Fault-tolerant Hamiltonian cycle strategy for fast node fault diagnosis based on PMC in data center networks

System-level fault diagnosis model, namely, the PMC model, detects fault nodes only through the mutual testing of nodes in the system without physical equipment. In order to achieve server nodes fault diagnosis in large-scale data center networks (DCNs), the traditional algorithm based on the PMC model cannot meet the characteristics of high diagnosability, high accuracy and high efficiency due to its inability to ensure that the test nodes are fault-free. This paper first proposed a fault-tolerant Hamiltonian cycle fault diagnosis (FHFD) algorithm, which tests nodes in the order of the Hamiltonian cycle to ensure that the test nodes are faultless. In order to improve testing efficiency, a hierarchical diagnosis mechanism was further proposed, which recursively divides high scale structures into a large number of low scale structures based on the recursive structure characteristics of DCNs. Additionally, we proved that $2(n-2){n^{k-1}}$ and $(n-2){t_{n, k}}/{t_{n, 1}}$ faulty nodes could be detected for $BCub{e_{n, k}}$ and $DCel{l_{n, k}}$ within a limited time for the proposed diagnosis strategy. Simulation experiments have also shown that our proposed strategy has improved the diagnosability and test efficiency dramatically

Tools and Algorithms for the Construction and Analysis of Systems

This open access book constitutes the proceedings of the 28th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2022, which was held during April 2-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 46 full papers and 4 short papers presented in this volume were carefully reviewed and selected from 159 submissions. The proceedings also contain 16 tool papers of the affiliated competition SV-Comp and 1 paper consisting of the competition report. TACAS is a forum for researchers, developers, and users interested in rigorously based tools and algorithms for the construction and analysis of systems. The conference aims to bridge the gaps between different communities with this common interest and to support them in their quest to improve the utility, reliability, exibility, and efficiency of tools and algorithms for building computer-controlled systems

Tools and Algorithms for the Construction and Analysis of Systems

This open access book constitutes the proceedings of the 28th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2022, which was held during April 2-7, 2022, in Munich, Germany, as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2022. The 46 full papers and 4 short papers presented in this volume were carefully reviewed and selected from 159 submissions. The proceedings also contain 16 tool papers of the affiliated competition SV-Comp and 1 paper consisting of the competition report. TACAS is a forum for researchers, developers, and users interested in rigorously based tools and algorithms for the construction and analysis of systems. The conference aims to bridge the gaps between different communities with this common interest and to support them in their quest to improve the utility, reliability, exibility, and efficiency of tools and algorithms for building computer-controlled systems

Estratégias eficientes para identificação de falhas utilizando o diagnóstico baseado em comparações

Orientador: Prof. Dr. Elias Procópio Duarte Jr.Tese (doutorado) - Universidade Federal do Paraná, Setor de Ciências Exatas, Curso de Pós-Graduaçao em Informática. Defesa: Curitiba, 12/04/2013Bibliografia: fls. 126-148Resumo: O diagnóstico baseado em comparações e uma forma realista para detectar falhas em hardware, software, redes e sistemas distribuídos. O diagnostico se baseia na comparaçao de resultados de tarefas produzidos por pares de unidades para determinar quais sao as unidades falhas e sem-falha do sistema. Qualquer diferenca no resultado da comparacao indica que uma ou ambas as unidades estao falhas. O diagnostico completo do sistema e baseado no resultado de todas as comparações. Este trabalho apresenta um novo algoritmo de diagnostico para identificar falhas em sistemas de topologia arbitraria com base no modelo MM*. A complexidade do algoritmo proposto e O(t2AN) no pior caso para sistemas de N unidades, onde t denota o numero maximo permitido de unidades falhas e A e o grau da unidade de maior grau no sistema. Esta complexidade e significativamente menor que a dos outros algoritmos previamente publicados. Alem da especificacao do algoritmo e das provas de correcão, resultados obtidos atraves da execucao exaustiva de experimentos sao apresentados, mostrando o desempenho me dio do algoritmo para diferentes sistemas. Al em do novo algoritmo para sistemas de topologia arbitraria, este trabalho tambem apresenta duas outras solucoes para deteccão e combate a poluicao de conteudo, ou alteracoes nao autorizadas, em transmissões de mídia contínua ao vivo em redes P2P - a primeira e uma solucão centralizada e que realiza o diagnostico da poluicao na rede, e a segunda e uma solucao completamente distribuída e descentralizada que tem o objetivo de combater a propagacao da poluicao na rede. Ambas as solucoes utilizam o diagnostico baseado em comparacoes para detectar alterações no conteudo dos dados transmitidos. As soluções foram implementadas no Fireflies, um protocolo escalavel para redes overlay, e diversos experimentos atraves de simulacao foram conduzidos. Os resultados mostram que ambas as estrategias sao solucães viaveis para identificar e combater a poluiçcãao de conteudo em transmissãoes ao vivo e que adicionam baixa sobrecarga ao trafego da rede. Em particular a estrategia de combate a poluicao foi capaz de reduzir consideravelmente a poluicão de conteudo em diversas configurações, em varios casos chegando a elimina-la no decorrer das transmissoães.Abstract: Comparison-based diagnosis is a practical approach to detect faults in hardware, software, and network-based systems. Diagnosis is based on the comparison of task outputs returned by pairs of system units in order to determine whether those units are faulty or fault-free. If the comparison results in a mismatch then one ore both units are faulty. System diagnosis is based on the complete set of all comparison results. This work introduces a novel diagnosis algorithm to identify faults in t-diagnosable systems of arbitrary topology under the MM* model. The complexity of the proposed algorithm is O(t2AN) in the worst case for systems with N units, where t denotes the maximum number of faulty units allowed and A corresponds to the maximum degree of a unit in the system. This complexity is significantly lower than those of previously published algorithms. Besides the algorithm specification and correctness proofs, exhaustive simulations results are presented, showing the typical performance of the algorithm for different systems. Moreover, this work also presents two different strategies to detect and fight content pollution in P2P live streaming transmissions - the first strategy is centralized and performs the diagnosis of content pollution in the network, and the second strategy is a completely distributed solution to combat the propagation of the pollution. Both strategies employ comparison-based diagnosis in order to detect any modification in the data transmitted. The solutions were also implemented in Fireflies, a scalable and fault-tolerant overlay network protocol, and a large number of simulation experiments were conduced. Results show that both strategies are feasible solutions to identify and fight content pollution in live streaming sessions and that they add low overhead in terms of network bandwidth usage. In particular, the solution proposed to combat content pollution was able to significantly reduce the pollution over the system in diverse network configurations - in many cases the solution nearly eliminated the pollution during the transmission

Advances in Robot Navigation

Robot navigation includes different interrelated activities such as perception - obtaining and interpreting sensory information; exploration - the strategy that guides the robot to select the next direction to go; mapping - the construction of a spatial representation by using the sensory information perceived; localization - the strategy to estimate the robot position within the spatial map; path planning - the strategy to find a path towards a goal location being optimal or not; and path execution, where motor actions are determined and adapted to environmental changes. This book integrates results from the research work of authors all over the world, addressing the abovementioned activities and analyzing the critical implications of dealing with dynamic environments. Different solutions providing adaptive navigation are taken from nature inspiration, and diverse applications are described in the context of an important field of study: social robotics

A Track Reconstructing Low-latency Trigger Processor for High-energy Physics

The detection and analysis of the large number of particles emerging from high-energy collisions between atomic nuclei is a major challenge in experimental heavy-ion physics. Efficient trigger systems help to focus the analysis on relevant events. A primary objective of the Transition Radiation Detector of the ALICE experiment at the LHC is to trigger on high-momentum electrons. In this thesis, a trigger processor is presented that employs massive parallelism to perform the required online event reconstruction within 2 µs to contribute to the Level-1 trigger decision. Its three-stage hierarchical architecture comprises 109 nodes based on FPGA technology. Ninety processing nodes receive data from the detector front-end at an aggregate net bandwidth of 2.16 Tbps via 1080 optical links. Using specifically developed components and interconnections, the system combines high bandwidth with minimum latency. The employed tracking algorithm three-dimensionally reassembles the track segments found in the detector's drift chambers based on explicit value comparisons, calculates the momentum of the originating particles from the course of the reconstructed tracks, and finally leads to a trigger decision. The architecture is capable of processing up to 20,000 track segments in less than 2 µs with high detection efficiency and reconstruction precision for high-momentum particles. As a result, this thesis shows how a trigger processor performing complex online track reconstruction within tight real-time requirements can be realized. The presented hardware has been built and is in continuous data taking operation in the ALICE experiment

Small-world interconnection networks for large parallel computer systems

The use of small-world graphs as interconnection networks of multicomputers is proposed and analysed in this work. Small-world interconnection networks are constructed by adding (or modifying) edges to an underlying local graph. Graphs with a rich local structure but with a large diameter are shown to be the most suitable candidates for the underlying graph. Generation models based on random and deterministic wiring processes are proposed and analysed. For the random case basic properties such as degree, diameter, average length and bisection width are analysed, and the results show that a fast transition from a large diameter to a small diameter is experienced when the number of new edges introduced is increased. Random traffic analysis on these networks is undertaken, and it is shown that although the average latency experiences a similar reduction, networks with a small number of shortcuts have a tendency to saturate as most of the traffic flows through a small number of links. An analysis of the congestion of the networks corroborates this result and provides away of estimating the minimum number of shortcuts required to avoid saturation. To overcome these problems deterministic wiring is proposed and analysed. A Linear Feedback Shift Register is used to introduce shortcuts in the LFSR graphs. A simple routing algorithm has been constructed for the LFSR and extended with a greedy local optimisation technique. It has been shown that a small search depth gives good results and is less costly to implement than a full shortest path algorithm. The Hilbert graph on the other hand provides some additional characteristics, such as support for incremental expansion, efficient layout in two dimensional space (using two layers), and a small fixed degree of four. Small-world hypergraphs have also been studied. In particular incomplete hypermeshes have been introduced and analysed and it has been shown that they outperform the complete traditional implementations under a constant pinout argument. Since it has been shown that complete hypermeshes outperform the mesh, the torus, low dimensional m-ary d-cubes (with and without bypass channels), and multi-stage interconnection networks (when realistic decision times are accounted for and with a constant pinout), it follows that incomplete hypermeshes outperform them as well

Combining SOA and BPM Technologies for Cross-System Process Automation

This paper summarizes the results of an industry case study that introduced a cross-system business process automation solution based on a combination of SOA and BPM standard technologies (i.e., BPMN, BPEL, WSDL). Besides discussing major weaknesses of the existing, custom-built, solution and comparing them against experiences with the developed prototype, the paper presents a course of action for transforming the current solution into the proposed solution. This includes a general approach, consisting of four distinct steps, as well as specific action items that are to be performed for every step. The discussion also covers language and tool support and challenges arising from the transformation

Updating structural wind turbine blade models via invertible neural networks

Wind turbine rotor blades are huge and complex composite structures that are exposed to exceptionally high loads, both extreme and fatigue loads. These can result in damages causing severe downtimes or repair costs. It is thus of utmost importance that the blades are carefully designed, including uncertainty analyses in order to produce safe, reliable, and cost-efficient wind turbines. An accurate reliability assessment should already start during the design and manufacturing phases. Recent developments in digitalization give rise to the concept of a digital twin, which replicates a product and its properties into a digital environment. Model updating is a technique, which helps to adapt the digital twin according to the measured characteristics of the real structure. Current model updating techniques are most often based on heuristic optimization algorithms, which are computationally expensive, can only deal with a relatively small parameter space, or do not estimate the uncertainty of the computed results. This thesis’ objective is to present a computationally efficient model updating method that recovers parameter deviation. This method is able to consider uncertainties and a high fidelity degree of the rotor blade model. A validated, fully parameterized model generator is used to perform a physics-informed training of a conditional invertible neural network. This network finally represents a surrogate of the inverse physical model, which then can be used to recover model parameters based on the structural responses of the blade. All presented generic model updating applications show excellent results, predicting the a posteriori distribution of the significant model parameters accurately.Bundesministerium für Wirtschaft und Klimaschutz/Energietechnologien (BMWi)/0324032C, 0324335B/E