Dissertations / Theses on the topic 'Autonomic computing'

In algorithmic computing, the program follows a predefined set of rules – the algorithm. The analyst/designer of the program analyzes the intended tasks of the program, defines the rules for its expected behaviour and programs the implementation. The creators of algorithmic software must therefore foresee, identify and implement all possible cases for its behaviour in the future application! However, what if the problem is not fully defined? Or the environment is uncertain? What if situations are too complex to be predicted? Or the environment is changing dynamically? In many such cases algorithmic computing fails. In such situations, the software needs an additional degree of freedom: Autonomy! Autonomy allows software to adapt to partially defined problems, to uncertain or dynamically changing environments and to situations that are too complex to be predicted. As more and more applications – such as autonomous cars and planes, adaptive power grid management, survivable networks, and many more – fall into this category, a gradual switch from algorithmic computing to autonomic computing takes place. Autonomic computing has become an important software engineering discipline with a rich literature, an active research community, and a growing number of applications.:Introduction 5 1 A Process Data Based Autonomic Optimization of Energy Efficiency in Manufacturing Processes, Daniel Höschele 9 2 Eine autonome Optimierung der Stabilität von Produktionsprozessen auf Basis von Prozessdaten, Richard Horn 25 3 Assuring Safety in Autonomous Systems, Christian Rose 41 4 MAPE-K in der Praxis - Grundlage für eine mögliche automatische Ressourcenzuweisung, in der Cloud Michael Schneider 59

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.

Heterogeneity is increasing across all levels of computing, with the rise of accelerators such as GPUs, FPGAs, and other coprocessors into everything from cell phones to supercomputers. More quietly it is increasing with the rise of NUMA systems, hierarchical caching, OS noise, and a myriad of other factors. As heterogeneity becomes a fact of life, efficiently managing heterogeneous compute resources is becoming a critical, and ever more complex, task. The focus of this dissertation is to lay the foundation for an autonomic system for heterogeneous computing, employing runtime adaptation to improve performance portability and performance consistency while maintaining or increasing programmability. We investigate heterogeneity arising from a myriad of factors, grouped into the dimensions of locality and capability. This work has resulted in runtime schedulers capable of automatically detecting and mitigating heterogeneity in physically homogeneous systems through MPI and adaptive coscheduling for physically heterogeneous accelerator based systems as well as a synthesis of the two to address multiple levels of heterogeneity as a coherent whole. We also discuss our current work towards the next generation of fine-grained scheduling and synchronization across heterogeneous platforms in the design of a highly-scalable and portable concurrent queue for many-core systems. Each component addresses aspects of the urgent need for automated management of the extreme and ever expanding complexity introduced by heterogeneity.
Ph. D.

The power consumption of data centers and cloud systems has increased almost three times between 2007 and 2012. The traditional resource allocation methods are typically designed for high performance as the primary objective to support peak resource requirements. However, it is shown that server utilization is between 12% and 18%, while the power consumption is close to those at peak loads. Hence, there is a pressing need for devising sophisticated resource management approaches. State of the art dynamic resource management schemes typically rely on only a single resource such as core number, core speed, memory, disk, and network. There is a lack of fundamental research on methods addressing dynamic management of multiple resources and properties with the objective of allocating just enough resources for each workload to meet quality of service requirements while optimizing for power consumption. The main focus of this dissertation is to simultaneously manage power and performance for large cloud systems. The objective of this research is to develop a framework of performance and power management and investigate a general methodology for an integrated autonomic cloud management. In this dissertation, we developed an autonomic management framework based on a novel data structure, AppFlow, used for modeling current and near-term future cloud application behavior. We have developed the following capabilities for the performance and power management of the cloud computing systems: 1) online modeling and characterizing the cloud application behavior and resource requirements; 2) predicting the application behavior to proactively optimize its operations at runtime; 3) a holistic optimization methodology for performance and power using number of cores, CPU frequency, and memory amount; and 4) an autonomic cloud management to support the dynamic change in VM configurations at runtime to simultaneously optimize multiple objectives including performance, power, availability, etc. We validated our approach using RUBiS benchmark (emulating eBay), on an IBM HS22 blade server. Our experimental results showed that our approach can lead to a significant reduction in power consumption upto 87% when compared to the static resource allocation strategy, 72% when compared to adaptive frequency scaling strategy, and 66% when compared to a multi-resource management strategy.

Efficient resource management is one of key problems associated with large-scale distributed computational systems. Taking into account their increasing complexity, inherent distribution and dynamism, such systems are required to adjust and adapt resources market that is offered by them at run-time and with minimal cost. However, as observed by major IT vendors such as IBM, SUN or HP, the very nature of such systems prevents any reliable and efficient control over their functioning through human administration. For this reason, autonomic system architectures capable of regulating their own functioning are suggested as the alternative solution to looming software complexity crisis. Here, large-scale infrastructures are assumed to comprise myriads of autonomic elements, each acting, learning or evolving separately in response to interactions in their local environments. The self-regulation of the whole system, in turn, becomes a product of local adaptations and interactions between system elements. Although many researchers suggest the application of multi-agent systems that are suitable for realising this vision, not much is known about regulatory mechanisms that are capable to achieve efficient organisation within a system comprising a population of locally and autonomously interacting agents. To address this problem, the aim of the work presented in this thesis was to understand how global system control can emerge out of such local interactions of individual system elements and to develop decentralised decision control mechanisms that are capable to employ this bottom-up self-organisation in order to preserve efficient resource management in dynamic and unpredictable system functioning conditions. To do so, we have identified the study of complex natural systems and their self-organising properties as an area of research that may deliver novel control solutions within the context of autonomic computing. In such a setting, a central challenge for the construction of distributed computational systems was to develop an engineering methodology that can exploit self-organising principles observed in natural systems. This, in particular, required to identify conditions and local mechanisms that give rise to useful self-organisation of interacting elements into structures that support required system functionality. To achieve this, we proposed an autonomic system model exploiting self-organising algorithms and its thermodynamic interpretation, providing a general understanding of self-organising processes that need to be taken into account within artificial systems exploiting self-organisation.

The advances in computing and communication technologies and software tools have resulted in an explosive growth in networked applications and information services that cover all aspects of our life. These services and applications are inherently complex, dynamic and heterogeneous. In a similar way, the underlying information infrastructure, e.g. the Internet, is large, complex, heterogeneous and dynamic, globally aggregating large numbers of independent computing and communication resources. The combination of the two results in application development and management complexities that break current computing paradigms, which are based on static behaviors. As a result, applications, programming environments and information infrastructures are rapidly becoming fragile, unmanageable and insecure. This has led researchers to consider alternative programming paradigms and management techniques that are based on strategies used by biological systems. Autonomic programming paradigm is inspired by the human autonomic nervous system that handles complexity, uncertainties and abnormality. The overarching goal of the autonomic programming paradigm is to help building systems and applications capable of self-management. Firstly, we investigated the large-scale scientific computing applications which generally experience different execution phases at run time and each phase has different computational, communication and storage requirements as well as different physical characteristics. In this dissertation, we present Physics Aware Optimization (PAO) paradigm that enables programmers to identify the appropriate solution methods to exploit the heterogeneity and the dynamism of the application execution states. We implement a Physics Aware Optimization Manager to exploit the PAO paradigm. On the other hand we present a self configuration paradigm based on the principles of autonomic computing that can handle efficiently complexity, dynamism and uncertainty in configuring server and networked systems and their applications. Our approach is based on making any resource/application to operate as an Autonomic Component (that means it can be self-managed component) by using our autonomic programming paradigm. Our POA technique for medical application yielded about 3X improvement of performance with 98.3% simulation accuracy compared to traditional techniques for performance optimization. Also, our Self-configuration management for power and performance management in GPU cluster demonstrated 53.7% power savings for CUDAworkload while maintaining the cluster performance within given acceptable thresholds.

Dissertação (mestrado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2016.
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Vemos um crescente interesse em aplicações que devem contar com ambientes de computação heterogêneos, como a Internet das Coisas (IoT). Esses aplicativos são destinados a executar em uma ampla gama de dispositivos com diferentes recursos computacionais disponíveis. Para lidar com algum tipo de heterogeneidade, como dois tipos possíveis de processadores gráficos em um computador pessoal, podemos usar abordagens simples como um script que escolhe a biblioteca de software certa a ser copiada para uma pasta. Essas abordagens simples são centralizadas e criadas em tempo de design. Eles requerem um especialista ou equipe para controlar todo o espaço de variabilidade. Dessa forma, essas abordagens não são escaláveis para ambientes altamente heterogêneos. Em ambientes altamente heterogêneos, é difícil prever o ambiente computacional em tempo de projeto, implicando provavelmente indecidibilidade na configuração correta para cada ambiente. Em nosso trabalho, propomos GoalD: um método que permite a implantação autônoma de sistemas, refletindo sobre os objetivos do sistema e seu ambiente computacional. Por implantação autônoma, queremos dizer que o sistema é capaz de encontrar o conjunto correto de componentes para o ambiente computacional alvo, sem intervenção humana. Nós avaliamos nossa abordagem em um estudo de caso: conselheiro de estação de abastecimento, onde uma aplicação aconselha um motorista onde reabastecer / recarregar seu veículo. Nós projetamos a aplicação com variabilidade em nível de requisitos, arquitetura e implantação, o que pode permitir que a aplicação projetada seja executada em diferentes dispositivos. Para cenários com diferentes ambientes, foi possível planejar a implantação de forma autônoma. Além disso, a escalabilidade do algoritmo que planeja a implantação foi avaliada em um ambiente simulado. Os resultados mostram que usando a abordagem é possível planejar de forma autônoma a implantação de um sistema com milhares de componentes em poucos segundos.
We see a growing interest in computing applications that should rely on heterogeneous computing environments, like Internet of Things (IoT). Such applications are intended to execute in a broad range of devices with different available computing resources. In order to handle some kind of heterogeneity, such as two possible types of graphical processors in a desktop computer, we can use simple approaches as a script at deployment-time that chooses the right software library to be copied to a folder. These simple approaches are centralized and created at design-time. They require one specialist or team to control the entire space of variability. However, such approaches are not scalable to highly heterogeneous environments. In highly dynamic and heterogeneous environment it is hard to predict the computing environment at design-time, implying likely undecidability on the correct configuration for each environment at design-time. In our work, we propose GoalD: a method that allows autonomous deployment of systems by reflecting about the goals of the system and its computing environment. By autonomous deployment, we mean that the system can find the correct set of components, for the target computing environment, without human intervention. We evaluate our approach on the filling station advisor case study where an application advises a driver where to refuel/recharge its vehicle. We design the application with variability at requirements, architecture, and deployment, which can allow the designed application be executed in different devices. For scenarios with different environments, it was possible to plan the deployment autonomously. Additionally, the scalability of the algorithm that plan the deployment was evaluated in a simulated environment. Results show that using the approach it is possible to autonomously plan the deployment of a system with thousands of components in few seconds.

Software has never been as important as today – and its impact on life, work and society is growing at an impressive rate. We are in the flow of a software-induced transformation of nearly all aspects of our way of life and work. The dependence on software has become almost total. Malfunctions and unavailability may threaten vital areas of our society, life and work at any time. The two massive challenges of software are one hand the complexity of the software and on the other hand the disruptive environment. Complexity of the software is a result of the size, the continuously growing functionality, the more complicated technology and the growing networking. The unfortunate consequence is that complexity leads to many problems in design, development, evolution and operation of software-systems, especially of large software-systems. All software-systems live in an environment. Many of today’s environments can be disruptive and cause severe problems for the systems and their users. Examples of disruptions are attacks, failures of partner systems or networks, faults in communications or malicious activities. Traditionally, both growing complexity and disruptions from the environment have been tackled by better and better software engineering. The development and operating processes are constantly being improved and more powerful engineering tools are introduced. For defending against disruptions, predictive methods – such as risk analysis or fault trees – are used. All this techniques are based on the ingenuity, experience and skills of the engineers! However, the growing complexity and the increasing intensity of possible disruptions from the environment make it more and more questionable, if people are really able to successfully cope with this raising challenge in the future. Already, serious research suggests that this is not the case anymore and that we need assistance from the software-systems themselves! Here enters “autonomic computing” – A promising branch of software science which enables software-systems with self-configuring, self-healing, self-optimization and self-protection capabilities. Autonomic computing systems are able to re-organize, optimize, defend and adapt themselves with no real-time human intervention. Autonomic computing relies on many branches of science – especially computer science, artificial intelligence, control theory, machine learning, multi-agent systems and more. Autonomic computing is an active research field which currently transfers many of its results into software engineering and many applications. This Hauptseminar offered the opportunity to learn about the fascinating technology “autonomic computing” and to do some personal research guided by a professor and assisted by the seminar peers.:Introduction 5 1 What Knowledge Does a Taxi Need? – Overview of Rule Based, Model Based and Reinforcement Learning Systems for Autonomic Computing (Anja Reusch) 11 2 Chancen und Risiken von Virtual Assistent Systemen (Felix Hanspach) 23 3 Evolution einer Microservice Architektur zu Autonomic Computing (Ilja Bauer) 37 4 Mögliche Einflüsse von autonomen Informationsdiensten auf ihre Nutzer (Jan Engelmohr) 49 5 The Benefits of Resolving the Trust Issues between Autonomic Computing Systems and their Users (Marc Kandler) 61

Apache Spark è un framework open source per la computazione distribuita su larga scala, caratterizzato da un engine in-memory che permette prestazioni superiori a soluzioni concorrenti nell’elaborazione di dati a riposo (batch) o in movimento (streaming). In questo lavoro presenteremo alcune tecniche progettate e implementate per migliorare l’elasticità e l’adattabilità del framework rispetto a modifiche dinamiche nell’ambiente di esecuzione o nel workload. Lo scopo primario di tali tecniche è di permettere ad applicazioni concorrenti di condividere le risorse fisiche disponibili nell’infrastruttura cluster sottostante in modo efficiente. Il contesto nel quale le applicazioni distribuite vengono eseguite difficilmente può essere considerato statico: le componenti hardware possono fallire, i processi possono interrompersi, gli utenti possono allocare risorse aggiuntive in modo imprevedibile nel tentativo di accelerare la computazione o di allegerire il carico di lavoro. Infine, non soltanto le risorse fisiche ma anche i dati in input possono variare di dimensione e complessità durante l’esecuzione, così che sia dati sia risorse non possano essere considerati statici. Una configurazione immutabile del cluster non riuscirà a ottenere la migliore efficienza possibile per tutti i differenti carichi di lavoro. Ne consegue che un framework per il calcolo distribuito che sia "consapevole" delle modifiche ambientali e delle modifiche al workload e che sia in grado di adattarsi a esse puo risultare piu performante di un framework che permetta unicamente configurazioni statiche. Gli esperimenti da noi compiuti con applicazioni Big Data altamente parallelizzabili mostrano come il costo della soluzione proposta sia minimo e come la nostra version di Spark più dinamica e adattiva possa portare a benefici in termini di flessibilità, scalabilità ed efficienza.

Self-management is the main objective of Autonomic Computing (AC), and it is needed to increase the running system's reliability, stability, and performance. Investigation some issues related to complex systems such as; self-awareness system, when and where an error state occurs, knowledge for system stabilization, analyze the problem, healing plan with different solutions for adaptation without the need for human intervention. This research work focuses on self-healing, which is the most important component of Autonomic Computing. Self-healing is a technique that has different phases, which aims to detect, analyze, and repair existing faults within the system. All of these phases are accomplished in a real-time system. In this approach, the system is capable of performing a reconfiguration action in order to recover from a permanent fault. Moreover, self- healing system should have the ability to modify its own behavior in response to changes within the environment. However, there are some challenges that still face the implementation of self-healing in real system adaptation. These challenges are monitoring, interpretation, resolution, and adaptation. Artificial Neural Networks have been proposed to overcome these challenges. Neural network proposed to minimize the error between the desired response and the actual output by modifying its weights. , ... ~' Furthermore, Neural Networks have a built-in capability to adapt their weights in nonstatinary environment, and that is required in real time problems as in self-healing systems. A recurrent neural network is used to show the ability of neural network to overcome the challenges associated with self-healing. A modified pipelined neural network is introduced to fulfill the requirements in this field. Two different applications were suggested and used to examine the validity of research work. Client server / / application has shown promising results comparing to the outcomes of feedforward -- neural network. Moreover, with the overcurrent relay experiment in the field of power system has achieved good results using pipelined recurrent neural network. The main point for the comparison between pipelined recurrent neural network and feedforward neural network is the continuous learning or online learning. This is important since autonomic systems aim to apply the monitoring of system behaviors and apply the suitable re configuration plan during the running time of the system.

The major motivation for self-adaptive computing systems is the self-adjustment of the software according to a changing environment. Adaptive computing systems can add, remove, and replace their own components in response to changes in the system itself and in the operating environment of a software system. Although these systems may provide a certain degree of confidence against new environments, their structural and behavioral changes should be validated after adaptation occurs at runtime. Testing dynamically adaptive systems is extremely challenging because both the structure and behavior of the system may change during its execution. After self adaptation occurs in autonomic software, new components may be integrated to the software system. When new components are incorporated, testing them becomes vital phase for ensuring that they will interact and behave as expected. When self adaptation is about removing existing components, a predefined test set may no longer be applicable due to changes in the program structure. Investigating techniques for dynamically updating regression tests after adaptation is therefore necessary to ensure such approaches can be applied in practice. We propose a model-driven approach that is based on change propagation for synchronizing a runtime test model for a software system with the model of its component structure after dynamic adaptation. A workflow and meta-model to support the approach was provided, referred to as Test Information Propagation (TIP). To demonstrate TIP, a prototype was developed that simulates a reductive and additive change to an autonomic, service-oriented healthcare application. To demonstrate the generalization of our TIP approach to be instantiated into the domain of up-to-date runtime testing for self-adaptive software systems, the TIP approach was applied to the self-adaptive JPacman 3.0 system. To measure the accuracy of the TIP engine, we consider and compare the work of a developer who manually identifyied changes that should be performed to update the test model after self-adaptation occurs in self-adaptive systems in our study. The experiments show how TIP is highly accurate for reductive change propagation across self-adaptive systems. Promising results have been achieved in simulating the additive changes as well.

In order to run and manage resource-intensive high-performance applications, large-scale computing and storage platforms have been evolving rapidly in various domains in both academia and industry. The energy expenditure consumed to operate and maintain these cloud computing infrastructures is a major factor to influence the overall profit and efficiency for most cloud service providers. Moreover, considering the mitigation of environmental damage from excessive carbon dioxide emission, the amount of power consumed by enterprise-scale data centers should be constrained for protection of the environment.Generally speaking, there exists a trade-off between power consumption and application performance in large-scale computing systems and how to balance these two factors has become an important topic for researchers and engineers in cloud and HPC communities. Therefore, minimizing the power usage while satisfying the Service Level Agreements have become one of the most desirable objectives in cloud computing research and implementation. Since the fundamental feature of the cloud computing platform is hosting workloads with a variety of characteristics in a consolidated and on-demand manner, it is demanding to explore the inherent relationship between power usage and machine configurations. Subsequently, with an understanding of these inherent relationships, researchers are able to develop effective power management policies to optimize productivity by balancing power usage and system performance. In this dissertation, we develop an autonomic power-aware system management framework for large-scale computer systems. We propose a series of techniques including coarse-grain power profiling, VM power modelling, power-aware resource auto-configuration and full-system power usage simulator. These techniques help us to understand the characteristics of power consumption of various system components. Based on these techniques, we are able to test various job scheduling strategies and develop resource management approaches to enhance the systems' power efficiency.

Thesis (M.A. in electrical engineering and computer science)--Washington State University, May 2010.
Title from PDF title page (viewed on May 3, 2010). "Department of Electrical Engineering and Computer Science." Includes bibliographical references (p. 110-117).

In den vergangenen Jahren ist es zu einer dramatischen Vervielfachung der verfügbaren Rechenzeit gekommen. Diese 'Grid Ressourcen' stehen jedoch nicht als kontinuierlicher Strom zur Verfügung, sondern sind über verschiedene Maschinentypen, Plattformen und Betriebssysteme verteilt, die jeweils durch Netzwerke mit fluktuierender Bandbreite verbunden sind.
Es wird für Wissenschaftler zunehmend schwieriger, die verfügbaren Ressourcen für ihre Anwendungen zu nutzen. Wir glauben, dass intelligente, selbstbestimmende Applikationen in der Lage sein sollten, ihre Ressourcen in einer dynamischen und heterogenen Umgebung selbst zu wählen: Migrierende Applikationen suchen eine neue Ressource, wenn die alte aufgebraucht ist. 'Spawning'-Anwendungen lassen Algorithmen auf externen Maschinen laufen, um die Hauptanwendung zu beschleunigen. Applikationen werden neu gestartet, sobald ein Absturz endeckt wird. Alle diese Verfahren können ohne menschliche Interaktion erfolgen.
Eine verteilte Rechenumgebung besitzt eine natürliche Unverlässlichkeit. Jede Applikation, die mit einer solchen Umgebung interagiert, muss auf die gestörten Komponenten reagieren können: schlechte Netzwerkverbindung, abstürzende Maschinen, fehlerhafte Software. Wir konstruieren eine verlässliche Serviceinfrastruktur, indem wir der Serviceumgebung eine 'Peer-to-Peer'-Topology aufprägen. Diese “Grid Peer Service” Infrastruktur beinhaltet Services wie Migration und Spawning, als auch Services zum Starten von Applikationen, zur Dateiübertragung und Auswahl von Rechenressourcen. Sie benutzt existierende Gridtechnologie wo immer möglich, um ihre Aufgabe durchzuführen. Ein Applikations-Information- Server arbeitet als generische Registratur für alle Teilnehmer in der Serviceumgebung.
Die Serviceumgebung, die wir entwickelt haben, erlaubt es Applikationen z.B. eine Relokationsanfrage an einen Migrationsserver zu stellen. Der Server sucht einen neuen Computer, basierend auf den übermittelten Ressourcen-Anforderungen. Er transferiert den Statusfile des Applikation zu der neuen Maschine und startet die Applikation neu. Obwohl das umgebende Ressourcensubstrat nicht kontinuierlich ist, können wir kontinuierliche Berechnungen auf Grids ausführen, indem wir die Applikation migrieren. Wir zeigen mit realistischen Beispielen, wie sich z.B. ein traditionelles Genom-Analyse-Programm leicht modifizieren lässt, um selbstbestimmte Migrationen in dieser Serviceumgebung durchzuführen.
In recent years, there has been a dramatic increase in available compute capacities. However, these “Grid resources” are rarely accessible in a continuous stream, but rather appear scattered across various machine types, platforms and operating systems, which are coupled by networks of fluctuating bandwidth. It becomes increasingly difficult for scientists to exploit available resources for their applications. We believe that intelligent, self-governing applications should be able to select resources in a dynamic and heterogeneous environment: Migrating applications determine a resource when old capacities are used up. Spawning simulations launch algorithms on external machines to speed up the main execution. Applications are restarted as soon as a failure is detected. All these actions can be taken without human interaction.

A distributed compute environment possesses an intrinsic unreliability. Any application that interacts with such an environment must be able to cope with its failing components: deteriorating networks, crashing machines, failing software. We construct a reliable service infrastructure by endowing a service environment with a peer-to-peer topology. This “Grid Peer Services” infrastructure accommodates high-level services like migration and spawning, as well as fundamental services for application launching, file transfer and resource selection. It utilizes existing Grid technology wherever possible to accomplish its tasks. An Application Information Server acts as a generic information registry to all participants in a service environment.

The service environment that we developed, allows applications e.g. to send a relocation requests to a migration server. The server selects a new computer based on the transmitted resource requirements. It transfers the application's checkpoint and binary to the new host and resumes the simulation. Although the Grid's underlying resource substrate is not continuous, we achieve persistent computations on Grids by relocating the application. We show with our real-world examples that a traditional genome analysis program can be easily modified to perform self-determined migrations in this service environment.

The increasingly popular cloud-computing paradigm provides on-demand access to computing and storage with the appearance of unlimited resources. Users are given access to a variety of data and software utilities to manage their work. Users rent virtual resources and pay for only what they use. In spite of the many benefits that cloud computing promises, the lack of dependability in shared virtualized infrastructures is a major obstacle for its wider adoption, especially for mission-critical applications. Virtualization and multi-tenancy increase system complexity and dynamicity. They introduce new sources of failure degrading the dependability of cloud computing systems. To assure cloud dependability, in my dissertation research, I develop autonomic failure identification and diagnosis techniques that are crucial for understanding emergent, cloud-wide phenomena and self-managing resource burdens for cloud availability and productivity enhancement. We study the runtime cloud performance data collected from a cloud test-bed and by using traces from production cloud systems. We define cloud signatures including those metrics that are most relevant to failure instances. We exploit profiled cloud performance data in both time and frequency domain to identify anomalous cloud behaviors and leverage cloud metric subspace analysis to automate the diagnosis of observed failures. We implement a prototype of the anomaly identification system and conduct the experiments in an on-campus cloud computing test-bed and by using the Google datacenter traces. Our experimental results show that our proposed anomaly detection mechanism can achieve 93% detection sensitivity while keeping the false positive rate as low as 6.1% and outperform other tested anomaly detection schemes. In addition, the anomaly detector adapts itself by recursively learning from these newly verified detection results to refine future detection.

Increasingly, software needs to dynamically adapt its behavior at run-time in response to changing conditions in the supporting computing infrastructure and in the surrounding physical environment. Adaptability is emerging as a necessary underlying capability, particularly for highly dynamic systems such as context-aware or ubiquitous systems. By automating tasks such as installation, adaptation, or healing, Autonomic Computing envisions computing environments that evolve without the need for human intervention. Even though there is a fair amount of work on architectures and their theoretical design, Autonomic Computing was criticised as being a \hype topic" because very little of it has been implemented fully. Furthermore, given that the autonomic system must change states at runtime and that some of those states may emerge and are much less deterministic, there is a great challenge to provide new guidelines, techniques and tools to help autonomic system development. This thesis shows that building up on the central ideas of Model Driven Development (Models as rst-order citizens) and Software Product Lines (Variability Management) can play a signi cant role as we move towards implementing the key self-management properties associated with autonomic computing. The presented approach encompass systems that are capable of modifying their own behavior with respect to changes in their operating environment, by using variability models as if they were the policies that drive the system's autonomic recon guration at runtime. Under a set of recon guration commands, the components that make up the architecture dynamically cooperate to change the con guration of the architecture to a new con guration. This work also provides the implementation of a Model-Based Recon guration Engine (MoRE) to blend the above ideas. Given a context event, MoRE queries the variability models to determine how the system should evolve, and then it provides the mechanisms for modifying the system.
Cetina Englada, C. (2010). Achieving Autonomic Computing through the Use of Variability Models at Run-time [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/7484
Palancia

Critical infrastructures are defined as the basic facilities, services and utilities needed to support the functioning of society. For over three-thousand years, civil engineers have built these infrastructures to ensure that needed services and products are available to make mankind more comfortable, secure and productive. Modern infrastructure control systems are vulnerable to disruption from natural disaster, accident, negligent operation and intentional cyber assaults from malicious agents. Many critical processes within our infrastructures are continuous (e.g., electric power, etc.) and cannot be interrupted without consequence to industry and the public. Failure to protect the critical infrastructure from cyber assaults will result in physical, economic and social impacts, extending from the local to the national level. Cyber weapons have shown that harm to infrastructures can occur before system operators have time to determine the source.We present the thesis that infrastructure control systems can employ autonomic computing technology to detect anomalies and mitigate process disruption. Specifically we focus on: 1) autonomic computing algorithms that can be integrated into control systems and networks to detect and respond to anomalies; 2) autonomic technology capable of detecting and blocking infrastructure controller commands, that if executed, would result in process disruption; 3) design and construction of a prototype Autonomic Critical Infrastructure Protection appliance (ACIP) for integration and testing of autonomic algorithms; and 4) the design and construction of a test bed capable of modeling critical infrastructures and related control systems and processes for the purpose of testing and demonstrating new autonomic technologies.We report on the development of a new, multi-dimension ontology that organizes cyber assault methodologies correlated with perpetrator motivation and goals. Using this ontology, we create a theoretical framework to identify the integration points for protective technology within infrastructure control systems. We have created a unique modeling and simulation test bed for critical infrastructure systems and processes, and a prototype autonomic computing appliance. Through this work, we have developed an expanded understanding of autonomic computing theory and its application to controls systems. We also, through experimentation, prove the thesis and establish a roadmap for future research.

This thesis addresses orchestration of servers amongst multiple co-hosted internet services such as e-Banking, e-Auction and e-Retail in hosting centres. The hosting paradigm entails levying fees for hosting third party internet services on servers at guaranteed levels of service performance. The orchestration of server ensemble in hosting centres is considered in the context of maximising the hosting centre's revenue over a lengthy time horizon. The inspiration for the server orchestration approach proposed in this thesis is drawn from nature and generally classed as swarm intelligence, specifically, sophisticated collective behaviour of social insects borne out of primitive interactions amongst members of the group to solve problems beyond the capability of individual members. Consequently, the approach is self-organising, adaptive and robust. A new scheme for server ensemble orchestration is introduced in this thesis. This scheme exploits the many similarities between server orchestration in an internet hosting centre and forager allocation in a honeybee (Apis mellifera) colony. The scheme mimics the way a honeybee colony distributes foragers amongst flower patches to maximise nectar influx, to orchestrate servers amongst hosted internet services to maximise revenue. The scheme is extended by further exploiting inherent feedback loops within the colony to introduce self-tuning and energy-aware server ensemble orchestration. In order to evaluate the new server ensemble orchestration scheme, a collection of server ensemble orchestration methods is developed, including a classical technique that relies on past history to make time varying orchestration decisions and two theoretical techniques that omnisciently make optimal time varying orchestration decisions or an optimal static orchestration decision based on complete knowledge of the future. The efficacy of the new biomimetic scheme is assessed in terms of adaptiveness and versatility. The performance study uses representative classes of internet traffic stream behaviour, service user's behaviour, demand intensity, multiple services co-hosting as well as differentiated hosting fee schedule. The biomimetic orchestration scheme is compared with the classical and the theoretical optimal orchestration techniques in terms of revenue stream. This study reveals that the new server ensemble orchestration approach is adaptive in a widely varying external internet environments. The study also highlights the versatility of the biomimetic approach over the classical technique. The self-tuning scheme improves on the original performance. The energy-aware scheme is able to conserve significant energy with minimal revenue performance degradation. The simulation results also indicate that the new scheme is competitive or better than classical and static methods.

Le Cloud Computing est un nouveau paradigme qui fournit des ressources informatiques sous forme de services à la demande via internet fondé sur le modèle de facturation pay-per-use. Il est de plus en plus utilisé pour le déploiement et l’exécution des processus métier en général et des processus métier à base de services (SBPs) en particulier. Les environnements cloud sont généralement très dynamiques. À cet effet, il devient indispensable de s’appuyer sur des agents intelligents appelés gestionnaires autonomiques (AMs), qui permettent de rendre les SBPs capables de se gérer de façon autonome afin de faire face aux changements dynamiques induits parle cloud. Cependant, les solutions existantes sont limitées à l’utilisation soit d’un AM centralisé, soit d’un AM par service pour gérer un SBP. Il est évident que la deuxième solution représente un gaspillage d’AMs et peut conduire à la prise de décisions de gestion contradictoires, tandis que la première solution peut conduire à des goulots d’étranglement au niveau de la gestion du SBP. Par conséquent, il est essentiel de trouver le nombre optimal d’AMs qui seront utilisés pour gérer un SBP afin de minimiser leur nombre tout en évitant les goulots d’étranglement. De plus, en raison de l’hétérogénéité des ressources cloud et de la diversité de la qualité de service (QoS) requise par les SBPs, l’allocation des ressources cloud pour ces AMs peut entraîner des coûts de calcul et de communication élevés et/ou une QoS inférieure à celle exigée. Pour cela, il est également essentiel de trouver l’allocation optimale des ressources cloud pour les AMs qui seront utilisés pour gérer un SBP afin de minimiser les coûts tout en maintenant les exigences de QoS. Dans ce travail, nous proposons un modèle d’optimisation déterministe pour chacun de ces deux problèmes. En outre, en raison du temps nécessaire pour résoudre ces problèmes qui croît de manière exponentielle avec la taille du problème, nous proposons des algorithmes quasi-optimaux qui permettent d’obtenir de bonnes solutions dans un temps raisonnable
Cloud Computing is a new paradigm that provides computing resources as a service over the internet in a pay-per-use model. It is increasingly used for hosting and executing business processes in general and service-based business processes (SBPs) in particular. Cloud environments are usually highly dynamic. Hence, executing these SBPs requires autonomic management to cope with the changes of cloud environments implies the usage of a number of controlling devices, referred to as Autonomic Managers (AMs). However, existing solutions are limited to use either a centralized AM or an AM per service for managing a whole SBP. It is obvious that the latter solution is resource consuming and may lead to conflicting management decisions, while the former one may lead to management bottlenecks. An important problem in this context, deals with finding the optimal number of AMs for the management of an SBP, minimizing costs in terms of number of AMs while at the same time avoiding management bottlenecks and ensuring good management performance. Moreover, due to the heterogeneity of cloud resources and the diversity of the required quality of service (QoS) of SBPs, the allocation of cloud resources to these AMs may result in high computing costs and an increase in the communication overheads and/or lower QoS. It is also crucial to find an optimal allocation of cloud resources to the AMs, minimizing costs while at the same time maintaining the QoS requirements. To address these challenges, in this work, we propose a deterministic optimization model for each problem. Furthermore, due to the amount of time needed to solve these problems that grows exponentially with the size of the problem, we propose near-optimal algorithms that provide good solutions in reasonable time

Tese (doutorado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2014.
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A computação em nuvem tem sido considerada como uma opção para executar aplicações de alto desempenho. Entretanto, enquanto as plataformas de alto desempenho tradicionais como grid e supercomputadores oferecem um ambiente estável quanto à falha, desempenho e número de recursos, a computação em nuvem oferece recursos sob demanda, geralmente com desempenho imprevisível à baixo custo financeiro. Além disso, em ambiente de nuvem, as falhas fazem parte da sua normal operação. No entanto, as nuvens podem ser combinadas, criando uma federação, para superar os limites de uma nuvem muitas vezes com um baixo custo para os usuários. A federação de nuvens pode ajudar tanto os provedores quanto os usuários das nuvens a atingirem diferentes objetivos tais como: reduzir o tempo de execução de uma aplicação, reduzir o custo financeiro, aumentar a disponibilidade do ambiente, reduzir o consumo de energia, entre outros. Por isso, a federação de nuvens pode ser uma solução elegante para evitar o sub-provisionamento de recursos ajudando os provedores a reduzirem os custos operacionais e a reduzir o número de recursos ativos, que outrora ficariam ociosos consumindo energia, por exemplo. No entanto, a federação de nuvens aumenta as opções de recursos disponíveis para os usuários, requerendo, em muito dos casos, conhecimento em administração de sistemas ou em computação em nuvem, bem como um tempo considerável para aprender sobre as opções disponíveis. Neste contexto, surgem algumas questões, tais como: (a) qual dentre os recursos disponíveis é apropriado para uma determinada aplicação? (b) como os usuários podem executar suas aplicações na nuvem e obter um desempenho e um custo financeiro aceitável, sem ter que modificá-las para atender as restrições do ambiente de nuvem? (c) como os usuários não especialistas em nuvem podem maximizar o uso da nuvem, sem ficar dependente de um provedor? (d) como os provedores podem utilizar a federação para reduzir o consumo de energia dos datacenters e ao mesmo tempo atender os acordos de níveis de serviços? A partir destas questões, este trabalho apresenta uma solução para consolidação de aplicações em nuvem federalizadas considerando os acordos de serviços. Nossa solução utiliza um sistema multi-agente para negociar a migração das máquinas virtuais entres as nuvens. Simulações mostram que nossa abordagem pode reduzir em até 46% o consumo de energia e atender os requisitos de qualidade. Nós também desenvolvemos e avaliamos uma solução para executar uma aplicação de bioinformática em nuvens federalizadas, a custo zero. Nesse caso, utilizando a federação, conseguimos diminuir o tempo de execução da aplicação em 22,55%, considerando o seu tempo de execução na melhor nuvem. Além disso, este trabalho apresenta uma arquitetura chamada Excalibur, que possibilita escalar a execução de aplicações comuns em nuvem. Excalibur conseguiu escalar automaticamente a execução de um conjunto de aplicações de bioinformática em até 11 máquinas virtuais, reduzindo o tempo de execução em 63% e o custo financeiro em 84% quando comparado com uma configuração definida pelos usuários. Por fim, este trabalho apresenta um método baseado em linha de produto de software para lidar com as variabilidades dos serviços oferecidos por nuvens de infraestrutura (IaaS), e um sistema que utiliza deste processo para configurar o ambiente e para lidar com falhas de forma automática. O nosso método utiliza modelo de feature estendido com atributos para descrever os recursos e para selecioná-los com base nos objetivos dos usuários. Experimentos realizados com dois provedores diferentes mostraram que utilizando o nosso processo, os usuários podem executar as suas aplicações em um ambiente de nuvem federalizada, sem conhecer as variabilidades e limitações das nuvens. _______________________________________________________________________________________ ABSTRACT
Cloud computing has been seen as an option to execute high performance computing (HPC) applications. While traditional HPC platforms such as grid and supercomputers offer a stable environment in terms of failures, performance, and number of resources, cloud computing offers on-demand resources generally with unpredictable performance at low financial cost. Furthermore, in cloud environment, failures are part of its normal operation. To overcome the limits of a single cloud, clouds can be combined, forming a cloud federation often with minimal additional costs for the users. A cloud federation can help both cloud providers and cloud users to achieve their goals such as to reduce the execution time, to achieve minimum cost, to increase availability, to reduce power consumption, among others. Hence, cloud federation can be an elegant solution to avoid over provisioning, thus reducing the operational costs in an average load situation, and removing resources that would otherwise remain idle and wasting power consumption, for instance. However, cloud federation increases the range of resources available for the users. As a result, cloud or system administration skills may be demanded from the users, as well as a considerable time to learn about the available options. In this context, some questions arise such as: (a) which cloud resource is appropriate for a given application? (b) how can the users execute their HPC applications with acceptable performance and financial costs, without needing to re-engineer the applications to fit clouds’ constraints? (c) how can non-cloud specialists maximize the features of the clouds, without being tied to a cloud provider? and (d) how can the cloud providers use the federation to reduce power consumption of the clouds, while still being able to give service-level agreement (SLA) guarantees to the users? Motivated by these questions, this thesis presents a SLA-aware application consolidation solution for cloud federation. Using a multi-agent system (MAS) to negotiate virtual machine (VM) migrations between the clouds, simulation results show that our approach could reduce up to 46% of the power consumption, while trying to meet performance requirements. Using the federation, we developed and evaluated an approach to execute a huge bioinformatics application at zero-cost. Moreover, we could decrease the execution time in 22.55% over the best single cloud execution. In addition, this thesis presents a cloud architecture called Excalibur to auto-scale cloud-unaware application. Executing a genomics workflow, Excalibur could seamlessly scale the applications up to 11 virtual machines, reducing the execution time by 63% and the cost by 84% when compared to a user’s configuration. Finally, this thesis presents a software product line engineering (SPLE) method to handle the commonality and variability of infrastructure-as-a-service (IaaS) clouds, and an autonomic multi-cloud architecture that uses this method to configure and to deal with failures autonomously. The SPLE method uses extended feature model (EFM) with attributes to describe the resources and to select them based on the users’ objectives. Experiments realized with two different cloud providers show that using the proposed method, the users could execute their application on a federated cloud environment, without needing to know the variability and constraints of the clouds. _______________________________________________________________________________________ RÉSUMÉ
Le cloud computing a été considéré comme une option pour exécuter des applications de calcul haute performance (HPC). Bien que les plateformes traditionnelles de calcul haute performance telles que les grilles et les supercalculateurs offrent un environnement stable du point de vue des défaillances, des performances, et de la taille des ressources, le cloud computing offre des ressources à la demande, généralement avec des performances imprévisibles mais à des coûts financiers abordables. En outre, dans un environnement de cloud, les défaillances sont perçues comme étant ordinaires. Pour surmonter les limites d’un cloud individuel, plusieurs clouds peuvent être combinés pour former une fédération de clouds, souvent avec des coûts supplémentaires légers pour les utilisateurs. Une fédération de clouds peut aider autant les fournisseurs que les utilisateurs à atteindre leurs objectifs tels la réduction du temps d’exécution, la minimisation des coûts, l’augmentation de la disponibilité, la réduction de la consummation d’énergie, pour ne citer que ceux-là. Ainsi, la fédération de clouds peut être une solution élégante pour éviter le sur-approvisionnement, réduisant ainsi les coûts d’exploitation en situation de charge moyenne, et en supprimant des ressources qui, autrement, resteraient inutilisées et gaspilleraient ainsi de énergie. Cependant, la fédération de clouds élargit la gamme des ressources disponibles. En conséquence, pour les utilisateurs, des compétences en cloud computing ou en administration système sont nécessaires, ainsi qu’un temps d’apprentissage considérable pour maîtrises les options disponibles. Dans ce contexte, certaines questions se posent : (a) Quelle ressource du cloud est appropriée pour une application donnée ? (b) Comment les utilisateurs peuvent-ils exécuter leurs applications HPC avec un rendement acceptable et des coûts financiers abordables, sans avoir à reconfigurer les applications pour répondre aux norms et contraintes du cloud ? (c) Comment les non-spécialistes du cloud peuvent-ils maximiser l’usage des caractéristiques du cloud, sans être liés au fournisseur du cloud ? et (d) Comment les fournisseurs de cloud peuvent-ils exploiter la fédération pour réduire la consommation électrique, tout en étant en mesure de fournir un service garantissant les normes de qualité préétablies ? À partir de ces questions, la presente thèse propose une solution de consolidation d’applications pour la fédération de clouds qui garantit le respect des normes de qualité de service. On utilise un système multi-agents (SMA) pour négocier la migration des machines virtuelles entre les clouds. Les résultats de simulations montrent que notre approche pourrait réduire jusqu’à 46% la consommation totale d’énergie, tout en respectant les exigencies de performance. En nous basant sur la fédération de clouds, nous avons développé et évalué une approche pour exécuter une énorme application de bioinformatique à coût zéro. En outre, nous avons pu réduire le temps d’exécution de 22,55% par rapport à la meilleure exécution dans un cloud individuel. Cette thèse présente aussi une architecture de cloud baptisée « Excalibur » qui permet l’adaptation automatique des applications standards pour le cloud. Dans l’exécution d’une chaîne de traitements de la génomique, Excalibur a pu parfaitement mettre à l’échelle les applications sur jusqu’à 11 machines virtuelles, ce qui a réduit le temps d’exécution de 63% et le coût de 84% par rapport à la configuration de l’utilisateur. Enfin, cette thèse présente un processus d’ingénierie des lignes de produits (PLE) pour gérer la variabilité de l’infrastructure à la demande du cloud, et une architecture multi-cloud autonome qui utilise ce processus pour configurer et faire face aux défaillances de manière indépendante. Le processus PLE utilise le modele étendu de fonction (EFM) avec des attributs pour décrire les ressources et les sélectionner en fonction dês objectifs de l’utilisateur. Les expériences réalisées avec deux fournisseurs de cloud différents montrent qu’em utilisant le modèle proposé, les utilisateurs peuvent exécuter leurs applications dans un environnement de clouds fédérés, sans avoir besoin de connaître les variabilités et contraintes du cloud.

Le cloud computing a été considéré comme une option pour exécuter des applications de calcul haute performance. Bien que les plateformes traditionnelles de calcul haute performance telles que les grilles et les supercalculateurs offrent un environnement stable du point de vue des défaillances, des performances, et de la taille des ressources, le cloud computing offre des ressources à la demande, généralement avec des performances imprévisibles mais à des coûts financiers abordables. Pour surmonter les limites d’un cloud individuel, plusieurs clouds peuvent être combinés pour former une fédération de clouds, souvent avec des coûts supplémentaires légers pour les utilisateurs. Une fédération de clouds peut aider autant les fournisseurs que les utilisateurs à atteindre leurs objectifs tels la réduction du temps d’exécution, la minimisation des coûts, l’augmentation de la disponibilité, la réduction de la consommation d’énergie, pour ne citer que ceux-Là. Ainsi, la fédération de clouds peut être une solution élégante pour éviter le sur-Approvisionnement, réduisant ainsi les coûts d’exploitation en situation de charge moyenne, et en supprimant des ressources qui, autrement, resteraient inutilisées et gaspilleraient ainsi de énergie. Cependant, la fédération de clouds élargit la gamme des ressources disponibles. En conséquence, pour les utilisateurs, des compétences en cloud computing ou en administration système sont nécessaires, ainsi qu’un temps d’apprentissage considérable pour maîtrises les options disponibles. Dans ce contexte, certaines questions se posent: (a) Quelle ressource du cloud est appropriée pour une application donnée? (b) Comment les utilisateurs peuvent-Ils exécuter leurs applications HPC avec un rendement acceptable et des coûts financiers abordables, sans avoir à reconfigurer les applications pour répondre aux normes et contraintes du cloud ? (c) Comment les non-Spécialistes du cloud peuvent-Ils maximiser l’usage des caractéristiques du cloud, sans être liés au fournisseur du cloud ? et (d) Comment les fournisseurs de cloud peuvent-Ils exploiter la fédération pour réduire la consommation électrique, tout en étant en mesure de fournir un service garantissant les normes de qualité préétablies ? À partir de ces questions, la présente thèse propose une solution de consolidation d’applications pour la fédération de clouds qui garantit le respect des normes de qualité de service. On utilise un système multi-Agents pour négocier la migration des machines virtuelles entre les clouds. En nous basant sur la fédération de clouds, nous avons développé et évalué une approche pour exécuter une énorme application de bioinformatique à coût zéro. En outre, nous avons pu réduire le temps d’exécution de 22,55% par rapport à la meilleure exécution dans un cloud individuel. Cette thèse présente aussi une architecture de cloud baptisée « Excalibur » qui permet l’adaptation automatique des applications standards pour le cloud. Dans l’exécution d’une chaîne de traitements de la génomique, Excalibur a pu parfaitement mettre à l’échelle les applications sur jusqu’à 11 machines virtuelles, ce qui a réduit le temps d’exécution de 63% et le coût de 84% par rapport à la configuration de l’utilisateur. Enfin, cette thèse présente un processus d’ingénierie des lignes de produits (PLE) pour gérer la variabilité de l’infrastructure à la demande du cloud, et une architecture multi-Cloud autonome qui utilise ce processus pour configurer et faire face aux défaillances de manière indépendante. Le processus PLE utilise le modèle étendu de fonction avec des attributs pour décrire les ressources et les sélectionner en fonction des objectifs de l’utilisateur. Les expériences réalisées avec deux fournisseurs de cloud différents montrent qu’en utilisant le modèle proposé, les utilisateurs peuvent exécuter leurs applications dans un environnement de clouds fédérés, sans avoir besoin de connaître les variabilités et contraintes du cloud
Cloud computing has been seen as an option to execute high performance computing (HPC) applications. While traditional HPC platforms such as grid and supercomputers offer a stable environment in terms of failures, performance, and number of resources, cloud computing offers on-Demand resources generally with unpredictable performance at low financial cost. Furthermore, in cloud environment, failures are part of its normal operation. To overcome the limits of a single cloud, clouds can be combined, forming a cloud federation often with minimal additional costs for the users. A cloud federation can help both cloud providers and cloud users to achieve their goals such as to reduce the execution time, to achieve minimum cost, to increase availability, to reduce power consumption, among others. Hence, cloud federation can be an elegant solution to avoid over provisioning, thus reducing the operational costs in an average load situation, and removing resources that would otherwise remain idle and wasting power consumption, for instance. However, cloud federation increases the range of resources available for the users. As a result, cloud or system administration skills may be demanded from the users, as well as a considerable time to learn about the available options. In this context, some questions arise such as: (a) which cloud resource is appropriate for a given application? (b) how can the users execute their HPC applications with acceptable performance and financial costs, without needing to re-Engineer the applications to fit clouds' constraints? (c) how can non-Cloud specialists maximize the features of the clouds, without being tied to a cloud provider? and (d) how can the cloud providers use the federation to reduce power consumption of the clouds, while still being able to give service-Level agreement (SLA) guarantees to the users? Motivated by these questions, this thesis presents a SLA-Aware application consolidation solution for cloud federation. Using a multi-Agent system (MAS) to negotiate virtual machine (VM) migrations between the clouds, simulation results show that our approach could reduce up to 46% of the power consumption, while trying to meet performance requirements. Using the federation, we developed and evaluated an approach to execute a huge bioinformatics application at zero-Cost. Moreover, we could decrease the execution time in 22.55% over the best single cloud execution. In addition, this thesis presents a cloud architecture called Excalibur to auto-Scale cloud-Unaware application. Executing a genomics workflow, Excalibur could seamlessly scale the applications up to 11 virtual machines, reducing the execution time by 63% and the cost by 84% when compared to a user's configuration. Finally, this thesis presents a product line engineering (PLE) process to handle the variabilities of infrastructure-As-A-Service (IaaS) clouds, and an autonomic multi-Cloud architecture that uses this process to configure and to deal with failures autonomously. The PLE process uses extended feature model (EFM) with attributes to describe the resources and to select them based on users' objectives. Experiments realized with two different cloud providers show that using the proposed model, the users could execute their application in a cloud federation environment, without needing to know the variabilities and constraints of the clouds

A2B Electronics AB is a company that develops and manufactures products and technology for digital cable television. A2B's new EXM-product family translates digital television channels from multiple source networks into a single destination network. Multiple EXM-units are connected in a system to provide a custom set of TV channels. To minimize the administrative effort, the units in a system should be able to interact and collaborate without manual intervention. The purpose of this thesis is to propose an underlying system that supports seamless interaction and collaboration between units.

The autonomic system concept has served as a foundation for the proposed solution. The requirements for the EXM-system proved to be similar to many properties of an autonomic system. The proposed solution was elaborated by answering five reseach questions. The answers describe how an autonomic system can be implemented with the prerequisites of the EXM-system. Solutions for service availability, configuration preservation, system state changes and automatic addressing and communication are provided.

The project has resulted in a proposal of a general autonomic system. The solution has also been implemented as prototype that runs both in a simulator and on the EXM-hardware. The simulator was also developed in the scope of this project as a side-effect of the limited access to EXM-hardware.

The proposed solution together with the prototype can hopefully serve as a base for projects with prerequisites similar to the project described in this thesis.

Large-scale virtualized data centers require cloud providers to implement scalable, autonomic, and energy-efficient cloud management systems. To address these challenges this thesis provides four main contributions. The first one proposes Snooze, a novel Infrastructure-as-a-Service (IaaS) cloud management system, which is designed to scale across many thousands of servers and virtual machines (VMs) while being easy to configure, highly available, and energy efficient. For scalability, Snooze performs distributed VM management based on a hierarchical architecture. To support ease of configuration and high availability Snooze implements self-configuring and self-healing features. Finally, for energy efficiency, Snooze integrates a holistic energy management approach via VM resource (i.e. CPU, memory, network) utilization monitoring, underload/overload detection and mitigation, VM consolidation (by implementing a modified version of the Sercon algorithm), and power management to transition idle servers into a power saving mode. A highly modular Snooze prototype was developed and extensively evaluated on the Grid'5000 testbed using realistic applications. Results show that: (i) distributed VM management does not impact submission time; (ii) fault tolerance mechanisms do not impact application performance and (iii) the system scales well with an increasing number of resources thus making it suitable for managing large-scale data centers. We also show that the system is able to dynamically scale the data center energy consumption with its utilization thus allowing it to conserve substantial power amounts with only limited impact on application performance. Snooze is an open-source software under the GPLv2 license. The second contribution is a novel VM placement algorithm based on the Ant Colony Optimization (ACO) meta-heuristic. ACO is interesting for VM placement due to its polynomial worst-case time complexity, close to optimal solutions and ease of parallelization. Simulation results show that while the scalability of the current algorithm implementation is limited to a smaller number of servers and VMs, the algorithm outperforms the evaluated First-Fit Decreasing greedy approach in terms of the number of required servers and computes close to optimal solutions. In order to enable scalable VM consolidation, this thesis makes two further contributions: (i) an ACO-based consolidation algorithm; (ii) a fully decentralized consolidation system based on an unstructured peer-to-peer network. The key idea is to apply consolidation only in small, randomly formed neighbourhoods of servers. We evaluated our approach by emulation on the Grid'5000 testbed using two state-of-the-art consolidation algorithms (i.e. Sercon and V-MAN) and our ACO-based consolidation algorithm. Results show our system to be scalable as well as to achieve a data center utilization close to the one obtained by executing a centralized consolidation algorithm.

L'émergence des nouveaux types d'environnements informatiques amplifie le besoin pour des systèmes logiciels d'être capables d'évoluer dynamiquement. Cependant, ces systèmes rendent très difficile le déploiement de logiciels en utilisant des processus humains. Il y a donc un besoin croissant d'outils d'automatisation qui permettent de déployer et reconfigurer des systèmes logiciels sans en interrompre l'exécution. Le processus de déploiement continu et automatisé permet de mettre à jour ou d'adapter un logiciel en exécution en fonction des changements contextuels et des exigences opérationnelles. Les solutions existantes ne permettent pas des déploiements reproductibles et tolérant aux pannes dans des environnements fluctuants, et donc requérant une adaptation continue. Cette thèse se concentre en particulier sur des solutions de déploiement continu pour les plates-formes d'exécution dynamiques, tels que celle utilisé dans les environnements ubiquitaires. Elle adopte une approche basée sur un processus transactionnel et idempotent pour coordonner les actions de déploiement. La thèse propose, également, un ensemble d'outils, y compris un gestionnaire de déploiement capable de mener des déploiements discret, mais également d'adapter les applications continuellement en fonction des changements contextuels. La mise en œuvre de ces outils, permet notamment aux développeurs et aux administrateurs de développer des déploiements d'applications grâce à un langage spécifique suivant les principes de l‘infrastructure-as-code. En utilisant l'implantation de Rondo, les propositions de cette thèse sont validées dans plusieurs projets industriels et académiques à la fois pour l'administration de plates-formes ubiquitaires ainsi que pour l'installation d'applications et leurs reconfigurations continues
Driven by the emergence of new computing environments, dynamically evolving software systems makes it impossible for developers to deploy software with human-centric processes. Instead, there is an increasing need for automation tools that continuously deploy software into execution, in order to push updates or adapt existing software regarding contextual and business changes. Existing solutions fall short on providing fault-tolerant, reproducible deployments that would scale on heterogeneous environments. This thesis focuses especially on enabling continuous deployment solutions for dynamic execution platforms, such as would be found in Pervasive Computing environments. It adopts an approach based on a transactional, idempotent process for coordinating deployment actions. The thesis proposes a set of deployment tools, including a deployment manager capable of conducting deployments and continuously adapting applications according to the changes in the current state of the target platform. The implementation of these tools, Rondo, also allows developers and administrators to code application deployments thanks to a deployment descriptor DSL. Using the implementation of Rondo, the propositions of this thesis are validated in several industrial and academic projects by provisioning frameworks as well as on installing application and continuous reconfigurations

Le Cloud Computing est un paradigme émergent dans les technologies de l'information. L'un de ses atouts majeurs étant la mise à disposition des ressources fondée sur le modèle pay-as-you-go. Les ressources Cloud se situent dans un environnement très dynamique. Cependant, chaque ressource provisionnée offre des services fonctionnels et peut ne pas offrir des services non fonctionnels tels que la supervision, la reconfiguration, la sécurité, etc. Dans un tel environnement dynamique, les services non fonctionnels ont une importance critique pour le maintien du niveau de service des ressources ainsi que le respect des contrats entre les fournisseurs et les consommateurs. Dans notre travail, nous nous intéressons à la supervision, la reconfiguration et la gestion autonomique des ressources Cloud. En particulier, nous mettons l'accent sur les applications à base de services. Ensuite, nous poussons plus loin notre travail pour traiter les ressources Cloud d'une manière générale. Par conséquent, cette thèse contient deux contributions majeures. Dans la première contribution, nous étendons le standard SCA (Service Component Architecture) afin de permettre l'ajout de besoins en supervision et reconfiguration à la description des composants. Dans ce contexte, nous proposons une liste de transformations qui permet d'ajouter automatiquement aux composants des facilités de supervision et de reconfiguration, et ce, même si ces facilités n'ont pas été prévues dans la conception des composants. Ceci facilite la tâche au développeur en lui permettant de se concentrer sur les services fonctionnels de ses composants. Pour être en conformité avec la scalabilité des environnements Cloud, nous utilisons une approche basée sur des micro-conteneurs pour le déploiement de composants. Dans la deuxième contribution, nous étendons le standard OCCI (Open Cloud Computing Interface) pour ajouter dynamiquement des facilités de supervision et de reconfiguration aux ressources Cloud, indépendamment de leurs niveaux de service. Cette extension implique la définition de nouvelles Ressources, Links et Mixins OCCI pour permettre d'ajouter dynamiquement des facilités de supervision et de reconfiguration à n'importe quelle ressource Cloud. Nous étendons par la suite nos deux contributions de supervision et reconfiguration afin d'ajouter des capacités de gestion autonomique aux applications SCA et ressources Cloud. Les solutions que nous proposons sont génériques, granulaires et basées sur les standards de facto (i.e., SCA et OCCI). Dans ce manuscrit de thèse, nous décrivons les détails de nos implémentations ainsi que les expérimentations que nous avons menées pour l'évaluation de nos propositions
Cloud Computing is an emerging paradigm in Information Technologies (IT). One of its major assets is the provisioning of resources based on pay-as-you-go model. Cloud resources are situated in a highly dynamic environment. However, each provisioned resource comes with functional properties and may not offer non functional properties like monitoring, reconfiguration, security, accountability, etc. In such dynamic environment, non functional properties have a critical importance to maintain the service level of resources and to make them respect the contracts between providers and consumers. In our work, we are interested in monitoring, reconfiguration and autonomic management of Cloud resources. Particularly, we put the focus on Service-based applications. Afterwards, we push further our work to treat Cloud resources. Consequently, this thesis contains two major contributions. On the first hand, we extend Service Component Architecture (SCA) in order to add monitoring and reconfiguration requirements description to components. In this context, we propose a list of transformations that dynamically adds monitoring and reconfiguration facilities to components even if they were designed without them. That alleviates the task of the developer and lets him focus just on the business of his components. To be in line with scalability of Cloud environments, we use a micro-container based approach for the deployment of components. On the second hand, we extend Open Cloud Computing Interface standards to dynamically add monitoring and reconfiguration facilities to Cloud resources while remaining agnostic to their level. This extension entails the definition of new Resources, Links and Mixins to dynamically add monitoring and reconfiguration facilities to resources. We extend the two contributions to couple monitoring and reconfiguration in order to add self management capabilities to SCA-based applications and Cloud resource. The solutions that we propose are generic, granular and are based on the de facto standards (i.e., SCA and OCCI). In this thesis manuscript, we give implementation details as well as experiments that we realized to evaluate our proposals

Cloud Computing is emerging as a new paradigm that aims at delivering computing as a utility. For the cloud computing paradigm to be fully adopted and effectively used it is critical that the security mechanisms are robust and resilient to malicious faults and attacks. Securing cloud is a challenging research problem because it suffers from current cybersecurity problems in computer networks and data centers and additional complexity introduced by virtualizations, multi-tenant occupancy, remote storage, and cloud management. It is widely accepted that we cannot build software and computing systems that are free from vulnerabilities and that cannot be penetrated or attacked. Furthermore, it is widely accepted that cyber resilient techniques are the most promising solutions to mitigate cyberattacks and change the game to advantage defender over attacker. Moving Target Defense (MTD) has been proposed as a mechanism to make it extremely challenging for an attacker to exploit existing vulnerabilities by varying different aspects of the execution environment. By continuously changing the environment (e.g. Programming language, Operating System, etc.) we can reduce the attack surface and consequently, the attackers will have very limited time to figure out current execution environment and vulnerabilities to be exploited. In this dissertation, we present a methodology to develop an Autonomic Resilient Cloud Management (ARCM) based on MTD and autonomic computing. The proposed research will utilize the following capabilities: Software Behavior Obfuscation (SBO), replication, diversity, and Autonomic Management (AM). SBO employs spatiotemporal behavior hiding or encryption and MTD to make software components change their implementation versions and resources randomly to avoid exploitations and penetrations. Diversity and random execution is achieved by using AM that will randomly "hot" shuffling multiple functionally-equivalent, behaviorally-different software versions at runtime (e.g., the software task can have multiple versions implemented in a different language and/or run on a different platform). The execution environment encryption will make it extremely difficult for an attack to disrupt normal operations of cloud. In this work, we evaluated the performance overhead and effectiveness of the proposed ARCM approach to secure and protect a wide range of cloud applications such as MapReduce and scientific and engineering applications.

Sistemas corporativos modernos cada vez mais dependentes da rede e a integração de serviços entorno do modelo TCP/IP elevam a exigência de Qualidade de Serviço da infraestrutura de TI. Neste cenário, o dinamismo das redes atuais em conjunto com os novos requisitos de QoS exigem que a infra-estrutura de TI seja mais autônoma e confiável. Para tratar esta questão, o modelo de Gerenciamento de Redes Baseado em Políticas, proposto pelo IETF, vem se consolidando como uma abordagem para controlar o comportamento da rede através do controle das configurações dos seus dispositivos. Porém, o foco deste modelo é o gerenciamento de políticas internas a um domínio administrativo. Esta característica faz com que o modelo possua algumas limitações, tais como a incapacidade de estabelecer qualquer tipo de coordenação entre diferentes PDPs e a impossibilidade de reagir a eventos externos. Visando agregar autonomia ao modelo de gerenciamento baseado em políticas, este trabalho propõe uma arquitetura em camadas que empregue os conceitos de Autonomic Computing relacionados a: i) adaptação dinâmica dos recursos gerenciados em resposta às mudanças no ambiente, ii) integração com sistemas de gerenciamento de outros domínios através do recebimento de notificações destes, iii) capacidade de planejar ações de gerenciamento e iv) promoção de ações de gerenciamento que envolvam mais de um domínio administrativo, estabelecendo uma espécie de coordenação entre PDPs. Para a implementação destes conceitos, a arquitetura prevê o uso de uma camada peerto- peer (P2P) sobre a plataforma de políticas. Desta forma, a partir de uma notificação recebida, a camada P2P planeja ações visando adaptar o comportamento da rede aos eventos ocorridos na infra-estrutura de TI. As ações planejadas traduzem-se em inclusões ou remoções de políticas da plataforma de políticas responsável por gerenciar a configuração dos dispositivos de rede. Para notificações que envolvam recursos de mais de um domínio administrativo, os peers de gerenciamento agem de forma coordenada para implantar as devidas ações em cada domínio. A arquitetura proposta foi projetada com foco em prover QoS em uma rede com suporte à DiffServ, embora acredite-se que a sua estrutura seja genérica o bastante para ser aplicada a outros contextos. Como estudo de caso, foi analisado o emprego da arquitetura em resposta a eventos gerados por uma grade computacional. Foi elaborado ainda um protótipo da arquitetura utilizando o Globus Toolkit 4 como fonte de eventos.
Modern corporative systems becoming more dependent of the network and the integration of services around the TCP/IP model increase the requirement of Quality of Service (QoS) of the IT infrastructure. In this scene, the dynamism of current networks together with the new requirements of QoS demands a more autonomous and reliable IT infrastructure. To address this issue, the model of Police Based Network Management, proposed by IETF, has been consolidated as an approach to control the behavior of the network through the control of the configurations of its devices. However, the focus of this model is the management of the policies internal to an administrative domain. This feature brings some limitations to the model, such as the incapacity to establish any kind of coordination between different PDPs and the impossibility to react to external events. Aiming at to add autonomy to the model of Policy Based Network Management, this work proposes a layered architecture based on the concepts of Autonomic Computing related to: i) the dynamic adaptation of the managed resources in response to changes in the environment, ii) integration with management systems of other domains through the reception of notifications of these systems, iii) ability of planning the management actions and iv) execution of multi-domain management actions, establishing a kind of coordination between PDPs. To implement these concepts, the architecture was designed with a peer-to-peer layer above the policy platform. Thus, from a received notification, the P2P layer plans actions aiming to adapt the network behavior in response to the events occurred in the IT infrastructure. The planned actions are, actually, inclusions or removals of policies in the policy platform responsible for the management of the network devices configuration. For notifications related with resources of more than one administrative domain, the management peers act in a coordinated way in order to establish the suitable actions in each domain. The proposed architecture was designed with focus in providing QoS in a network with support to DiffServ, although we believe that its structure is generic enough to be applied to other contexts. As case study, it was analyzed the use of the architecture in response to events generated by a computational grid. Additionally, a prototype of the architecture was build making use of Globus Toolkit 4 as an event source.

Sensor networks use a group of collaborating sensor nodes to collect information about real world phenomena. Sensor nodes use low-power short-range radio links to communicate with each other. Communication between sensor nodes shows significant variation over time and space. This can lead to unreliable and unpredictable network performance. These dynamic and lossy characteristics of wireless links pose major challenges for building reliable sensor networks and raise new issues that data delivery protocols must address. This thesis addresses the problems of designing protocols to overcome time-varying environmental conditions that lead to unpredictable network performance. The goal is to provide reliable data delivery in sensor networks and to minimise energy use. The major contributions of this thesis are: measuring the performance of wireless links in field trials on a time scale of weeks; systematic analysis of strengths and weaknesses of existing data delivery protocols; and the design, implementation and testing of a novel autonomic communication framework. We have measured link quality over time in experiments in unattended outdoor environments. Most previous work focused on spatial properties and experiments were not extensive, only lasting for a few hours. Besides common phenomena found in other work, such as the variation of network performance over time and the existence of asymmetric links, we find that links are independent over long time scales, and performance patterns of links are different. We also analyse the performance of data delivery protocols that use different techniques to improve reliability in sensor networks. Through systematic analysis of strengths and weaknesses of existing data delivery strategies, we find that networks using a single technique can only perform well for a limited range of link conditions. Different strategies are required in different operating conditions. Based on these experimental and theoretical studies, a novel autonomic communication framework (ACF) for wireless sensor networks is proposed. Nodes in this ACF are able to change their behaviour to adapt to time-varying environments so that optimal network performance can be achieved. Our framework provides a holistic solution for reliable data delivery to overcome time-varying wireless links. Our implementation and experimental evaluations demonstrate that this holistic framework is effective for reliable and energy-efficient data delivery in realistic sensor network settings.

A cyber-physical controller (CPC) uses computing to control a physical process. Example CPCs can be found in self-driving automobiles, unmanned aerial vehicles, and other autonomous systems. They are also used in large-scale industrial control systems (ICSs) manufacturing and utility infrastructure. CPC operations rely on embedded systems having real-time, high-assurance interactions with physical processes. However, recent attacks like Stuxnet have demonstrated that CPC malware is not restricted to networks and general-purpose computers, rather embedded components are targeted as well. General-purpose computing and network approaches to security are failing to protect embedded controllers, which can have the direct effect of process disturbance or destruction. Moreover, as embedded systems increasingly grow in capability and find application in CPCs, embedded leaf node security is gaining priority. This work develops a root-of-trust design architecture, which provides process resilience to cyber attacks on, or from, embedded controllers: the Trustworthy Autonomic Interface Guardian Architecture (TAIGA). We define five trust requirements for building a fine-grained trusted computing component. TAIGA satisfies all requirements and addresses all classes of CPC attacks using an approach distinguished by adding resilience to the embedded controller, rather than seeking to prevent attacks from ever reaching the controller. TAIGA provides an on-chip, digital, security version of classic mechanical interlocks. This last line of defense monitors all of the communications of a controller using configurable or external hardware that is inaccessible to the controller processor. The interface controller is synthesized from C code, formally analyzed, and permits run-time checked, authenticated updates to certain system parameters but not code. TAIGA overrides any controller actions that are inconsistent with system specifications, including prediction and preemption of latent malwares attempts to disrupt system stability and safety. This material is based upon work supported by the National Science Foundation under Grant Number CNS-1222656. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. We are grateful for donations from Xilinx, Inc. and support from the Georgia Tech Research Institute.
Ph. D.

Les charges réelles d'applications sont souvent dynamiques. Ainsi, le dimensionnement statique de ressources est voué soit au gaspillage, s'il est basé sur une estimation du pire scénario, soit à la dégradation de performance, s'il est basé sur la charge moyenne. Grâce au modèle du cloud computing, les ressources peuvent être allouées à la demande et le dimensionnement adapté à la variation de la charge. Cependant, après avoir exploré les travaux existants, nous avons trouvé que la plupart des outils d'élasticité sont trop génériques et ne parviennent pas à répondre aux besoins spécifiques d'applications particulières. Dans le cadre de ce travail, nous utilisons des boucles autonomiques et diverses techniques d'élasticité afin de rendre élastiques différents types d'applications, à savoir un service de consolidation, un intergiciel de messagerie et une plateforme de traitement de données en temps-réel. Ces solutions élastiques ont été réalisées à partir d'applications libres et leur évaluation montre qu'ils permettent d'économiser les ressources utilisées avec un surcoût minimal
Real world workloads are often dynamic. This makes the static scaling of resourcesfatally result in either the waste of resources, if it is based on the estimatedworst case scenario, or the degradation of performance if it is based on the averageworkload. Thanks to the cloud computing model, resources can be provisioned ondemand and scaling can be adapted to the variations of the workload thus achievingelasticity. However, after exploring the existing works, we find that most elasticityframeworks are too generic and fail to meet the specific needs of particularapplications. In this work, we use autonomic loops along with various elasticitytechniques in order to render different types of applications elastic, namelya consolidation service, message-oriented middleware and a stream processingplatform. These elastic solutions have been implemented based on open-sourceapplications and their evaluation shows that they enable resources’ economy withminimal overhead

With the rapid growth of servers and applications spurred by the Internet economy, power consumption in today's data centers is reaching unsustainable limits. This has led to an imminent financial, technical and environmental crisis that is impacting the society at large. Hence, it has become critically important that power consumption be efficiently managed in these computing power-houses of today. In this work, we revisit the issue of adaptive power and performance management of data center server platforms. Traditional data center servers are statically configured and always over-provisioned to be able to handle peak load. We transform these statically configured data center servers to clairvoyant entities that can sense changes in the workload and dynamically scale in capacity to adapt to the requirements of the workload. The over-provisioned server capacity is transitioned to low-power states and they remain in those states for as long as the performance remains within given acceptable thresholds. The platform power expenditure is minimized subject to performance constraints. This is formulated as a performance-per-watt optimization problem and solved using analytical power and performance models. Coarse-grained optimizations at the platform-level are refined by local optimizations at the devices-level namely - the processor & memory subsystems. Our adaptive interleaving technique for memory power management yielded about 48.8% (26.7 kJ) energy savings compared to traditional techniques measured at 4.5%. Our adaptive platform power and performance management technique demonstrated 56.25% energy savings for memory-intensive workload, 63.75% savings for processor-intensive workload and 47.5% savings for a mixed workload while maintaining platform performance within given acceptable thresholds.

As an implication of rapid growth in Internet-of-Things (IoT) data, current focus has shifted towards utilizing and analysing the data in order to make sense of the data. The aim of which is to make instantaneous, automated, and informed decisions that will drive the future IoT. This corresponds to extracting and applying knowledge from IoT data which brings both a substantial challenge and high value. Context plays an important role in reaping value from data, and is capable of countering the IoT data challenges. The management of heterogeneous contextualized data is infeasible and insufficient with the existing solutions which mandates new solutions. Research until now has mostly concentrated on providing cloud-based IoT solutions; among other issues, this promotes real-time and faster decision-making issues. In view of this, this dissertation undertakes a study of a context-based approach entitled Distributed intelligence-assisted Autonomic Context Information Management (DACIM), the purpose of which is to efficiently (i) utilize and (ii) analyse IoT data. To address the challenges and solutions with respect to enabling DACIM, the dissertation starts with proposing a logical-clustering approach for proper IoT data utilization. The environment that the number of Things immerse changes rapidly and becomes dynamic. To this end, self-organization has been supported by proposing self-* algorithms that resulted in 10 organized Things per second and high accuracy rate for Things joining. IoT contextualized data further requires scalable dissemination which has been addressed by a Publish/Subscribe model, and it has been shown that high publication rate and faster subscription matching are realisable. The dissertation ends with the proposal of a new approach which assists distribution of intelligence with regard to analysing context information to alleviate intelligence of things. The approach allows to bring few of the application of knowledge from the cloud to the edge; where edge based solution has been facilitated with intelligence that enables faster responses and reduced dependency on the rules by leveraging artificial intelligence techniques. To infer knowledge for different IoT applications closer to the Things, a multi-modal reasoner has been proposed which demonstrates faster response. The evaluations of the designed and developed DACIM gives promising results, which are distributed over seven publications; from this, it can be concluded that it is feasible to realize a distributed intelligence-assisted context-based approach that contribute towards autonomic context information management in the ever-expanding IoT realm.

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 7: Submitted.

This dissertation presents algorithms and mechanisms that enable self-managed, scalable and efficient deployment of large-scale scientific and engineering applications in a highly dynamic and unpredictable distributed environment. Typically these applications are composed of a large number of distributed components and it is important to meet the computational power and network bandwidth requirements of those components and their interactions. However satisfying these requirements in a large-scale, shared, heterogeneous, and highly dynamic distributed environment is a significant challenge. As systems and applications grow in scale and complexity, attaining the desired level of performance in this uncertain environment using current approaches based on global knowledge, centralized scheduling and manual reallocation becomes infeasible. This dissertation focuses on the modeling of the application and underlying architecture into a common abstraction and on the incorporations of autonomic features into those abstractions to achieve self-managed deployment. In particular, we developed techniques for automatically identifying application components and their estimated resource requirements within an application and used them in order to model the application into a graph abstraction. We also developed techniques that allow the distributed resources to self-organize in a utility-aware way while assuming minimal knowledge about the system. Finally, to achieve self-managed deployment of application components to the distributed nodes, we designed a scalable and adaptive scheduling algorithm which is governed by a utility function. The utility function, which combines several application and system level attributes, governs both the initial deployment of the application components and their reconfigurations despite the dynamism and uncertainty associated with the computing environment. The experimental results show that it is possible to achieve and maintain efficient deployment by applying the utility function derived in this paper based solely on locally available information and without costly global communication or synchronization. The self-management is therefore decentralized and provides better adaptability, scalability and robustness.

The increased complexity, heterogeneity and the dynamism of networked systems and applications make current configuration and management tools to be ineffective. A new paradigm to dynamically configure and manage large-scale complex and heterogeneous networked systems is critically needed. In this dissertation, we present a self configuration paradigm based on the principles of autonomic computing that can handle efficiently complexity, dynamism and uncertainty in configuring networked systems and their applications. Our approach is based on making any resource/application to operate as an Autonomic Component (that means, it can be self-configured, self-healed, self-optimized and self-protected) by using two software modules: Component Management Interface (CMI) to specify the configuration and operational policies associated with each component and Component Runtime Manager (CRM) that manages the component configurations and operations using the policies defined in CMI. We use several configuration metrics (adaptability, complexity, latency, scalability, overhead, and effectiveness) to evaluate the effectiveness of our self-configuration approach when compared to other configuration techniques. We have used our approach to dynamically configure four systems: Automatic IT system management, Dynamic security configuration of networked systems, Self-management of data backup and disaster recovery system and Automatic security patches download and installation on a large scale test bed. Our experimental results showed that by applying our self-configuration approach, the initial configuration time, the initial configuration complexity and the dynamic configuration complexity can be reduced significantly. For example, the configuration time for security patches download and installation on nine machines is reduced to 4399 seconds from 27193 seconds. Furthermore our system provides most adaptability (e.g., 100% for Snort rule set configuration) comparing to hard coded approach (e.g., 22% for Snort rule set configuration) and can improve the performance of managed system greatly. For example, in data backup and recovery system, our approach can reduce the total cost by 54.1% when network bandwidth decreases. In addition, our framework is scalable and imposes very small overhead (less than 1%) on the managed system.

Autonomic computing is a paradigm that aims at reducing administrative overhead by using autonomic managers to make applications self-managing. To better deal with large-scale dynamic environments; and to improve scalability, robustness, and performance; we advocate for distribution of management functions among several cooperative autonomic managers that coordinate their activities in order to achieve management objectives. Programming autonomic management in turn requires programming environment support and higher level abstractions to become feasible.

In this thesis we present an introductory part and a number of papers that summaries our work in the area of autonomic computing. We focus on enabling and achieving self-management for large scale and/or dynamic distributed applications. We start by presenting our platform, called Niche, for programming self-managing component-based distributed applications. Niche supports a network-transparent view of system architecture simplifying designing application self-* code.  Niche provides a concise and expressive API for self-* code. The implementation of the framework relies on scalability and robustness of structured overlay networks. We have also developed a distributed file storage service, called YASS, to illustrate and evaluate Niche.

After introducing Niche we proceed by presenting a methodology and design space for designing the management part of a distributed self-managing application in a distributed manner. We define design steps, that includes partitioning of management functions and orchestration of multiple autonomic managers. We illustrate the proposed design methodology by applying it to the design and development of an improved version of our distributed storage service YASS as a case study.

We continue by presenting a generic policy-based management framework which has been integrated into Niche. Policies are sets of rules that govern the system behaviors and reflect the business goals or system management objectives. The policy based management is introduced to simplify the management and reduce the overhead, by setting up policies to govern system behaviors. A prototype of the framework is presented and two generic policy languages (policy engines and corresponding APIs), namely SPL and XACML, are evaluated using our self-managing file storage application YASS as a case study.

Finally, we present a generic approach to achieve robust services that is based on finite state machine replication with dynamic reconfiguration of replica sets. We contribute a decentralized algorithm that maintains the set of resource hosting service replicas in the presence of churn. We use this approach to implement robust management elements as robust services that can operate despite of churn.

 

QC 20100520

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

Le Cloud Computing est un nouveau paradigme qui fournit des ressources informatiques sous forme de services à la demande via internet fondé sur le modèle de facturation pay-per-use. Il est de plus en plus utilisé pour le déploiement et l’exécution des processus métier en général et des processus métier à base de services (SBPs) en particulier. Les environnements cloud sont généralement très dynamiques. À cet effet, il devient indispensable de s’appuyer sur des agents intelligents appelés gestionnaires autonomiques (AMs), qui permettent de rendre les SBPs capables de se gérer de façon autonome afin de faire face aux changements dynamiques induits parle cloud. Cependant, les solutions existantes sont limitées à l’utilisation soit d’un AM centralisé, soit d’un AM par service pour gérer un SBP. Il est évident que la deuxième solution représente un gaspillage d’AMs et peut conduire à la prise de décisions de gestion contradictoires, tandis que la première solution peut conduire à des goulots d’étranglement au niveau de la gestion du SBP. Par conséquent, il est essentiel de trouver le nombre optimal d’AMs qui seront utilisés pour gérer un SBP afin de minimiser leur nombre tout en évitant les goulots d’étranglement. De plus, en raison de l’hétérogénéité des ressources cloud et de la diversité de la qualité de service (QoS) requise par les SBPs, l’allocation des ressources cloud pour ces AMs peut entraîner des coûts de calcul et de communication élevés et/ou une QoS inférieure à celle exigée. Pour cela, il est également essentiel de trouver l’allocation optimale des ressources cloud pour les AMs qui seront utilisés pour gérer un SBP afin de minimiser les coûts tout en maintenant les exigences de QoS. Dans ce travail, nous proposons un modèle d’optimisation déterministe pour chacun de ces deux problèmes. En outre, en raison du temps nécessaire pour résoudre ces problèmes qui croît de manière exponentielle avec la taille du problème, nous proposons des algorithmes quasi-optimaux qui permettent d’obtenir de bonnes solutions dans un temps raisonnable
Cloud Computing is a new paradigm that provides computing resources as a service over the internet in a pay-per-use model. It is increasingly used for hosting and executing business processes in general and service-based business processes (SBPs) in particular. Cloud environments are usually highly dynamic. Hence, executing these SBPs requires autonomic management to cope with the changes of cloud environments implies the usage of a number of controlling devices, referred to as Autonomic Managers (AMs). However, existing solutions are limited to use either a centralized AM or an AM per service for managing a whole SBP. It is obvious that the latter solution is resource consuming and may lead to conflicting management decisions, while the former one may lead to management bottlenecks. An important problem in this context, deals with finding the optimal number of AMs for the management of an SBP, minimizing costs in terms of number of AMs while at the same time avoiding management bottlenecks and ensuring good management performance. Moreover, due to the heterogeneity of cloud resources and the diversity of the required quality of service (QoS) of SBPs, the allocation of cloud resources to these AMs may result in high computing costs and an increase in the communication overheads and/or lower QoS. It is also crucial to find an optimal allocation of cloud resources to the AMs, minimizing costs while at the same time maintaining the QoS requirements. To address these challenges, in this work, we propose a deterministic optimization model for each problem. Furthermore, due to the amount of time needed to solve these problems that grows exponentially with the size of the problem, we propose near-optimal algorithms that provide good solutions in reasonable time