The recent development of Integrated Power Systems (IPS) for shipboard application has opened the horizon to introduce new technologies that address the increasing power demand along with the associated performance specifications. Similarly, the Shipboard Power System (SPS) features system components with multiple dynamic characteristics and require stringent regulations, leveraging a challenge for an efficient system level management. The shipboard power management needs to support the survivability, reliability, autonomy, and economy as the key features for design consideration. To address these multiple issues for an increasing system load and to embrace future technologies, an autonomic power management framework is required to maintain the system level objectives. To address the lack of the efficient management scheme, a generic model-based holistic power management framework is developed for naval SPS applications. The relationship between the system parameters are introduced in the form of models to be used by the model-based predictive controller for achieving the various power management goals. An intelligent diagnostic support system is developed to support the decision making capabilities of the main framework. Naïve Bayes’ theorem is used to classify the status of SPS to help dispatch the appropriate controls. A voltage control module is developed and implemented on a real-time test bed to verify the computation time. Variants of the limited look-ahead controls (LLC) are used throughout the dissertation to support the management framework design. Additionally, the ARIMA prediction is embedded in the approach to forecast the environmental variables in the system design. The developed generic framework binds the multiple functionalities in the form of overall system modules. Finally, the dissertation develops the distributed controller using the Interaction Balance Principle to solve the interconnected subsystem optimization problem. The LLC approach is used at the local level, and the conjugate gradient method coordinates all the lower level controllers to achieve the overall optimal solution. This novel approach provides better computing performance, more flexibility in design, and improved fault handling. The case-study demonstrates the applicability of the method and compares with the centralized approach. In addition, several measures to characterize the performance of the distributed controls approach are studied.

Enterprise applications in virtualized data centers are often subject to time-varying workloads, i.e., the load intensity and request mix change over time, due to seasonal patterns and trends, or unpredictable bursts in user requests. Varying workloads result in frequently changing resource demands to the underlying hardware infrastructure. Virtualization technologies enable sharing and on-demand allocation of hardware resources between multiple applications. In this context, the resource allocations to virtualized applications should be continuously adapted in an elastic fashion, so that "at each point in time the available resources match the current demand as closely as possible" (Herbst el al., 2013). Autonomic approaches to resource management promise significant increases in resource efficiency while avoiding violations of performance and availability requirements during peak workloads. Traditional approaches for autonomic resource management use threshold-based rules (e.g., Amazon EC2) that execute pre-defined reconfiguration actions when a metric reaches a certain threshold (e.g., high resource utilization or load imbalance). However, many business-critical applications are subject to Service-Level-Objectives defined on an application performance metric (e.g., response time or throughput). To determine thresholds so that the end-to-end application SLO is fulfilled poses a major challenge due to the complex relationship between the resource allocation to an application and the application performance. Furthermore, threshold-based approaches are inherently prone to an oscillating behavior resulting in unnecessary reconfigurations. In order to overcome the deficiencies of threshold-based approaches and enable a fully automated approach to dynamically control the resource allocations of virtualized applications, model-based approaches are required that can predict the impact of a reconfiguration on the application performance in advance. However, existing model-based approaches are severely limited in their learning capabilities. They either require complete performance models of the application as input, or use a pre-identified model structure and only learn certain model parameters from empirical data at run-time. The former requires high manual efforts and deep system knowledge to create the performance models. The latter does not provide the flexibility to capture the specifics of complex and heterogeneous system architectures. This thesis presents a self-aware approach to the resource management in virtualized data centers. In this context, self-aware means that it automatically learns performance models of the application and the virtualized infrastructure and reasons based on these models to autonomically adapt the resource allocations in accordance with given application SLOs. Learning a performance model requires the extraction of the model structure representing the system architecture as well as the estimation of model parameters, such as resource demands. The estimation of resource demands is a key challenge as they cannot be observed directly in most systems. The major scientific contributions of this thesis are: - A reference architecture for online model learning in virtualized systems. Our reference architecture is based on a set of model extraction agents. Each agent focuses on specific tasks to automatically create and update model skeletons capturing its local knowledge of the system and collaborates with other agents to extract the structural parts of a global performance model of the system. We define different agent roles in the reference architecture and propose a model-based collaboration mechanism for the agents. The agents may be bundled within virtual appliances and may be tailored to include knowledge about the software stack deployed in a specific virtual appliance. - An online method for the statistical estimation of resource demands. For a given request processed by an application, the resource time consumed for a specified resource within the system (e.g., CPU or I/O device), referred to as resource demand, is the total average time the resource is busy processing the request. A request could be any unit of work (e.g., web page request, database transaction, batch job) processed by the system. We provide a systematization of existing statistical approaches to resource demand estimation and conduct an extensive experimental comparison to evaluate the accuracy of these approaches. We propose a novel method to automatically select estimation approaches and demonstrate that it increases the robustness and accuracy of the estimated resource demands significantly. - Model-based controllers for autonomic vertical scaling of virtualized applications. We design two controllers based on online model-based reasoning techniques in order to vertically scale applications at run-time in accordance with application SLOs. The controllers exploit the knowledge from the automatically extracted performance models when determining necessary reconfigurations. The first controller adds and removes virtual CPUs to an application depending on the current demand. It uses a layered performance model to also consider the physical resource contention when determining the required resources. The second controller adapts the resource allocations proactively to ensure the availability of the application during workload peaks and avoid reconfiguration during phases of high workload. We demonstrate the applicability of our approach in current virtualized environments and show its effectiveness leading to significant increases in resource efficiency and improvements of the application performance and availability under time-varying workloads. The evaluation of our approach is based on two case studies representative of widely used enterprise applications in virtualized data centers. In our case studies, we were able to reduce the amount of required CPU resources by up to 23% and the number of reconfigurations by up to 95% compared to a rule-based approach while ensuring full compliance with application SLO. Furthermore, using workload forecasting techniques we were able to schedule expensive reconfigurations (e.g., changes to the memory size) during phases of load load and thus were able to reduce their impact on application availability by over 80% while significantly improving application performance compared to a reactive controller. The methods and techniques for resource demand estimation and vertical application scaling were developed and evaluated in close collaboration with VMware and Google. / Unternehmensanwendungen in virtualisierten Rechenzentren unterliegen häufig zeitabhängigen Arbeitslasten, d.h. die Lastintensität und der Anfragemix ändern sich mit der Zeit wegen saisonalen Mustern und Trends, sowie unvorhergesehenen Lastspitzen bei den Nutzeranfragen. Variierende Arbeitslasten führen dazu, dass sich die Ressourcenanforderungen an die darunterliegende Hardware-Infrastruktur häufig ändern. Virtualisierungstechniken erlauben die gemeinsame Nutzung und bedarfsgesteuerte Zuteilung von Hardware-Ressourcen zwischen mehreren Anwendungen. In diesem Zusammenhang sollte die Zuteilung von Ressourcen an virtualisierte Anwendungen fortwährend in einer elastischen Art und Weise angepasst werden, um sicherzustellen, dass "zu jedem Zeitpunkt die verfügbaren Ressourcen dem derzeitigen Bedarf möglichst genau entsprechen" (Herbst et al., 2013). Autonome Ansätze zur Ressourcenverwaltung versprechen eine deutliche Steigerung der Ressourceneffizienz wobei Verletzungen der Anforderungen hinsichtlich Performanz und Verfügbarkeit bei Lastspitzen vermieden werden. Herkömmliche Ansätze zur autonomen Ressourcenverwaltung nutzen feste Regeln (z.B., Amazon EC2), die vordefinierte Rekonfigurationen durchführen sobald eine Metrik einen bestimmten Schwellwert erreicht (z.B., hohe Ressourcenauslastung oder ungleichmäßige Lastverteilung). Viele geschäftskritische Anwendungen unterliegen jedoch Zielvorgaben hinsichtlich der Dienstgüte (SLO, engl. Service Level Objectives), die auf Performanzmetriken der Anwendung definiert sind (z.B., Antwortzeit oder Durchsatz). Die Bestimmung von Schwellwerten, sodass die Ende-zu-Ende Anwendungs-SLOs erfüllt werden, stellt aufgrund des komplexen Zusammenspiels zwischen der Ressourcenzuteilung und der Performanz einer Anwendung eine bedeutende Herausforderung dar. Des Weiteren sind Ansätze basierend auf Schwellwerten inhärent anfällig für Oszillationen, die zu überflüssigen Rekonfigurationen führen können. Um die Schwächen schwellwertbasierter Ansätze zu lösen und einen vollständig automatisierten Ansatz zur dynamischen Steuerung von Ressourcenzuteilungen virtualisierter Anwendungen zu ermöglichen, bedarf es modellbasierter Ansätze, die den Einfluss einer Rekonfiguration auf die Performanz einer Anwendung im Voraus vorhersagen können. Bestehende modellbasierte Ansätze sind jedoch stark eingeschränkt hinsichtlich ihrer Lernfähigkeiten. Sie erfordern entweder vollständige Performanzmodelle der Anwendung als Eingabe oder nutzen vorbestimmte Modellstrukturen und lernen nur bestimmte Modellparameter auf Basis von empirischen Daten zur Laufzeit. Erstere erfordern hohe manuelle Aufwände und eine tiefe Systemkenntnis um die Performanzmodelle zu erstellen. Letztere bieten nur eingeschränkte Möglichkeiten um die Besonderheiten von komplexen und heterogenen Systemarchitekturen zu erfassen. Diese Arbeit stellt einen selbstwahrnehmenden (engl. self-aware) Ansatz zur Ressourcenverwaltung in virtualisierten Rechenzentren vor. In diesem Zusammenhang bedeutet Selbstwahrnehmung, dass der Ansatz automatisch Performanzmodelle der Anwendung und der virtualisierten Infrastruktur lernt Basierend auf diesen Modellen entscheidet er autonom wie die Ressourcenzuteilungen angepasst werden, um die Anwendungs-SLOs zu erfüllen. Das Lernen von Performanzmodellen erfordert sowohl die Extraktion der Modellstruktur, die die Systemarchitektur abbildet, als auch die Schätzung von Modellparametern, wie zum Beispiel der Ressourcenverbräuche einzelner Funktionen. Die Schätzung der Ressourcenverbräuche stellt hier eine zentrale Herausforderung dar, da diese in den meisten Systemen nicht direkt gemessen werden können. Die wissenschaftlichen Hauptbeiträge dieser Arbeit sind wie folgt: - Eine Referenzarchitektur, die das Lernen von Modellen in virtualisierten Systemen während des Betriebs ermöglicht. Unsere Referenzarchitektur basiert auf einer Menge von Modellextraktionsagenten. Jeder Agent fokussiert sich auf bestimmte Aufgaben um automatisch ein Modellskeleton, das sein lokales Wissen über das System erfasst, zu erstellen und zu aktualisieren. Jeder Agent arbeitet mit anderen Agenten zusammen um die strukturellen Teile eines globalen Performanzmodells des Systems zu extrahieren. Die Rereferenzarchitektur definiert unterschiedliche Agentenrollen und beinhaltet einen modellbasierten Mechanismus, der die Kooperation unterschiedlicher Agenten ermöglicht. Die Agenten können als Teil virtuellen Appliances gebündelt werden und können dabei maßgeschneidertes Wissen über die Software-Strukturen in dieser virtuellen Appliance beinhalten. - Eine Methode zur fortwährenden statistischen Schätzung von Ressourcenverbräuchen. Der Ressourcenverbrauch (engl. resource demand) einer Anfrage, die von einer Anwendung verarbeitet wird, entspricht der Zeit, die an einer spezifischen Ressource im System (z.B., CPU oder I/O-Gerät) verbraucht wird. Eine Anfrage kann dabei eine beliebige Arbeitseinheit, die von einem System verarbeitet wird, darstellen (z.B. eine Webseitenanfrage, eine Datenbanktransaktion, oder ein Stapelverarbeitungsauftrag). Die vorliegende Arbeit bietet eine Systematisierung existierender Ansätze zur statistischen Schätzung von Ressourcenverbräuchen und führt einen umfangreichen, auf Experimenten aufbauenden Vergleich zur Bewertung der Genauigkeit dieser Ansätze durch. Es wird eine neuartige Methode zur automatischen Auswahl eines Schätzverfahrens vorgeschlagen und gezeigt, dass diese die Robustheit und Genauigkeit der geschätzten Ressourcenverbräuche maßgeblich verbessert. - Modellbasierte Regler für das autonome, vertikale Skalieren von virtualisierten Anwendungen. Es werden zwei Regler entworfen, die auf modellbasierten Entscheidungstechniken basieren, um Anwendungen zur Laufzeit vertikal in Übereinstimmung mit Anwendungs-SLOs zu skalieren. Die Regler nutzen das Wissen aus automatisch extrahierten Performanzmodellen bei der Bestimmung notwendiger Rekonfigurationen. Der erste Regler fügt virtuelle CPUs zu Anwendungen hinzu und entfernt sie wieder in Abhängigkeit vom aktuellen Bedarf. Er nutzt ein geschichtetes Performanzmodell, um bei der Bestimmung der benötigten Ressourcen die Konkurrenzsituation der physikalischen Ressourcen zu beachten. Der zweite Regler passt Ressourcenzuteilungen proaktiv an, um die Verfügbarkeit einer Anwendung während Lastspitzen sicherzustellen und Rekonfigurationen unter großer Last zu vermeiden. Die Arbeit demonstriert die Anwendbarkeit unseres Ansatzes in aktuellen virtualisierten Umgebungen und zeigt seine Effektivität bei der Erhöhung der Ressourceneffizienz und der Verbesserung der Anwendungsperformanz und -verfügbarkeit unter zeitabhängigen Arbeitslasten. Die Evaluation des Ansatzes basiert auf zwei Fallstudien, die repräsentativ für gängige Unternehmensanwendungen in virtualisierten Rechenzentren sind. In den Fallstudien wurde eine Reduzierung der benötigten CPU-Ressourcen von bis zu 23% und der Anzahl der Rekonfigurationen von bis zu 95% im Vergleich zu regel-basierten Ansätzen erreicht, bei gleichzeitiger Erfüllung der Anwendungs-SLOs. Mit Hilfe von Vorhersagetechniken für die Arbeitslast konnten außerdem aufwändige Rekonfigurationen (z.B., Änderungen bei der Menge an zugewiesenem Arbeitsspeicher) so geplant werden, dass sie in Phasen geringer Last durchgeführt werden. Dadurch konnten deren Auswirkungen auf die Verfügbarkeit der Anwendung um mehr als 80% verringert werden bei gleichzeitiger Verbesserung der Anwendungsperformanz verglichen mit einem reaktiven Regler. Die Methoden und Techniken zur Schätzung von Ressourcenverbräuchen und zur vertikalen Skalierung von Anwendungen wurden in enger Zusammenarbeit mit VMware und Google entwickelt und evaluiert.

Large scale enterprise applications are known to observe dynamic workload; provisioning correct capacity for these applications remains an important and challenging problem. Predicting high variability fluctuations in workload or the peak workload is difficult; erroneous predictions often lead to under-utilized systems or in some situations cause temporarily outage of an otherwise well provisioned web-site. Consequently, rather than provisioning server capacity to handle infrequent peak workloads, an alternate approach of dynamically provisioning capacity on-the-fly in response to workload fluctuations has become popular. Cloud platforms are particularly suited for such applications due to their ability to provision capacity when needed and charge for usage on pay-per-use basis. Cloud environments enable elastic provisioning by providing a variety of hardware configurations as well as mechanisms to add or remove server capacity. The first part of this thesis presents Kingfisher, a cost-aware system that provides a generalized provisioning framework for supporting elasticity in the cloud by (i) leveraging multiple mechanisms to reduce the time to transition to new configurations, and (ii) optimizing the selection of a virtual server configuration that minimize cost. Majority of these enterprise applications, deployed as web applications, are distributed or replicated with a multi-tier architecture. SLAs for such applications are often expressed as a high percentile of a performance metric, for e.g. 99 percentile of end to end response time is less than 1 sec. In the second part of this thesis I present a model driven technique which provisions a multi-tier application for such an SLA and is targeted for cloud platforms. Enterprises critically depend on these applications and often own large IT infrastructure to support the regular operation of these applications. However, provisioning for a peak load or for high percentile of response time could be prohibitively expensive. Thus there is a need of hybrid cloud model, where the enterprise uses its own private resources for the majority of its computing, but then "bursts" into the cloud when local resources are insufficient. I discuss a new system, namely Seagull, which performs dynamic provisioning over a hybrid cloud model by enabling cloud bursting. Finally, I describe a methodology to model the configuration patterns (i.e deployment topologies) of different control plane services of a cloud management system itself. I present a generic methodology, based on empirical profiling, which provides initial deployment configuration of a control plane service and also a mechanism which iteratively adjusts the configuration to avoid violation of control plane's Service Level Objective (SLO).

Utvecklingen av datorer och deras förmåga ökar klyftan mellan datorer och skapar en stormångfald av hårdvarutillgänglighet. Kapaciteten som datorer besitter varierar kraftigt ochdärmed även förmågan att hantera komplexa applikationer. Moderna applikationer erbju-der inställningar som användare kan anpassa för datorn, något som kräver att användarenhar tillräcklig kunskap för att förstå inställningarnas e ekt. Att välja ut de optimala in-ställningarna för datorn är en process som är både svår och tidskrävande. Detta arbetegranskar hur ett dynamiskt adaptivt system automatiskt kan anpassa inställningarna förapplikationen medan den kör, utan manuell extern inverkan. Systemet mäter kontinuerligtapplikationen och individuella teknikers prestanda. Baserat på mätresultat anpassar detadaptiva systemet applikationens inställningar. Genom att abstrahera funktionaliteten attjustera applikationen kan datorns kapacitet utnyttjas, utan varken användarens kunskapeller påverkan. Arbetet undersöker hur olika tekniker kan in uera applikationens prestandaoch genomför tester av ett dynamiskt anpassande systems förmåga att skräddarsy appli-kationen för exekveringsmiljön. / The development of computers and their ability increases the gap between computers andcreate a wide variety of hardware availability. The capacity that computers possess varyconsiderably and thus the ability to handle complex applications. Modern applicationsprovides settings that users can customize for the computer, which requires the user tohave su cient knowledge to understand the attitude of the e ect. To select the optimalsettings for your PC is a process that is both di cult and time consuming. This thesisexamines how a dynamically adaptive system can automatically adjust the settings for theapplication while it is running, without manual external impact. The system continuouslymonitors the application and individual technologies performance. Based on measurementsthe adaptive system can customize applications settings. By abstracting the functionalityto adjust the application the computer's capacity is utlizied, without neither the user'sknowledge or in uence. The thesis examines how various technologies can in uence ap-plication performance and conducts tests of a dynamically adaptive system's ability tocustomize the application for execution environment.

In this dissertation we describe the event processing autonomic computing reference architecture (EPACRA), an innovative reference architecture that solves many important problems related to adaptive root cause analysis and diagnosis (RCAD). Along with the research progress for defining EPACRA, we also identified a set of autonomic computing architecture patterns and proposed a new information seeking model called net-casting model. EPACRA is important because today, root cause analysis and diagnosis (RCAD) in enterprise systems is still largely performed manually by experienced system administrators. The goal of this research is to characterize, simplify, improve, and automate RCAD processes to ease selected tasks for system administrators and end-users. Research on RCAD processes involves three domains: (1) autonomic computing architecture patterns, (2) information seeking models, and (3) complex event processing (CEP) technologies. These domains as well as existing technologies and standards contribute to the synthesized knowledge of this dissertation. To minimize human involvement in RCAD, we investigated architecture patterns to be utilized in RCAD processes. We identified a set of autonomic computing architecture patterns and analyzed the interactions among the feedback loops in these individual architecture patterns and how the autonomic elements interact with each other. By illustrating the architecture patterns, we recognized ambiguity in the aggregator-escalator-peer pattern. This problem has been solved by adding a new architecture pattern, namely the chain-of-monitors pattern, to the lattice of autonomic computing architecture patterns. To facilitate the autonomic information seeking process, we developed the net-casting information seeking model. After identifying the commonalities among three traditional information seeking models, we defined the net-casting model as a five stage process and then tailored it to describe our automated RCAD process. One of the main contributions of this dissertation is an innovative autonomic computing reference architecture called event processing autonomic computing reference architecture (EPACRA). This reference architecture is based on (1) complex event processing (CEP) concepts, (2) autonomic computing architecture patterns, (3) real use-case workflows, and (4) our net-casting information seeking model. This reference architecture can be leveraged to relieve the system administrator’s burden of routinely performing RCAD tasks in a heterogeneous environment. EPACRA can be viewed as a variant of the IBM ACRA model—extended with CEP to deal with large event clouds in real-time environments. In the middle layer of the reference model, EPACRA introduces an innovative design referred to as use-case-unit—a use case is the scenario of an RCAD process initiated by a symptom—event processing network (EPN) for RCAD. Each use-case-unit EPN reflects our automation approach, including identification of events from the use cases and classifying those events into event types. Apart from defining individual event processing agents (EPAs) to process the different types of events, dynamically constructing use-case unit EPNs is also an innovative approach which may lead to fully autonomic RCAD systems in the future. Finally, this dissertation presents a case study for EPACRA. As a case study we use a prototype of a Web application intrusion detection tool to demonstrate the autonomic mechanisms of our RCAD process. Specifically, this tool recognizes two types of malicious attacks on web application systems and then takes actions to prevent intrusion attempts. This case study validates both our chain-of-monitors autonomic architecture pattern and our net-casting model. It also validates our use-case-unit EPN approach as an innovative approach to realizing RCAD workflows. Hopefully, this research platform will be beneficial for other RCAD projects and researchers with similar interests and goals.

Les applications logicielles sont de plus en plus diversifié et complexe. Avec le développement orageux du Cloud Computing et de ses applications, les applications logicielles deviennent encore plus complexes que jamais. Les applications de cloud computing complexes peuvent contenir un grand nombre de composants logiciels qui nécessitent et consomment une grande quantité de ressources (matériel ou d'autres composants logiciels) répartis en plusieurs niveaux en fonction de la granularité de ces ressources. En outre, ces composants logiciels peuvent être situés sur différents nuages. Les composants logiciels et de leurs ressources requises d'une application Nuage ont des relations complexes dont certains pourraient être résolus au moment de la conception, mais certains sont nécessaires pour faire face au moment de l'exécution. La complexité des logiciels et de l'hétérogénéité de l'environnement Couverture devenir défis que les solutions d'élasticité actuelles ont besoin de trouver des réponses appropriées à résoudre. L'élasticité est l'un des avantages du cloud computing, qui est la capacité d'un système Cloud pour adapter à la charge de travail des changements par des ressources d'approvisionnement et deprovisioning d'une manière autonome. Par conséquent, les ressources disponibles correspondent à la demande actuelle d'aussi près que possible à chaque moment. Pour avoir une solution d'élasticité efficace, qui ne reflète pas seulement la complexité des applications Cloud mais également à déployer et à gérer eux d'une manière autonome, nous proposons une approche d'élasticité roman. Il est appelé à plusieurs niveaux élasticité fine qui comprend deux aspects de la complexité de l'application: plusieurs composants logiciels et la granularité des ressources. Le multi-niveau élasticité fine concerne les objets touchés par les actions d'élasticité et la granularité de ces actions. Dans cette thèse, nous introduisons plateforme Roboconf un système de cloud computing autonome (ACCS) pour installer et reconfigurer les applications complexes ainsi que soutenir le multi-niveau élasticité fine. A cet effet, Roboconf est également un gestionnaire d'élasticité autonome. Merci à cette plate-forme, nous pouvons abstraire les applications cloud complexes et automatiser leur installation et de reconfiguration qui peut être de plusieurs centaines d'heures de travail. Nous utilisons également Roboconf à mettre en œuvre les algorithmes de multi-niveau élasticité fine sur ces applications. Les expériences menées indiquent non seulement l'efficacité de l'élasticité fine multi-niveau, mais aussi de valider les caractéristiques de support de cette approche de la plateforme Roboconf. / Software applications are becoming more diverse and complex. With the stormy development of Cloud Computing and its applications, software applications become even more complex than ever. The complex Cloud applications may contain a lot of software components that require and consume a large amount of resources (hardware or other software components) distributed into multiple levels based on granularity of these resources. Moreover these software components might be located on different clouds. The software components and their required resources of a Cloud application have complex relationships which some could be resolved at design time but some are required to tackle at run time. The complexity of software and heterogeneity of Cloud environment become challenges that current elasticity solutions need to find appropriate answers to resolve. Elasticity is one of benefits of Cloud computing, which is capability of a Cloud system to adapt to workload changes by provisioning and deprovisioning resources in an autonomic manner. Hence, the available resources fit the current demand as closely as possible at each point in time. To have an efficient elasticity solution which not only reflects the complexity of Cloud applications but also deploy and manage them in an autonomic manner, we propose a novel elasticity approach. It is called multi-level fine-grained elasticity which includes two aspects of application’s complexity: multiple software components and the granularity of resources. The multi-level fine-grained elasticity concerns objects impacted by elasticity actions and granularity of these actions. In this thesis, we introduce Roboconf platform an autonomic Cloud computing system (ACCS) to install and reconfigure the complex applications as well as support the multi-level fine-grained elasticity. To this end, Roboconf is also an autonomic elasticity manager. Thanks to this platform, we can abstract the complex Cloud applications and automate their installation and reconfiguration that can be up to several hundred hours of labour. We also use Roboconf to implement the algorithms of multi-level fine-grained elasticity on these applications. The conducted experiments not only indicate efficiency of the multi-level fine-grained elasticity but also validate features supporting this approach of Roboconf platform.

Sandvik har utvecklat en teknik för att placera sensorer i rör. Denna teknik har stor marknadspotential och kan effektivisera många industriprocesser. Den färdiga tjänsten ska kunna strömma sensordata till molntjänster för analys och avläsning.Deras nuvarande system kräver idag manuell konfiguration på plats och är komplicerad att installera. Denna uppsats undersöker hur systemets utrustning kan konfigureras automatiskt och hur ett system för underliggande IT-tjänster skulle kunna fungera.En lösning presenteras där många delar av installationsprocessen har automatiserats, samt en skiss för ett underliggande system.Lösningen utvärderas genom att utföra en mätning av konfigureringskomplexitet. Slutsatsen av utvärderingen var att det utvecklade system hade utökad funktionalitet, jämfört med dagens manuella tillvägagångssätt, och var inte mer komplex att konfigurera. I många avseenden mindre komplex. / Sandvik has developed a technique to place sensors inside tubes. This technology has great market potential and can optimize many industrial processes. The finished product should be able to stream sensor data to cloudservices for analysis and reading.The current system requires manual configuration on-site and the installation is labor intensive. This thesis investigates how the system’s hardware can be configured atomically, and how a supporting IT-system could function.A solution is presented where large portion of the installation process has been automated, along with an outline for a supporting system.The solution is evaluated by performing a measurement of the configuration complexity. The evaluation shows that the developed system had increased functionality compared to today’s manual configuration, configuration complexity was not increased. In many aspects, the configuration complexity was reduced.

<p>Advanced architectures for cluster-based services that have been recently proposed allow for service differentiation, server overload control and high utilization of resources. These systems, however, rely on centralized functions, which limit their ability to scale and to tolerate faults. In addition, they do not have built-in architectural support for automatic reconfiguration in case of failures or addition/removal of system components.</p><p>Recent research in peer-to-peer systems and distributed management has demonstrated the potential benefits of decentralized over centralized designs: a decentralized design can reduce the configuration complexity of a system and increase its scalability and fault tolerance.</p><p>This research focuses on introducing self-management capabilities into the design of cluster-based services. Its intended benefits are to make service platforms dynamically adapt to the needs of customers and to environment changes, while giving the service providers the capability to adjust operational policies at run-time.</p><p>We have developed a decentralized design that efficiently allocates resources among multiple services inside a server cluster. The design combines the advantages of both centralized and decentralized architectures. It allows associating a set of QoS objectives with each service. In case of overload or failures, the quality of service degrades in a controllable manner. We have evaluated the performance of our design through extensive simulations. The results have been compared with performance characteristics of ideal systems.</p>

Autonomic Computing (AC) aims at solving the problem of managing the rapidly-growing complexity of Information Technology systems, by creating self-managing systems. In this thesis, we have surveyed the progress of the AC field, and studied the requirements, models and architectures of AC. The commonly recognized AC requirements are four properties - self-configuring, self-healing, self-optimizing, and self-protecting. The recommended software architecture is the MAPE-K model containing four modules, namely - monitor, analyze, plan and execute, as well as the knowledge repository. In the modern software marketplace, Java Management Extensions (JMX) has facilitated one function of the AC requirements - monitoring. Using JMX, we implemented a package that attempts to assist programming for AC features including socket management, logging, and recovery of distributed computation. In the experiments, we have not only realized the powerful Java capabilities that are unknown to many educators, we also illustrated the feasibility of learning AC in senior computer science courses.

Le déploiement d'équipements informatiques à large échelle, sur les multiples infrastructures interconnectées de l'Internet, a eu un impact considérable sur la complexité de la tâche de gestion. L'informatique autonome permet de faire face à cet enjeu en spécifiant des objectifs de haut niveau et en déléguant les activités de gestion aux réseaux et systèmes eux-mêmes. Cependant, lorsque des changements sont opérés par les administrateurs ou par les équipements autonomes, des configurations vulnérables peuvent être involontairement introduites. Ces vulnérabilités offrent un point d'entrée pour des attaques de sécurité. À cet égard, les mécanismes de gestion des vulnérabilités sont essentiels pour assurer une configuration sûre de ces environnements. Cette thèse porte sur la conception et le développement de nouvelles méthodes et techniques pour la gestion des vulnérabilités dans les réseaux et systèmes autonomes, afin de leur permettre de détecter et de corriger leurs propres expositions aux failles de sécurité. Nous présenterons tout d'abord un état de l'art sur l'informatique autonome et la gestion de vulnérabilités. Nous décrirons ensuite notre approche d'intégration du processus de gestion des vulnérabilités dans ces environnements, et en détaillerons les différentes facettes, notamment : extension de l'approche dans le cas de vulnérabilités distribuées, prise en compte du facteur temps en considérant une historisation des paramètres de configuration, et application en environnements contraints en utilisant des techniques probabilistes. Nous présenterons également les prototypes et les résultats expérimentaux qui ont permis d'évaluer ces différentes contributions / Over the last years, the massive deployment of computing devices over disparate interconnected infrastructures has dramatically increased the complexity of network management. Autonomic computing has emerged as a novel paradigm to cope with this challenging reality. By specifying high-level objectives, autonomic computing aims at delegating management activities to the networks themselves. However, when changes are performed by administrators and self-governed entities, vulnerable configurations may be unknowingly introduced. Nowadays, vulnerabilities constitute the main entry point for security attacks. Therefore, vulnerability management mechanisms are vital to ensure safe configurations, and with them, the survivability of any autonomic environment. This thesis targets the design and development of novel autonomous mechanisms for dealing with vulnerabilities, in order to increase the security of autonomic networks and systems. We first present a comprehensive state of the art in autonomic computing and vulnerability management. Afterwards, we present our contributions which include autonomic assessment strategies for device-based vulnerabilities and extensions in several dimensions, namely, distributed vulnerabilities (spatial), past hidden vulnerable states (temporal), and mobile security assessment (technological). In addition, we present vulnerability remediation approaches able to autonomously bring networks and systems into secure states. The scientific approaches presented in this thesis have been largely validated by an extensive set of experiments which are also discussed in this manuscript