Im Bereich des wissenschaftlichen Rechnens nimmt die Anzahl von Programmläufen (Jobs), die von einem Benutzer ausgeführt werden, immer weiter zu. Dieser Trend resultiert sowohl aus einer steigenden Anzahl an CPU-Cores, auf die ein Nutzer zugreifen kann, als auch durch den immer einfacheren Zugriff auf diese mittels Portalen, Workflow-Systemen oder Services. Gleichzeitig schränken zusätzliche Abstraktionsschichten von Grid- und Cloud-Umgebungen die Möglichkeit zur Beobachtung von Jobs ein. Eine Lösung bietet das jobzentrische Monitoring, das die Ausführung von Jobs transparent darstellen kann. Die vorliegende Dissertation zeigt zum einen Methoden mit denen eine skalierbare Infrastruktur zur Verwaltung von Monitoring-Daten im Kontext von Grid, Cloud oder HPC (High Performance Computing) realisiert werden kann. Zu diesem Zweck wird sowohl eine Aufgabenteilung unter Berücksichtigung von Aspekten wie Netzwerkbandbreite und Speicherkapazität mittels einer Strukturierung der verwendeten Server in Schichten, als auch eine dezentrale Aufbereitung und Speicherung der Daten realisiert. Zum anderen wurden drei Analyseverfahren zur automatisierten und massenhaften Auswertung der Daten entwickelt. Hierzu wurde unter anderem ein auf der Kreuzkorrelation basierender Algorithmus mit einem baumbasierten Optimierungsverfahren zur Reduzierung der Laufzeit und des Speicherbedarfs entwickelt. Diese drei Verfahren können die Anzahl der manuell zu analysierenden Jobs von vielen Tausenden, auf die wenigen, interessanten, tatsächlichen Ausreißer bei der Jobausführung reduzieren. Die Methoden und Verfahren zur massenhaften Analyse, sowie zur skalierbaren Verwaltung der jobzentrischen Monitoring-Daten, wurden entworfen, prototypisch implementiert und mittels Messungen sowie durch theoretische Analysen untersucht. / An increasing number of program executions (jobs) is an ongoing trend in scientific computing. Increasing numbers of available compute cores and lower access barriers, based on portal-systems, workflow-systems, or services, drive this trend. At the same time, the abstraction layers that enable grid and cloud solutions pose challenges in observing job behaviour. Thus, observation and monitoring capabilities for large numbers of jobs are lacking. Job-centric monitoring offers a solution to present job executions in a transparent manner. This dissertation presents methods for scalable infrastructures that handle monitoring data of jobs in grid, cloud, and HPC (High Performance Computing) solutions. A layer-based organisation of servers with a distributed storage scheme enables a task sharing that respects network bandwidths and data capacities. Additionally, three proposed automatic analysis techniques enable an evaluation of huge data quantities. One of the developed algorithms is based on cross-correlation and uses a tree-based optimisation strategy to decrease both runtime and memory usage. These three methods are able to significantly reduce the number of jobs for manual analysis from many thousands to a few interesting jobs that exhibit outlier-behaviour during job execution. Contributions of this thesis include a design, a prototype implementation, and an evaluation for methods that analyse large amounts of job-data, as well for the scalable storage concept for such data.

<p>Disertacija se bavi analizom uticaja raspodela podataka na rezultate algoritama diskretizacije u okviru procesa ma&scaron;inskog učenja. Na osnovu izabranih baza i algoritama diskretizacije teorije grubih skupova i stabala odlučivanja, istražen je uticaj odnosa raspodela podataka i tačaka reza određene diskretizacije.<br />Praćena je promena konzistentnosti diskretizovane tabele u zavisnosti od položaja redukovane tačke reza na histogramu. Definisane su fiksne tačke reza u zavisnosti od segmentacije multimodal raspodele, na osnovu kojih je moguće raditi redukciju preostalih tačaka reza. Za određivanje fiksnih tačaka konstruisan je algoritam FixedPoints koji ih određuje u skladu sa grubom segmentacijom multimodal raspodele.<br />Konstruisan je algoritam aproksimativne diskretizacije APPROX MD za redukciju tačaka reza, koji koristi tačke reza dobijene algoritmom maksimalne razberivosti i parametre vezane za procenat nepreciznih pravila, ukupni procenat klasifikacije i broj tačaka redukcije. Algoritam je kompariran u odnosu na algoritam maksimalne razberivosti i u odnosu na algoritam maksimalne razberivosti sa aproksimativnim re&scaron;enjima za &alpha;=0,95.</p> / <p>This dissertation analyses the influence of data distribution on the results of discretization algorithms within the process of machine learning. Based on the chosen databases and the discretization algorithms within the rough set theory and decision trees, the influence of the data distribution-cuts relation within certain discretization has been researched.<br />Changes in consistency of a discretized table, as dependent on the position of the reduced cut on the histogram, has been monitored. Fixed cuts have been defined, as dependent on the multimodal segmentation, on basis of which it is possible to do the reduction of the remaining cuts. To determine the fixed cuts, an algorithm FixedPoints has been constructed, determining these points in accordance with the rough segmentation of multimodal distribution.<br />An algorithm for approximate discretization, APPROX MD, has been constructed for cuts reduction, using cuts obtained through the maximum discernibility (MD-Heuristic) algorithm and the parametres related to the percent of imprecise rules, the total classification percent and the number of reduction cuts. The algorithm has been compared to the MD algorithm and to the MD algorithm with approximate solutions for &alpha;=0,95.</p>

Le calcul scientifique parallèle est un domaine en plein essor qui permet à la fois d’augmenter la vitesse des longs traitements, de traiter des problèmes de taille plus importante ou encore des problèmes plus précis. Ce domaine permet donc d’aller plus loin dans les calculs scientifiques, d’obtenir des résultats plus pertinents, car plus précis, ou d’étudier des problèmes plus volumineux qu’auparavant. Dans le monde plus particulier de la simulation numérique scientifique, la résolution d’équations aux dérivées partielles (EDP) est un calcul particulièrement demandeur de ressources parallèles. Si les ressources matérielles permettant le calcul parallèle sont de plus en plus présentes et disponibles pour les scientifiques, à l’inverse leur utilisation et la programmation parallèle se démocratisent difficilement. Pour cette raison, des modèles de programmation parallèle, des outils de développement et même des langages de programmation parallèle ont vu le jour et visent à simplifier l’utilisation de ces machines. Il est toutefois difficile, dans ce domaine dit du “parallélisme implicite”, de trouver le niveau d’abstraction idéal pour les scientifiques, tout en réduisant l’effort de programmation. Ce travail de thèse propose tout d’abord un modèle permettant de mettre en oeuvre des solutions de parallélisme implicite pour les simulations numériques et la résolution d’EDP. Ce modèle est appelé “Structured Implicit Parallelism for scientific SIMulations” (SIPSim), et propose une vision au croisement de plusieurs types d’abstraction, en tentant de conserver les avantages de chaque vision. Une première implémentation de ce modèle, sous la forme d’une librairie C++ appelée SkelGIS, est proposée pour les maillages cartésiens à deux dimensions. Par la suite, SkelGIS, et donc l’implémentation du modèle, est étendue à des simulations numériques sur les réseaux (permettant l’application de simulations représentant plusieurs phénomènes physiques). Les performances de ces deux implémentations sont évaluées et analysées sur des cas d’application réels et complexes et démontrent qu’il est possible d’obtenir de bonnes performances en implémentant le modèle SIPSim. / Parallel scientific computations is an expanding domain of computer science which increases the speed of calculations and offers a way to deal with heavier or more accurate calculations. Thus, the interest of scientific computations increases, with more precised results and bigger physical domains to study. In the particular case of scientific numerical simulations, solving partial differential equations (PDEs) is an especially heavy calculation and a perfect applicant to parallel computations. On one hand, it is more and more easy to get an access to very powerfull parallel machines and clusters, but on the other hand parallel programming is hard to democratize, and most scientists are not able to use these machines. As a result, high level programming models, framework, libraries, languages etc. have been proposed to hide technical details of parallel programming. However, in this “implicit parallelism” field, it is difficult to find the good abstraction level while keeping a low programming effort. This thesis proposes a model to write implicit parallelism solutions for numerical simulations such as mesh-based PDEs computations. This model is called “Structured Implicit Parallelism for scientific SIMulations” (SIPSim), and proposes an approach at the crossroads of existing solutions, taking advantage of each one. A first implementation of this model is proposed, as a C++ library called SkelGIS, for two dimensional Cartesian meshes. A second implementation of the model, and an extension of SkelGIS, proposes an implicit parallelism solution for network-simulations (which deals with simulations with multiple physical phenomenons), and is studied in details. A performance analysis of both these implementations is given on real case simulations, and it demonstrates that the SIPSim model can be implemented efficiently.

La tesis se enmarca en el estudio de las arquitecturas inteligentes de control distribuido y de los sistemas de comunicaciones empleados, más concretamente el trabajo se centra en la optimización del sistema de control por medio de la evaluación del rendimiento en el middleware a través de los parámetros de calidad de servicio y de la optimización del control empleando políticas de calidad de servicio. El principal objetivo de este trabajo ha sido estudiar, diseñar, desarrollar y evaluar una arquitectura de control distribuido, basándose en el estándar de comunicaciones Data-Distribution Service for Real-Time Systems (DDS) propuesto por la organización Object Management Group (OMG). Como aportación principal de la tesis se propone el diseño de una arquitectura distribuida de control inteligente que de soporte a la QoS, tanto en la medición por medio de los parámetros, como en la gestión por medio de las políticas de QoS. Las políticas deben permitir la variación de las características de la comunicación en función de los requisitos de control, expresados estos últimos por medio de los parámetros de QoC. A la arquitectura desarrollada se le ha llamado FSACtrl. Para determinar los requisitos de la arquitectura FSACtrl, se han estudiado las revisiones de los autores más relevantes acerca de las características más destacadas de las arquitecturas distribuidas de sistemas de control. A partir de estas características se han diseñado los elementos de la arquitectura FSACtrl. Los elementos que dan soporte a las comunicaciones se han basado en los del estándar DDS de la OMG, mientras que los elementos de control se han basado en el estándar Sensor Web Enablement (SWE) del Open Geospatial Consortium (OGC). Para la validación de la arquitectura se ha implementado un entorno de diseño y simulación del control. / Poza Luján, JL. (2012). Propuesta de arquitectura distribuida de control inteligente basada en políticas de calidad de servicio [Tesis doctoral]. Editorial Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/14674 / Palancia

Historically, the United States (US) electric grid has been a stable one-way power delivery infrastructure that supplies centrally-generated electricity to its predictably consuming demand. However, the US electric grid is now undergoing a huge transformation from a simple and static system to a complex and dynamic network, which is starting to interconnect intermittent distributed energy resources (DERs), portable electric vehicles (EVs), and load-altering home automation devices, that create bidirectional power flow or stochastic load behavior. In order for this grid of the future to effectively embrace the high penetration of these disruptive and fast-responding digital technologies without compromising its safety, reliability, and affordability, plug-and-play interoperability within the field area network must be enabled between operational technology (OT), information technology (IT), and telecommunication assets in order to seamlessly and securely integrate into the electric utility's operations and planning systems in a modular, flexible, and scalable fashion. This research proposes a potential approach to simplifying the translation and contextualization of operational data on the electric grid without being routed to the utility datacenter for a control decision. This methodology integrates modern software technology from other industries, along with utility industry-standard semantic models, to overcome information siloes and enable interoperability. By leveraging industrial engineering tools, a framework is also developed to help devise a reference architecture and use-case application process that is applied and validated at a US electric utility.