using the Compton scanner, the first experimental images of the depletion surface of a large-volume, non-segmented HPGe detector were produced. It was shown that a modified impurity model of the detector, determined using the evolution of the depletion surface at different biases, outperforms the conventional model of the impurity profile. Pulse-shape simulation is heavily reliant on the impurity model. Thus, a precise understanding of impurities is critical for the development of background rejection techniques used in Ge-based rare-event searches.Doctor of Philosoph

    Configurable data center switch architectures

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    In this thesis, we explore alternative architectures for implementing con_gurable Data Center Switches along with the advantages that can be provided by such switches. Our first contribution centers around determining switch architectures that can be implemented on Field Programmable Gate Array (FPGA) to provide configurable switching protocols. In the process, we identify a gap in the availability of frameworks to realistically evaluate the performance of switch architectures in data centers and contribute a simulation framework that relies on realistic data center traffic patterns. Our framework is then used to evaluate the performance of currently existing as well as newly proposed FPGA-amenable switch designs. Through collaborative work with Meng and Papaphilippou, we establish that only small-medium range switches can be implemented on today's FPGAs. Our second contribution is a novel switch architecture that integrates a custom in-network hardware accelerator with a generic switch to accelerate Deep Neural Network training applications in data centers. Our proposed accelerator architecture is prototyped on an FPGA, and a scalability study is conducted to demonstrate the trade-offs of an FPGA implementation when compared to an ASIC implementation. In addition to the hardware prototype, we contribute a light weight load-balancing and congestion control protocol that leverages the unique communication patterns of ML data-parallel jobs to enable fair sharing of network resources across different jobs. Our large-scale simulations demonstrate the ability of our novel switch architecture and light weight congestion control protocol to both accelerate the training time of machine learning jobs by up to 1.34x and benefit other latency-sensitive applications by reducing their 99%-tile completion time by up to 4.5x. As for our final contribution, we identify the main requirements of in-network applications and propose a Network-on-Chip (NoC)-based architecture for supporting a heterogeneous set of applications. Observing the lack of tools to support such research, we provide a tool that can be used to evaluate NoC-based switch architectures.Open Acces
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