Cloud computing provides an easy access to computing resources. Customers can acquire and release resources any time. However, it is not trivial to determine when and how many resources to allocate. Many applications running in the cloud face workload changes that affect their resource demand. The first thought is to plan capacity either for the average load or for the peak load. In the first case there is less cost incurred, but performance will be affected if the peak load occurs. The second case leads to money wastage, since resources will remain underutilized most of the time. Therefore there is a need for a more sophisticated resource provisioning techniques that can automatically scale the application resources according to workload demand and performance constrains. Large cloud providers such as Amazon, Microsoft, RightScale provide auto-scaling services. However, without the proper configuration and testing such services can do more harm than good. In this work I investigate application specific online resource allocation techniques that allow to dynamically adapt to incoming workload, minimize the cost of virtual resources and meet user-specified performance objectives.

With rising complexity of high performance computing systems and their parallel software, performance analysis and optimization has become essential in the development of efficient applications. The comparison of performance data is a key operation required in performance analysis. An analyst may conduct different types of comparisons in order to understand the performance properties of an application. One use case is comparing performance data from multiple measurements. Typical examples for such comparisons are before/after comparisons when applying optimizations or changing code versions. Besides comparing performance between multiple runs, also comparing performance characteristics across the parallel execution streams of an application is essential to detect performance problems. This is typically useful to detect imbalances, outliers, or changing runtime behavior during the execution of an application. While such comparisons are straightforward for the aggregated data in performance profiles, only limited solutions exist for comparing event traces. Trace-based analysis, i.e., the collection of fine-grained information on individual application events with timestamps and application context, has proven to be a powerful technique. The detailed performance information included in event traces make them very suitable for performance analysis. However, this level of detail also presents a challenge because it implies a large and overwhelming amount of data. Currently, users need to perform manual comparison of event traces, which is extremely challenging and time consuming because of the large volume of detailed data and the need to correctly line up trace events. To fill the gap of missing solutions for automatic comparison of event traces, this work proposes a set of techniques that automatically align traces. The alignment allows their structural comparison and the highlighting of differences between them. A set of novel metrics provide the user with an objective measure of the differences between traces, both in terms of differences in the event stream and timing differences across events. An additional important aspect of trace-based analysis is the visualization of performance data in event timelines. This has proven to be a powerful approach for the detection of various types of performance problems. However, visualization of large numbers of event timelines quickly hits the limits of available display resolution. Likewise, identifying performance problems is challenging in the large amount of visualized performance data. To alleviate these problems this work proposes two new approaches for event timeline visualization. First, novel folding strategies for event timelines facilitate visual scalability and provide powerful overviews of performance data at the same time. Second, this work presents an effective approach that automatically identifies and highlights several types of performance critical sections in an application run. This approach identifies time dominant functions of an application and subsequently uses them to analyze runtime imbalances throughout the application run. Intuitive visualizations present the resulting runtime variations and guide the analyst to performance hot spots. Evaluations with benchmarks and real-world applications assess all introduced techniques. The effectiveness of the comparison approaches is demonstrated by showing automatically detected performance issues and structural differences between different versions of applications and across parallel execution streams. Case studies showcase the capabilities of the event timeline visualization techniques by demonstrating scalable performance data visualizations and detecting performance problems and code inefficiencies in real-world applications.

Systems for high performance computing are getting increasingly complex. On the one hand, the number of processors is increasing. On the other hand, the individual processors are getting more and more powerful. In recent years, the latter is to a large extent achieved by increasing the number of cores per processor. Unfortunately, scientific applications often fail to fully utilize the available computational performance. Therefore, performance analysis tools that help to localize and fix performance problems are indispensable. Large scale systems for high performance computing typically consist of multiple compute nodes that are connected via network. Performance analysis tools that analyze performance problems that arise from using multiple nodes are readily available. However, the increasing number of cores per processor that can be observed within the last decade represents a major change in the node architecture. Therefore, this work concentrates on the analysis of the node performance. The goal of this thesis is to improve the understanding of the achieved application performance on existing hardware. It can be observed that the scaling of parallel applications on multi-core processors differs significantly from the scaling on multiple processors. Therefore, the properties of shared resources in contemporary multi-core processors as well as remote accesses in multi-processor systems are investigated and their respective impact on the application performance is analyzed. As a first step, a comprehensive suite of highly optimized micro-benchmarks is developed. These benchmarks are able to determine the performance of memory accesses depending on the location and coherence state of the data. They are used to perform an in-depth analysis of the characteristics of memory accesses in contemporary multi-processor systems, which identifies potential bottlenecks. However, in order to localize performance problems, it also has to be determined to which extend the application performance is limited by certain resources. Therefore, a methodology to derive metrics for the utilization of individual components in the memory hierarchy as well as waiting times caused by memory accesses is developed in the second step. The approach is based on hardware performance counters, which record the number of certain hardware events. The developed micro-benchmarks are used to selectively stress individual components, which can be used to identify the events that provide a reasonable assessment for the utilization of the respective component and the amount of time that is spent waiting for memory accesses to complete. Finally, the knowledge gained from this process is used to implement a visualization of memory related performance issues in existing performance analysis tools. The results of the micro-benchmarks reveal that the increasing number of cores per processor and the usage of multiple processors per node leads to complex systems with vastly different performance characteristics of memory accesses depending on the location of the accessed data. Furthermore, it can be observed that the aggregated throughput of shared resources in multi-core processors does not necessarily scale linearly with the number of cores that access them concurrently, which limits the scalability of parallel applications. It is shown that the proposed methodology for the identification of meaningful hardware performance counters yields useful metrics for the localization of memory related performance limitations.

Die Menge an verfügbaren, elektronisch auswertbaren Informationen nimmt stetig zu. Mobiltelefone mit unterschiedlichsten Sensoren, soziale Netzwerke und das Internet der Dinge sind Beispiele für Erzeuger von potentiell interessanten und verwertbaren Daten. Das Themenfeld der ereignisverarbeitenden Systeme (Event Processing – EP) bietet Technologien und Werkzeuge an, um eintreffende Daten, sog. Ereignisse, in nahezu Echtzeit zu verarbeiten. So können z.B. Muster in den Ereignissen erkannt werden. Durch die Erstellung von abgeleiteten Ereignissen können somit weitere Systemen auf diese Mustererkennung reagieren. So können u.a. zeitbasierte Funktionalitäten realisiert werden, wie z.B. das Überwachen von Aktienkursen in einem definierten Zeitraum. Im Gegensatz zu einem nachrichtenorientierten Kommunikationssystem können in EP-Anwendungen fachlich relevante Anwendungsfunktionalitäten umgesetzt werden. Die Validierung dieser Anwendungen durch Fachexperten gewinnt dadurch eine gesteigerte Bedeutung. Die abnahmetestgetriebene Entwicklung (Acceptance Test Driven Development – ATDD) ist eine Methode der agilen Softwareentwicklung und fokussiert sich auf die Integration von Fachexperten in die Erstellung und Auswertung von automatisierbaren Testfällen. Neben dem Potential der Automatisierung von manuellen Regressionstests liegt in der Methode die Möglichkeit den Wissenstransfer zwischen Entwicklern und Fachexperten zu verbessern. Die vorliegende Arbeit leistet mehrere Beiträge zur Untersuchung von ATDD im Bereich der EP-Anwendungsentwicklung. Zunächst wurden Anforderungen für eine entsprechende Werkzeugunterstützung auf Basis der Eigenschaften von EP-Anwendungen ermittelt und der Produktqualitätsklassifikationen funktionalen Eignung, Modularität und Benutzbarkeit zugeordnet. Im Rahmen einer systematischen Literaturrecherche wurden Ansätze aus der Literatur sowie die Werkzeugunterstützung der vorhandenen Produktlösungen analysiert. Dabei wurde deutlich, dass die verwandten Lösungen die identifizierten Anforderungen nicht ausreichend erfüllen. Dadurch motiviert wurde eine Testbeschreibungssprache sowie ein ausführendes, verteiltes Testsystem konzipiert und formal beschrieben. Die Testbeschreibungssprache bietet Kommandos zur produktunabhängigen Spezifikation von Testfällen an. Mit Hilfe des Testsystems ist es möglich, diese Testfälle gegen EP-Produktlösungen auszuführen. Anhand von ausgewählten Fallstudien und einer prototypischen Umsetzung des Lösungsansatzes wurde eine Validierung vorgenommen. Dabei wird ersichtlich, dass der vorgestellte Lösungsansatz den aktuellen Stand der Technik hinsichtlich funktionaler Eignung und Modularität in diesem Anwendungsbereich übersteigt. Die Benutzbarkeit wurde anhand von zwei Benutzerstudien tiefergehend untersucht. Dabei sind erste Erkenntnisse über die praktische Nutzung der Testbeschreibungssprache sowie zukünftige Fragestellungen aufgedeckt worden. In der ersten Studie wurde das Verstehen von Testfällen untersucht und dabei die automatisierbare Testbeschreibungssprache mit einer klassischen Testbeschreibungsvorlage verglichen. Hinsichtlich der Bearbeitungsdauer wurde ein signifikanter Effekt zugunsten der automatisierbaren Sprache ermittelt. Die zweite Studie betrachtet das Spezifizieren von Testfällen. Auch hier wurden Vorteile hinsichtlich der Bearbeitungsdauer aufgedeckt.

Die Konstruktion von Systemen oder Modellen, welche unter Unsicherheit und Umweltschwankungen robust arbeiten, ist eine zentrale Herausforderung sowohl im Ingenieurwesen als auch in den Naturwissenschaften. Dies ist im Design-Zentrierungsproblem formalisiert als das Finden eines Designs, welches vorgegebene Spezifikationen erfüllt und dies mit einer hohen Wahrscheinlichkeit auch noch tut, wenn die Systemparameter oder die Spezifikationen zufällig schwanken. Das Finden des Zentrums wird oft durch das Problem der Quantifizierung der Robustheit eines Systems begleitet. Hier stellen wir eine neue adaptive statistische Methode vor, um beide Probleme gleichzeitig zu lösen. Unsere Methode, Lp-Adaptation, ist durch Robustheit in biologischen Systemen und durch randomisierte Lösungen für konvexe Volumenberechnung inspiriert. Lp-Adaptation ist in der Lage, beide Probleme im allgemeinen, nicht-konvexen Fall und bei niedrigen Rechenkosten zu lösen. In dieser Arbeit beschreiben wir die Konzepte des Algorithmus und seine einzelnen Schritte. Wir testen ihn dann anhand bekannter Vergleichsfälle und zeigen seine Anwendbarkeit in elektronischen und biologischen Systemen. In allen Fällen übertrifft das vorliegende Verfahren den bisherigen Stand der Technik. Dies ermöglicht die Umformulierung von Optimierungsproblemen im Ingenieurwesen und in der Biologie als Design-Zentrierungsprobleme unter Berücksichtigung der globalen Robustheit des Systems. / The design of systems or models that work robustly under uncertainty and environmental fluctuations is a key challenge in both engineering and science. This is formalized in the design centering problem, defined as finding a design that fulfills given specifications and has a high probability of still doing so if the system parameters or the specifications randomly fluctuate. Design centering is often accompanied by the problem of quantifying the robustness of a system. Here we present a novel adaptive statistical method to simultaneously address both problems. Our method, Lp-Adaptation, is inspired by how robustness evolves in biological systems and by randomized schemes for convex volume computation. It is able to address both problems in the general, non-convex case and at low computational cost. In this thesis, we describe the concepts of the algorithm and detail its steps. We then test it on known benchmarks, and demonstrate its real-world applicability in electronic and biological systems. In all cases, the present method outperforms the previous state of the art. This enables re-formulating optimization problems in engineering and biology as design centering problems, taking global system robustness into account.

Adaptability is an intrinsic property of software systems that require adaptation to cope with dynamically changing environments. Achieving adaptability is challenging. Variability is a key solution as it enables a software system to change its behavior which corresponds to a specific need. The abstraction of variability is to manage variants, which are dynamic parts to be composed to the base system. Run-time variability realizes these variant compositions dynamically at run time to enable adaptation. Adaptation, relying on variants specified at build time, is called anticipated adaptation, which allows the system behavior to change with respect to a set of predefined execution environments. This implies the inability to solve practical problems in which the execution environment is not completely fixed and often unknown until run time. Enabling unanticipated adaptation, which allows variants to be dynamically added at run time, alleviates this inability, but it holds several implications yielding system instability such as inconsistency and run-time failures. Adaptation should be performed only when a system reaches a consistent state to avoid inconsistency. Inconsistency is an effect of adaptation happening when the system changes the state and behavior while a series of methods is still invoking. A software bug is another source of system instability. It often appears in a variant composition and is brought to the system during adaptation. The problem is even more critical for unanticipated adaptation as the system has no prior knowledge of the new variants. This dissertation aims to achieve anticipated and unanticipated adaptation. In achieving adaptation, the issues of inconsistency and software failures, which may happen as a consequence of run-time adaptation, are evidently addressed as well. Roles encapsulate dynamic behavior used to adapt players representing the base system, which is the rationale to select roles as the software system's variants. Based on the role concept, this dissertation presents three mechanisms to comprehensively address adaptation. First, a dynamic instance binding mechanism is proposed to loosely bind players and roles. Dynamic binding of roles enables anticipated and unanticipated adaptation. Second, an object-level tranquility mechanism is proposed to avoid inconsistency by allowing a player object to adapt only when its consistent state is reached. Last, a rollback recovery mechanism is proposed as a proactive mechanism to embrace and handle failures resulting from a defective composition of variants. A checkpoint of a system configuration is created before adaptation. If a specialized bug sensor detects a failure, the system rolls back to the most recent checkpoint. These mechanisms are integrated into a role-based runtime, called LyRT. LyRT was validated with three case studies to demonstrate the practical feasibility. This validation showed that LyRT is more advanced than the existing variability approaches with respect to adaptation due to its consistency control and failure handling. Besides, several benchmarks were set up to quantify the overhead of LyRT concerning the execution time of adaptation. The results revealed that the overhead introduced to achieve anticipated and unanticipated adaptation to be small enough for practical use in adaptive software systems. Thus, LyRT is suitable for adaptive software systems that frequently require the adaptation of large sets of objects.

The analysis of dynamic systems provides insights into their time-dependent characteristics. This enables us to monitor, evaluate, and improve systems from various areas. They are often represented as graphs that model the system's components and their relations. The analysis of the resulting dynamic graphs yields great insights into the system's underlying structure, its characteristics, as well as properties of single components. The interpretation of these results can help us understand how a system works and how parameters influence its performance. This knowledge supports the design of new systems and the improvement of existing ones. The main issue in this scenario is the performance of analyzing the dynamic graph to obtain relevant properties. While various approaches have been developed to analyze dynamic graphs, it is not always clear which one performs best for the analysis of a specific graph. The runtime also depends on many other factors, including the size and topology of the graph, the frequency of changes, and the data structures used to represent the graph in memory. While the benefits and drawbacks of many data structures are well-known, their runtime is hard to predict when used for the representation of dynamic graphs. Hence, tools are required to benchmark and compare different algorithms for the computation of graph properties and data structures for the representation of dynamic graphs in memory. Based on deeper insights into their performance, new algorithms can be developed and efficient data structures can be selected. In this thesis, we present four contributions to tackle these problems: A benchmarking framework for dynamic graph analysis, novel algorithms for the efficient analysis of dynamic graphs, an approach for the parallelization of dynamic graph analysis, and a novel paradigm to select and adapt graph data structures. In addition, we present three use cases from the areas of social, computer, and biological networks to illustrate the great insights provided by their graph-based analysis. We present a new benchmarking framework for the analysis of dynamic graphs, the Dynamic Network Analyzer (DNA). It provides tools to benchmark and compare different algorithms for the analysis of dynamic graphs as well as the data structures used to represent them in memory. DNA supports the development of new algorithms and the automatic verification of their results. Its visualization component provides different ways to represent dynamic graphs and the results of their analysis. We introduce three new stream-based algorithms for the analysis of dynamic graphs. We evaluate their performance on synthetic as well as real-world dynamic graphs and compare their runtimes to snapshot-based algorithms. Our results show great performance gains for all three algorithms. The new stream-based algorithm StreaM_k, which counts the frequencies of k-vertex motifs, achieves speedups up to 19,043 x for synthetic and 2882 x for real-world datasets. We present a novel approach for the distributed processing of dynamic graphs, called parallel Dynamic Graph Analysis (pDNA). To analyze a dynamic graph, the work is distributed by a partitioner that creates subgraphs and assigns them to workers. They compute the properties of their respective subgraph using standard algorithms. Their results are used by the collator component to merge them to the properties of the original graph. We evaluate the performance of pDNA for the computation of five graph properties on two real-world dynamic graphs with up to 32 workers. Our approach achieves great speedups, especially for the analysis of complex graph measures. We introduce two novel approaches for the selection of efficient graph data structures. The compile-time approach estimates the workload of an analysis after an initial profiling phase and recommends efficient data structures based on benchmarking results. It achieves speedups of up to 5.4 x over baseline data structure configurations for the analysis of real-word dynamic graphs. The run-time approach monitors the workload during analysis and exchanges the graph representation if it finds a configuration that promises to be more efficient for the current workload. Compared to baseline configurations, it achieves speedups up to 7.3 x for the analysis of a synthetic workload. Our contributions provide novel approaches for the efficient analysis of dynamic graphs and tools to further investigate the trade-offs between different factors that influence the performance.

This diploma thesis attempts to improve the language-support for coping with the problem of negative emergence in dynamic Systems-of-Systems (SoS). Negative emergence is understood to be the emergence of unintended behaviour among constituent systems of a SoS in response to certain changes to the composition of constituent systems in the SoS. The architecture description language (ADL) "EventArch 2.0" approaches this problem by allowing the SoS-manager to define certain rules to manipulate the original behaviour of certain constituent systems at certain critical points of execution of the SoS to prevent unintended behaviour ("coordination rules"). This thesis approaches a solution to the following problem: to prevent the introduction of unintended behaviour through overly- or underly-restrictive coordination rules, more- or less-restrictive variants of a coordination rule would have to be applied to the SoS depending on the current composition of constituent systems in the SoS. This thesis has the goal to approach this problem by devising a mechanism to dynamically exchange a coordination rule depending on the current composition of constituent systems in the SoS. To achieve that goal, the ADL "EventArch 2.0" is extended to support the dynamic application of a coordination rule to a System-of-Systems. The dynamic application is achieved by connecting coordinators and constituent systems at runtime. As a special characteristic, each coordinator is dedicated to a specific constituent system and is responsible for achieving compliance of that system with respect to a specific coordination rule. It is shown that this architectural setup can be nicely modeled using concepts from the field of "role-based modeling". The solution does therefore employ concepts that are central to the "role-based modeling"-approach: "Role", "Base", and "Compartment". The applicability of the extended language to practical coordination-problems is shown by applying it to a constructed use case in the field of energy-efficient computing. / Die vorliegende Diplomarbeit ist mit der Verbesserung der Sprachunterstützung zur Vermeidung negativer Emergenz in dynamischen Systems-of-Systems (SoS) befasst. Negative Emergenz wird dabei als unerwünschtes Verhalten von an einem SoS beteiligten Systemen verstanden, welches auf Grund von Änderungen in der Zusammensetzung des SoS (d.h. auf Grund des Eintritts oder Austritts von konstituierenden Systemen) aufgetreten ist. Die Architekturbeschreibungssprache "EventArch 2.0" unterstützt den SoS-manager bei der Lösung dieses Problems durch die Möglichkeit das Verhalten der beteiligten Systeme in bestimmten Ausführungsmomenten durch die Definition von Koordinationsregeln zu manipulieren und auf diesem Wege das Auftreten negativer Emergenz zu vermeiden. Die Diplomarbeit ist ein Beitrag zur Lösung des folgenden Problems: Um die Einführung von unerwünschtem Verhalten durch übermäßig- oder unzureichend restriktive Koordinationsregeln zu verhindern, müssten unterschiedliche Varianten einer Koordinationsregel, die sich im Grade ihrer Restriktivität unterscheiden, auf das SoS angewendet werden. Diese Anwendung müßte in Abhängigkeit der aktuellen Zusammensetzung des SoS aus konstituierenden Systemen erfolgen. In der vorliegenden Diplomarbeit wird eine Möglichkeit entwickelt um eine Koordinationsregel zur Laufzeit in Abhängigkeit der aktuellen Zusammensetzung des SoS aus konstituierenden Systemen auszutauschen. Sie leistet damit einen Beitrag zur Lösung des vorgenannten Problems. In der Arbeit wird die Architekturbeschreibungssprache "EventArch 2.0" um die Möglichkeit des dynamischen Austausches von Koordinationsregeln erweitert. Dabei werden Koordinationsregeln angewendet durch die gezielte Verbindung von Koordinatoren und konstituierenden Systemen. Die Besonderheit des Ansatzes besteht darin, dass jedem konstituierenden System ein persönlicher Koordinator zugeordnet wird, d.h. ein Koordinator der ausschließlich für die Anpassung des Verhaltens des jeweiligen Systems an eine bestimmte Koordinationsregel verantwortlich ist. In der Arbeit wird gezeigt, dass dieser architektonische Ansatz durch zentrale Konzepte des Modellierungsansatzes "rollenbasierte Modellierung" modelliert werden kann. In der entwickelten Spracherweiterung werden daher die Konzepte "Rolle", "Basis" und "Compartment" verwendet. Die Anwendbarkeit der erweiterten Sprache, wird durch deren Anwendung auf einen konstruierten Anwendungsfall aus dem Bereich der Energie-effizienten Berechnung gezeigt.

Machine learning of distributed word representations with neural embeddings is a state-of-the-art approach to modelling semantic relationships hidden in natural language. The thesis “Clustering of Distributed Word Representations and its Applicability for Enterprise Search” covers different aspects of how such a model can be applied to knowledge management in enterprises. A review of distributed word representations and related language modelling techniques, combined with an overview of applicable clustering algorithms, constitutes the basis for practical studies. The latter have two goals: firstly, they examine the quality of German embedding models trained with gensim and a selected choice of parameter configurations. Secondly, clusterings conducted on the resulting word representations are evaluated against the objective of retrieving immediate semantic relations for a given term. The application of the final results to company-wide knowledge management is subsequently outlined by the example of the platform intergator and conceptual extensions."

Software Product Lines (SPLs) and Software Ecosystems (SECOs) are approaches to capturing families of closely related software systems in terms of common and variable functionality (variability in space). SPLs and especially SECOs are subject to software evolution to adapt to new or changed requirements resulting in different versions of the software family and its variable assets (variability in time). Both dimensions may be interconnected (e.g., through version incompatibilities) and, thus, have to be handled simultaneously as not all customers upgrade their respective products immediately or completely. However, there currently is no integrated approach allowing variant derivation of features in different version combinations. In this thesis, remedy is provided in the form of an integrated approach making contributions in three areas: (1) As variability model, Hyper-Feature Models (HFMs) and a version-aware constraint language are introduced to conceptually capture variability in time as features and feature versions. (2) As variability realization mechanism, delta modeling is extended for variability in time, and a language creation infrastructure is provided to devise suitable delta languages. (3) For the variant derivation procedure, an automatic version selection mechanism is presented as well as a procedure to derive large parts of the application order for delta modules from the structure of the HFM. The presented integrated approach enables derivation of concrete software systems from an SPL or a SECO where both features and feature versions may be configured.