As virtualization platforms or cloud computing have become more of a commodity, many more organizations have been utilizing them. Many organizations and technologies have emerged to fulfill those cloud needs. Cloud vendors provide similar services, but the differences can have significant impact on specific applications. Selecting the right provider is difficult and confusing because of the number of options. It can be difficult to determine which application characteristics will impact the choice of implementation. There has not been a concise process to select which cloud vendor and characteristics are best suited for the application requirements and organization requirements. This thesis provides a model that identifies crucial application characteristics, organization requirements and also characteristics of a cloud. The model is used to analyze the interaction of the application with multiple cloud platforms and select the best option based on a suitability score. Case studies utilize this model to test three applications against three cloud implementations to identify the best fit cloud implementation. The model is further validated by a small group of peers through a survey. The studies show that the model is useful in identifying and comparing cloud implementations with regard to application requirements.

Energy efficiency is a major criterion for computing in general and High Performance Computing in particular. When optimizing for energy efficiency, it is essential to measure the underlying metric: energy consumption. To fully leverage energy measurements, their quality needs to be well-understood. To that end, this thesis provides a rigorous evaluation of various energy measurement techniques. I demonstrate how the deliberate selection of instrumentation points, sensors, and analog processing schemes can enhance the temporal and spatial resolution while preserving a well-known accuracy. Further, I evaluate a scalable energy measurement solution for production HPC systems and address its shortcomings. Such high-resolution and large-scale measurements present challenges regarding the management of large volumes of generated metric data. I address these challenges with a scalable infrastructure for collecting, storing, and analyzing metric data. With this infrastructure, I also introduce a novel persistent storage scheme for metric time series data, which allows efficient queries for aggregate timelines. To ensure that it satisfies the demanding requirements for scalable power measurements, I conduct an extensive performance evaluation and describe a productive deployment of the infrastructure. Finally, I describe different approaches and practical examples of analyses based on energy measurement data. In particular, I focus on the combination of energy measurements and application performance traces. However, interweaving fine-grained power recordings and application events requires accurately synchronized timestamps on both sides. To overcome this obstacle, I develop a resilient and automated technique for time synchronization, which utilizes crosscorrelation of a specifically influenced power measurement signal. Ultimately, this careful combination of sophisticated energy measurements and application performance traces yields a detailed insight into application and system energy efficiency at full-scale HPC systems and down to millisecond-range regions.:1 Introduction 2 Background and Related Work 2.1 Basic Concepts of Energy Measurements 2.1.1 Basics of Metrology 2.1.2 Measuring Voltage, Current, and Power 2.1.3 Measurement Signal Conditioning and Analog-to-Digital Conversion 2.2 Power Measurements for Computing Systems 2.2.1 Measuring Compute Nodes using External Power Meters 2.2.2 Custom Solutions for Measuring Compute Node Power 2.2.3 Measurement Solutions of System Integrators 2.2.4 CPU Energy Counters 2.2.5 Using Models to Determine Energy Consumption 2.3 Processing of Power Measurement Data 2.3.1 Time Series Databases 2.3.2 Data Center Monitoring Systems 2.4 Influences on the Energy Consumption of Computing Systems 2.4.1 Processor Power Consumption Breakdown 2.4.2 Energy-Efficient Hardware Configuration 2.5 HPC Performance and Energy Analysis 2.5.1 Performance Analysis Techniques 2.5.2 HPC Performance Analysis Tools 2.5.3 Combining Application and Power Measurements 2.6 Conclusion 3 Evaluating and Improving Energy Measurements 3.1 Description of the Systems Under Test 3.2 Instrumentation Points and Measurement Sensors 3.2.1 Analog Measurement at Voltage Regulators 3.2.2 Instrumentation with Hall Effect Transducers 3.2.3 Modular Instrumentation of DC Consumers 3.2.4 Optimal Wiring for Shunt-Based Measurements 3.2.5 Node-Level Instrumentation for HPC Systems 3.3 Analog Signal Conditioning and Analog-to-Digital Conversion 3.3.1 Signal Amplification 3.3.2 Analog Filtering and Analog-To-Digital Conversion 3.3.3 Integrated Solutions for High-Resolution Measurement 3.4 Accuracy Evaluation and Calibration 3.4.1 Synthetic Workloads for Evaluating Power Measurements 3.4.2 Improving and Evaluating the Accuracy of a Single-Node Measuring System 3.4.3 Absolute Accuracy Evaluation of a Many-Node Measuring System 3.5 Evaluating Temporal Granularity and Energy Correctness 3.5.1 Measurement Signal Bandwidth at Different Instrumentation Points 3.5.2 Retaining Energy Correctness During Digital Processing 3.6 Evaluating CPU Energy Counters 3.6.1 Energy Readouts with RAPL 3.6.2 Methodology 3.6.3 RAPL on Intel Sandy Bridge-EP 3.6.4 RAPL on Intel Haswell-EP and Skylake-SP 3.7 Conclusion 4 A Scalable Infrastructure for Processing Power Measurement Data 4.1 Requirements for Power Measurement Data Processing 4.2 Concepts and Implementation of Measurement Data Management 4.2.1 Message-Based Communication between Agents 4.2.2 Protocols 4.2.3 Application Programming Interfaces 4.2.4 Efficient Metric Time Series Storage and Retrieval 4.2.5 Hierarchical Timeline Aggregation 4.3 Performance Evaluation 4.3.1 Benchmark Hardware Specifications 4.3.2 Throughput in Symmetric Configuration with Replication 4.3.3 Throughput with Many Data Sources and Single Consumers 4.3.4 Temporary Storage in Message Queues 4.3.5 Persistent Metric Time Series Request Performance 4.3.6 Performance Comparison with Contemporary Time Series Storage Solutions 4.3.7 Practical Usage of MetricQ 4.4 Conclusion 5 Energy Efficiency Analysis 5.1 General Energy Efficiency Analysis Scenarios 5.1.1 Live Visualization of Power Measurements 5.1.2 Visualization of Long-Term Measurements 5.1.3 Integration in Application Performance Traces 5.1.4 Graphical Analysis of Application Power Traces 5.2 Correlating Power Measurements with Application Events 5.2.1 Challenges for Time Synchronization of Power Measurements 5.2.2 Reliable Automatic Time Synchronization with Correlation Sequences 5.2.3 Creating a Correlation Signal on a Power Measurement Channel 5.2.4 Processing the Correlation Signal and Measured Power Values 5.2.5 Common Oversampling of the Correlation Signals at Different Rates 5.2.6 Evaluation of Correlation and Time Synchronization 5.3 Use Cases for Application Power Traces 5.3.1 Analyzing Complex Power Anomalies 5.3.2 Quantifying C-State Transitions 5.3.3 Measuring the Dynamic Power Consumption of HPC Applications 5.4 Conclusion 6 Summary and Outlook

La complexité croissante des systèmes embarqués multi-coeur exige des structures de communication flexibles et capables de supporter de nombreuses requêtes de trafics au moment de l’exécution. Les Réseaux sur Puce (NoC) émergent comme la technologie de communication la plus prometteuse pour les SoCs (Systèmes sur Puce), du fait de leur plus grande flexibilité par rapport aux autres solutions comme les bus et les connexions points à points. Les NoCs sont devenus le standard comme support de communication pour les SoC, mais les outils d’évaluation de performances deviennent critiques pour ces systèmes. Les outils d’émulation sur FPGA accélèrent l’analyse comparative de NoC ainsi que l’exploration de l’espace de conception. Ces outils ont une grande précision et un faible temps d’exécution par rapport aux simulateurs de NoC. Un outil d’émulation basé sur FPGA est composé de dizaines ou de centaines de composants distribués. Ces composants doivent être correctement gérés afin d’exécuter différents scénarii d’évaluation de trafic. Pour cela, il faut être à même de re-programmer les composants, en utilisant un protocole standard qui permet alors de piloter l’émulateur de NoC sur FPGA. Ces protocoles facilitent l’intégration des composants d’émulation développés par différents concepteurs et simplifient la configuration des noeuds d’émulation sans resynthèse ainsi que l’extraction des résultats d’émulation. Bien que l’émulation matérielle de NoC soit assez difficile, il est important de valider de nouvelles architectures de NoC avec des trafics basés sur les applications réelles pour permettre d’obtenir des résultats plus précis. La génération de modèles de trafic basés sur des applications est une préoccupation majeure pour l’émulation de NoC. Les traces intégrant des informations de dépendances sont plus précises que les traces ordinaires, ceci pour un large éventail d’architectures de NoC. Cependant, elles ont tendance à être plus grosses que les traces originales et exigent plus de ressources FPGA. L’objectif de cette thèse est la synthèse de plateformes d’émulation de NoC sur FPGA pour les futurs systèmes embarqués multi-noeuds. Une recherche approfondie s’est portée sur les stratégies éventuelles pour la génération des modèles réalistes de trafic pour le NoC émulé sur FPGA, et pour la gestion des plateformes d’émulation en utilisant des protocoles standard inspirés des protocoles de réseaux informatiques. Une première contribution de cette thèse est une structure (« framework ») d’analyse de traces capable d’extraire les dépendances de paquets. La plateforme proposée analyse un ensemble de traces extraites d’une application embarquée basée sur l’échange de messages afin de construire un modèle de calcul (MoC). Un générateur de trafic (TG) intégrant cette dépendance est créé à partir du MoC proposé. Ce TG reproduit le motif de trafic d’une application pour une plateforme d’émulation sur FPGA. Une seconde contribution est une version allégée du protocole SNMP (Simple Network Management Protocol) pour la gestion d’une plateforme d’émulation de NoC sur FPGA. L’architecture de la plateforme d’émulation proposée est basée sur les concepts du protocole SNMP. Elle offre une interface standard de haut niveau pour les composants d’émulation fournis par le protocole SNMP. Ce protocole facilite également l’intégration de composants d’émulation créés par différents concepteurs. Une analyse prospective des futures architectures de NoC constitue également une contribution dans cette thèse. Dans cette analyse, une architecture conceptuelle d’un système embarqué multi-noeuds du futur constitue un modèle pour extraire les contraintes de ces réseaux. Un autre mécanisme présenté est un NoC tolérant aux pannes, basé sur l’utilisation de liens de contournement. Enfin, la dernière contribution repose sur une analyse de base des besoins des futurs NoC pour les outils d’émulation sur FPGA / The ever-increasing complexity of many-core embedded system applications demands a flexible communication structure capable of supporting different traffics requirements at run-time. The Networks-on-Chip (NoCs) emerge as the most promising communication technology for the modern many-cores SoC (System-on-Chip), whereby they have greater scalability than other solutions such as buses and point to point connections. As NoCs become de facto standard for on chip systems, NoC performance evaluation tools become critical for SoCs design. The FPGA based emulation platforms accelerate NoC benchmarking as well as design space exploration. Those platforms have high accuracy and low execution time in relation to NoC simulators. An FPGA-based emulation platform is composed by tens or hundreds of distributed components. These components should be timely managed in order to execute an evaluation scenario. There is a lack of standard protocols to drive FPGA-based NoC emulators. Such protocols could ease the integration of emulation components developed by different designers, as well as they could enable the configuration of the emulation nodes without FPGA re-synthesis and the extraction of emulation results. The NoC hardware emulation is quite challenging. It is important to validate new NoC architectures with realistic workloads, because they provide much more accurate results. The generation of applications traffic patterns is a key concern for NoC emulation. The dependency aware traces are an appealing solution for the generation of realistic traffic workloads. They are more accurate than ordinary traces for a broad range of NoC architectures because they contain packets dependencies information. However, they tend to be bigger than the original ones what demands more FPGA resources. This thesis aims the synthesis of FPGA-based NoC emulation platforms for the future multi-core embedded systems. We are interested in investigating strategies to generate realistic traffic patterns for NoCs emulated on FPGAs, as well as the management of the emulation platform using standard protocols inspired by the computer networks protocols. One contribution of this thesis is a trace analysis framework which addresses the packets dependencies extraction problem. The proposed framework analyzes traces from a message passing application in order to build a Model of Computation (MoC). This MoC reproduces the communicative behavior of an application node. A dependency-aware Traffic Generator (TG) is created from the proposed MoC. This TG generates the application traffic pattern during an FPGA-based NoC emulation. Another contribution is a light version of SNMP (Simple Network Management Protocol) to manage an FPGA-based NoC emulation platform. An FPGA-based emulation platform architecture is proposed based on the principles of SNMP protocol. This platform has a high-level interface to the emulation components provided by that protocol, which also eases the integration of emulation components created by different designers. The emulation platform and the protocol capacities are evaluated during a task mapping and mesh topology design space exploration. A prospective analysis of future NoCs architectures is also a contribution of this thesis. In this analysis, a conceptual architecture of a future multi-core embedded system is used as model to extract these networks requirements. From this analysis, it is proposed some networking mechanisms. The first mechanism is a congestion-aware routing algorithm, which is an adaptive routing algorithm that selects the output path for a given packet based on a simple prioritized scheme of sets of rules. It is also proposed a congestion-control mechanisms for the vertical links interconnecting the layers of a 3D NoC. This mechanism is based upon the diffusion of congestion information by a piggyback protocol

This work deals with the classification methods used in the knowledge discovery from data process and discusses the possibilities of their validation and comparison. Through experiments, the work focuses on the analysis of four selected methods: Naive Bayes classificator, decision tree, neural network and SVM. Factors influencing basic characteristics such as training speed, classification speed, accuracy are examined. A part of the thesis is a desktop application, which is a tool for training, testing and validation of individual methods. Eleven reference data sets are selected for experimental purposes. At the end of this work experimental results of comparison and observed characteristics of classification methods are summarized.