GoCity is designed to provide city visitors with up-to-date and context-aware information while they are exploring a city using Android mobile phones. This thesis not only introduces the design and analysis of GoCity, but also discusses four problems in leveraging three concepts—context-awareness, self-adaptation, and usability—in current mobile application design. First, few contexts other than location and time have been used in actual mobile applications. Second, there is no clear classification of context information for mobile application design. Third, mobile application designers lack systematic mechanisms to address sensing and monitoring requirements under changing context situations. This is crucial for effective self-adaptation. Fourth, most mobile applications have low usability due to poor user interface (UI) design. The model proposed in this thesis addresses these issues by (i) supporting diverse context dimensions, (ii) monitoring context changes continuously and tailoring the application behavior according to these changes, and (iii) improving UI design using selected usability methods. In addition, this thesis proposes two classifications of context information for mobile applications: source-based classification—personal context, mobile device context, and environmental context; and property-based classification—static context and dynamic context. The combination of these two classifications helps determine the observed context and its polling rate—the rate at which the context is collected—effectively. A distinctive feature of GoCity is that it supports two interaction modes—static mode and dynamic mode. In static mode, the application generates results only after the user sends the request to it. In other words, it does not actively generate results for users. In contrast, in the dynamic mode, the application continuously updates results even if the user does not send any request to it. The notion of an autonomic element (AE) is used for the dynamic mode to make GoCity self-adaptive. The polling rates on different contexts are also handled differently in the dynamic mode because of the differences among context properties. In addition, GoCity is composed of, but not limited to, four sub-applications. Each sub-application employs a variety of context information and can be implemented as an independent mobile application. Regarding usability, GoCity focuses on providing a simple and clear user interface as well as supporting user expectations for personalization. An experiment which involves a person visiting the city of Victoria was conducted to evaluate GoCity. In this evaluation, three determining factors of usability were employed to qualitatively and quantitatively assess GoCity. In addition, the static mode and dynamic mode were evaluated separately. / Graduate

Software-Intensive Cyber-Physical Systems (siCPS) are modular, open-ended, networked, large-scale embedded Information and Communication Technology (ICT) systems that are increasingly depending on software. They need to be both dependable and flexible to adapt to changes in their dynamic environments. This combination poses challenges in their design and development, as traditional model-driven design and development techniques cannot account for both dependability and self-adaptivity. The thesis proposes (1) a new, model-based design process for siCPS, which comprises both appropriate methods and models and deals with dependability and self-adaptivity, and (2) a mapping of the design models to implementation-level abstractions, which allows for model-driven development and early experimentations in siCPS. Specifically, the thesis delivers (1) by introducing and elaborating on the Invariant Refinement Method (IRM), and its extension for self-adaptivity, for the design of siCPS based on the ensemble paradigm. IRM was integrated into the ensemble development life cycle, a methodology for the development of autonomic ensemble-based systems. Contributing to (2), the thesis provides a mapping of the IRM concepts to the concepts of the DEECo components model. The mapping is supported by prototype...

Les principales caractéristiques des clouds d'infrastructure (laaS), comme l'élasticité instantanée et la mise à disposition automatique de ressources virtuelles, rendent ces clouds très dynamiques. Cette nature dynamique se traduit par de fréquents changements aux différents niveaux de l'infrastructure virtuelle. Étant données la criticité et parfois la confidentialité des informations traitées dans les infrastructures virtuelles des clients, la supervision de sécurité est une préoccupation importante pour les clients comme pour le fournisseur de cloud. Malheureusement, les changements dynamiques altèrent la capacité du système de supervision de sécurité à détecter avec succès les attaques ciblant les infrastructures virtuelles. Dans cette thèse, nous avons conçu un système de supervision de sécurité auto-adaptatif pour les clouds laaS. Ce système est conçu pour adapter ses composants en fonction des différents changements pouvant se produire dans une infrastructure de cloud. Notre système est instancié sous deux formes ciblant des équipements de sécurité différents : SAIDS, un système de détection d'intrusion réseau qui passe à l'échelle, et AL-SAFE, un firewall applicatif fondé sur l'introspection. Nous avons évalué notre prototype sous l'angle de la performance, du coût, et de la sécurité pour les clients comme pour le fournisseur. Nos résultats montrent que notre prototype impose un coût additionnel tolérable tout en fournissant une bonne qualité de détection. / Rapid elasticity and automatic provisioning of virtual resources are some of the main characteristics of laaS clouds. The dynamic nature of laaS clouds is translated to frequent changes that refer to different levels of the virtual infrastructure. Due to the critical and sometimes private information hosted in tenant virtual infrastructures, security monitoring is of great concern for both tenants and the provider. Unfortunately, the dynamic changes affect the ability of a security monitoring framework to successfully detect attacks that target cloud-hosted virtual infrastructures. In this thesis we have designed a self-adaptable security monitoring framework for laaS cloud environments that is designed to adapt its components based on different changes that occur in a virtual infrastructure. Our framework has two instantiations focused on different security devices: SAIDS, a scalable network intrusion detection system, and AL-SAFE, an introspection-based application-level firewall. We have evaluated our prototype focusing on performance, cost and security for both tenants and the provider. Our results demonstrate that our prototype imposes a tolerable overhead while providing accurate detection results.

Today’s software landscape features a high degree of complexity, frequent changes in requirements and stakeholder goals, and uncertainty. Uncertainty and high complexity imply a threat landscape where cybersecurity attacks are a common occurrence, while their consequences are often severe. Self-adaptive systems have been proposed to mitigate the complexity and frequent degree of change by adapting at run-time to deal with situations not known at design time. They, however, are not immune to attacks, as they themselves suffer from high degrees of complexity and uncertainty. Therefore, systems that can dynamically defend themselves from adversaries are required. Such systems are called self-protecting systems and aim to identify, analyse and mitigate threats autonomously. This thesis contributes two approaches towards the goal of providing systems with self-protection capabilities. The first approach aims to enhance the security of architecture-based selfadaptive systems and equip them with (proactive) self-protection capabilities that reduce the exposed attack surface. We target systems where information about the system components and its adaptation decisions is available, and control over its adaptation is also possible. We formally model the security of the system and provide two methods to analyze its security that help us rank adaptations in terms of their security level: a method based on quantitative risk assessment and a method based on probabilistic verification. The results indicate an improvement to the system security when either of our solutions is employed. However, only the second method can provide self-protecting capabilities. We have identified a direct relationship between security and performance overhead, i.e., higher security guarantees impose analogously higher performance overhead. The second approach targets open decentralized systems where we have limited information about and control over the system entities. Therefore, we attempt to employ decentralized information flow control mechanisms to enforce security by controlling interactions among the system elements. We extend a classical decentralized information flow control model by incorporating trust and adding adaptation capabilities that allow the system to identify security threats and self-organize to maximize the average trust between the system entities. We arrange entities of the system in trust hierarchies that enforce security policies among their elements and can mitigate security issues raised by the openness and uncertainty in the context and environment, without the need for a trusted central controller. The experiment results show that a reasonable level of trust can be achieved and at the same time confidentiality and integrity can be enforced with a low impact on the throughput and latency of messages exchanged in the system.

No description available.

The presence of computer technologies in our daily life is growing by leaps and bounds. One of the recent trends is the use of mobile technologies and cloud services for supporting everyday tasks and the sharing of information between users. The field of education is not absent from these developments and many organizations are adopting Information and Communication Technologies (ICT) in various ways for supporting teaching and learning. The field of Mobile Learning (M-Learning) offers new opportunities for carrying out collaborative educational activities in a variety of settings and situations. The use of mobile technologies for enhancing collaboration provides new opportunities but at the same time new challenges emerge. One of those challenges is discussed in this thesis and it con- cerns with uncertainties related to the dynamic aspects that characterized outdoor M-Learning activities. The existence of these uncertainties force software developers to make assumptions in their developments. However, these uncertainties are the cause of risks that may affect the required outcomes for M-Learning activities. Mitigations mechanisms can be developed and included to reduce the risks’ impact during the different phases of development. However, uncertainties which are present at runtime require adaptation mechanisms to mitigate the resulting risks. This thesis analyzes the current state of the art in self-adaptation in Technology-Enhanced Learning (TEL) and M-Learning. The results of an extensive literature survey in the field and the outcomes of the Geometry Mobile (GEM) research project are reported. A list of uncertainties in collaborative M-Learning activities and the associated risks that threaten the critical QoS outcomes for collaboration are identified and discussed. A detailed elaboration addressing mitigation mechanisms to cope with these problems is elaborated and presented. The results of these efforts provide valuable insights and the basis towards the design of a multi-agent self-adaptive architecture for multiple concerns that is illustrated with a prototype implementation. The proposed conceptual architecture is an initial cornerstone towards the creation of a decentralized distributed self-adaptive system for multiple concerns to guarantee collaboration in M-Learning.

Submitted by PPG Ci?ncia da Computa??o (ppgcc@pucrs.br) on 2018-04-03T14:37:48Z No. of bitstreams: 1 MARCELO_RUARO_TES.pdf: 4683751 bytes, checksum: 6eb242e44efbbffa6fa556ea81cdeace (MD5) / Approved for entry into archive by Tatiana Lopes (tatiana.lopes@pucrs.br) on 2018-04-13T17:30:40Z (GMT) No. of bitstreams: 1 MARCELO_RUARO_TES.pdf: 4683751 bytes, checksum: 6eb242e44efbbffa6fa556ea81cdeace (MD5) / Made available in DSpace on 2018-04-13T17:37:13Z (GMT). No. of bitstreams: 1 MARCELO_RUARO_TES.pdf: 4683751 bytes, checksum: 6eb242e44efbbffa6fa556ea81cdeace (MD5) Previous issue date: 2018-03-16 / Sistemas multi-n?cleos intra-chip s?o o estado-da-arte em termos de poder computacional, alcan?ando de d?zias a milhares de elementos de processamentos (PE) em um ?nico circuito integrado. Sistemas multi-n?cleos de prop?sito geral assumem uma admiss?o din?mica de aplica??es, onde o conjunto de aplica??es n?o ? conhecido em tempo de projeto e as aplica??es podem iniciar sua execu??o a qualquer momento. Algumas aplica??es podem ter requisitos de tempo real, requisitando n?veis de qualidade de servi?o (QoS) do sistema. Devido ao alto grau de imprevisibilidade do uso dos recursos e o grande n?mero de componentes para se gerenciar, propriedades autoadaptativas tornam-se fundamentais para dar suporte a QoS em tempo de execu??o. A literatura fornece diversas propostas de QoS autoadaptativo, focado em recursos de comunica??o (ex., redes intra-chip), ou computa??o (ex., CPU). Contudo, para fornecer um suporte de QoS completo, ? fundamental uma autoconsci?ncia abrangente dos recursos do sistema, e assumir t?cnicas adaptativas que permitem agir em ambos os n?veis de comunica??o e computa??o para atender os requisitos das aplica??es. Para suprir essas demandas, essa Tese prop?e uma infraestrutura e t?cnicas de gerenciamento de QoS autoadaptativo, cobrindo ambos os n?veis de computa??o e comunica??o. No n?vel de computa??o, a infraestrutura para QoS consiste em um escalonador din?mico de tarefas de tempo real e um protocolo de migra??o de tarefas de baixo custo. Estas t?cnicas fornecem QoS de computa??o, devido ao gerenciamento da utiliza??o e aloca??o da CPU. A novidade do escalonador de tarefas ? o suporte a requisitos de tempo real din?micos, o que gera mais flexibilidade para as tarefas em explorar a CPU de acordo com uma carga de trabalho vari?vel. A novidade do protocolo de migra??o de tarefas ? o baixo custo no tempo de execu??o comparado a trabalhos do estado-da-arte. No n?vel de comunica??o, a t?cnica proposta ? um chaveamento por circuito (CS) baseado em redes definidas por software (SDN). O paradigma SDN para NoCs ? uma inova??o desta Tese, e ? alcan?ado atrav?s de uma arquitetura gen?rica de software e hardware. Para QoS de comunica??o, SDN ? usado para definir caminhos CS em tempo de execu??o. Essas infraestruturas de QoS s?o gerenciadas de uma forma integrada por um gerenciamento de QoS autoadaptativo, o qual segue o paradigma ODA (Observar, Decidir, Agir), implementando um la?o fechado de adapta??es em tempo de execu??o. O gerenciamento de QoS ? autoconsciente dos recursos do sistema e das aplica??es em execu??o, e pode decidir por adapta??es no n?vel de computa??o ou comunica??o, baseado em notifica??es das tarefas, monitoramento do ambiente, e monitoramento de atendimento de QoS. A autoadapta??o decide reativamente assim como proativamente. Uma t?cnica de aprendizagem do perfil das aplica??es ? proposta para tra?ar o comportamento das tarefas de tempo real, possibilitando a??es proativas. Resultados gerais mostram que o gerenciamento de QoS autoadaptativo proposto pode restaurar os n?veis de QoS para as aplica??es com um baixo custo no tempo de execu??o das aplica??es. Uma avalia??o abrangente, assumindo diversos benchmarks mostra que, mesmo sob diversas interfer?ncias de QoS nos n?veis de computa??o e comunica??o, o tempo de execu??o das aplica??es ? restaurado pr?ximo ao cen?rio ?timo, como 99,5% das viola??es de deadlines mitigadas. / Many-core systems-on-chip are the state-of-the-art in processing power, reaching from a dozen to thousands of processing elements (PE) in a single integrated circuit. General purpose many-cores assume a dynamic application admission, where the application set is unknown at design-time and applications may start their execution at any moment, inducing interference between them. Some applications may have real-time constraints to fulfill, requiring levels of quality of service (QoS) from the system. Due to the high degree of resource?s utilization unpredictability and the number of components to manage, self-adaptive properties become fundamental to support QoS at run-time. The literature provides several self-adaptive QoS proposals, targeting either communication (e.g., Network-on-Chip) or computation resources (e.g., CPU). However, to offer a complete QoS support, it is fundamental to provide a comprehensive self-awareness of the system?s resources, assuming adaptive techniques enabling to act simultaneously at the communication and computation levels to meet the applications' constraints. To cope with these requirements, this Thesis proposes a self-adaptive QoS infrastructure and management techniques, covering both the computation and communication levels. At the computation level, the QoS-driven infrastructure comprises a dynamic real-time task scheduler and a low overhead task migration protocol. These techniques ensure computation QoS by managing the CPU utilization and allocation. The novelty of the task scheduler is the support for dynamic real time constraints, which leverage more flexibility to tasks to explore the CPU according to a variable workload. The novelty of the task migration protocol is its low execution time overhead compared to the state-of-the-art. At the communication level, the proposed technique is a Circuit-Switching (CS) approach based on the Software Defined Networking (SDN) paradigm. The SDN paradigm for NoCs is an innovation of this Thesis and is achieved through a generic software and hardware architecture. For communication QoS, SDN is used to define CS paths at run-time. A self-adaptive QoS management following the ODA (Observe Decide Act) paradigm controls these QoS-driven infrastructures in an integrated way, implementing a closed loop for run time adaptations. The QoS management is self-aware of the system and running applications and can decide to take adaptations at computation or communication levels based on the task feedbacks, environment monitoring, and QoS fulfillment monitoring. The self-adaptation decides reactively as well as proactively. An online application profile learning technique is proposed to trace the behavior of the RT tasks and enabling the proactive actions. Results show that the proposed self-adaptive QoS management can restore the QoS level for the applications with a low overhead over the applications execution time. A broad evaluation, using known benchmarks, shows that even under severe QoS disturbances at computation and communication levels, the execution time of the application is restored near to the optimal scenario, mitigating 99.5% of deadline misses.

Sensoriamento urbano e cidades inteligentes têm sido tópicos derivados da computação ubíqua em alta nos últimos anos, tanto para a academia como para a indústria, devido ao contínuo avanço tecnológico aliado à maior facilidade de acesso e aceitação pelos usuários. Na literatura pesquisada sobre plataformas que englobam tais tópicos foi constatado que diversas delas possuem algum processo autonômico utilizado para atender alguma necessidade de autoadaptação em tempo de execução. Apesar disso, nenhuma das plataformas pesquisadas focou especificamente em encontrar e propor uma solução para tratar exclusivamente a autoadaptação. Nesse contexto, esta dissertação tem por objetivo propor um mecanismo de autoadaptação para sistemas de sensoriamento urbano, além de avaliar seu comportamento. Como primeiro passo para realizar tal objetivo, foi conduzida uma pesquisa literária tendo em vistas identificar os principais casos de adaptação em sistemas de sensoriamento urbano, além de requisitos específicos da arquitetura de sensoriamento urbano UrboSenti, utilizada para implementação. Como segundo passo, a partir dos requisitos identificados, o modelo MAPE-K da computação autonômica foi escolhido como a base da construção do mecanismo de autoadaptação. A implementação deste modelo utilizou as técnicas de eventos passivos para monitoramento do ambiente, regras Evento-Condição-Ação, para tomada de decisão, planos estáticos para planejamento e adaptações por parâmetros e componentes para execução. Tanto o modelo como as técnicas escolhidas foram implementadas devido atenderem as necessidades dos cenários avaliados. Por fim, as avaliações aplicadas apontam resultados preliminares satisfatórios, dados os casos avaliados e os experimentos de tempo de resposta a eventos internos e interações; no entanto, tais avaliações revelarem diversos pontos que devem ser explorados em trabalhos futuros. / In the last years, urban sensing and smart cities have been popular topics derived from the ubiquitous computing, for both the academia and the industry, due to its continuous technological development combined with greater facilities of access and acceptance by the users. The reviewed literature about platforms that encompass such topics showed that many of them have some kind of autonomic process used to meet any need for self-adaptation at runtime. Despite this, none of the researched platforms focused in proposing a solution to exclusively meet the self-adaptation properties. In this way, this dissertation aims to propose a self-adaptive mechanism to urban sensing systems, as well as evaluating its behavior. As the first step to achieving such goal, a literature review was performed aiming to identify the main adaptation cases in urban sensing systems, as well to identify the specific requirements of the UrboSenti architecture for urban sensing. As the second step, the autonomic computing MAPE-K model was chosen to compose the foundation of the self-adaptive mechanism based on the identified requirements. The implementation of this model used the techniques of passive events for monitoring, rules Event-Condition-Action for decision making, static plans for planning and parameter and component adaptations for execution were used in the proposed implementation to meet the evaluated scenario needs. Lastly, the applied evaluations indicate satisfactory results, given the assessed cases and the experiments of scalability at the response of internal events and interactions. However, they have left many open points that should be explored in future works.

Sensoriamento urbano e cidades inteligentes têm sido tópicos derivados da computação ubíqua em alta nos últimos anos, tanto para a academia como para a indústria, devido ao contínuo avanço tecnológico aliado à maior facilidade de acesso e aceitação pelos usuários. Na literatura pesquisada sobre plataformas que englobam tais tópicos foi constatado que diversas delas possuem algum processo autonômico utilizado para atender alguma necessidade de autoadaptação em tempo de execução. Apesar disso, nenhuma das plataformas pesquisadas focou especificamente em encontrar e propor uma solução para tratar exclusivamente a autoadaptação. Nesse contexto, esta dissertação tem por objetivo propor um mecanismo de autoadaptação para sistemas de sensoriamento urbano, além de avaliar seu comportamento. Como primeiro passo para realizar tal objetivo, foi conduzida uma pesquisa literária tendo em vistas identificar os principais casos de adaptação em sistemas de sensoriamento urbano, além de requisitos específicos da arquitetura de sensoriamento urbano UrboSenti, utilizada para implementação. Como segundo passo, a partir dos requisitos identificados, o modelo MAPE-K da computação autonômica foi escolhido como a base da construção do mecanismo de autoadaptação. A implementação deste modelo utilizou as técnicas de eventos passivos para monitoramento do ambiente, regras Evento-Condição-Ação, para tomada de decisão, planos estáticos para planejamento e adaptações por parâmetros e componentes para execução. Tanto o modelo como as técnicas escolhidas foram implementadas devido atenderem as necessidades dos cenários avaliados. Por fim, as avaliações aplicadas apontam resultados preliminares satisfatórios, dados os casos avaliados e os experimentos de tempo de resposta a eventos internos e interações; no entanto, tais avaliações revelarem diversos pontos que devem ser explorados em trabalhos futuros. / In the last years, urban sensing and smart cities have been popular topics derived from the ubiquitous computing, for both the academia and the industry, due to its continuous technological development combined with greater facilities of access and acceptance by the users. The reviewed literature about platforms that encompass such topics showed that many of them have some kind of autonomic process used to meet any need for self-adaptation at runtime. Despite this, none of the researched platforms focused in proposing a solution to exclusively meet the self-adaptation properties. In this way, this dissertation aims to propose a self-adaptive mechanism to urban sensing systems, as well as evaluating its behavior. As the first step to achieving such goal, a literature review was performed aiming to identify the main adaptation cases in urban sensing systems, as well to identify the specific requirements of the UrboSenti architecture for urban sensing. As the second step, the autonomic computing MAPE-K model was chosen to compose the foundation of the self-adaptive mechanism based on the identified requirements. The implementation of this model used the techniques of passive events for monitoring, rules Event-Condition-Action for decision making, static plans for planning and parameter and component adaptations for execution were used in the proposed implementation to meet the evaluated scenario needs. Lastly, the applied evaluations indicate satisfactory results, given the assessed cases and the experiments of scalability at the response of internal events and interactions. However, they have left many open points that should be explored in future works.

abstract: Affect signals what humans care about and is involved in rational decision-making and action selection. Many technologies may be improved by the capability to recognize human affect and to respond adaptively by appropriately modifying their operation. This capability, named affect-driven self-adaptation, benefits systems as diverse as learning environments, healthcare applications, and video games, and indeed has the potential to improve systems that interact intimately with users across all sectors of society. The main challenge is that existing approaches to advancing affect-driven self-adaptive systems typically limit their applicability by supporting the creation of one-of-a-kind systems with hard-wired affect recognition and self-adaptation capabilities, which are brittle, costly to change, and difficult to reuse. A solution to this limitation is to leverage the development of affect-driven self-adaptive systems with a manufacturing vision. This dissertation demonstrates how using a software product line paradigm can jumpstart the development of affect-driven self-adaptive systems with that manufacturing vision. Applying a software product line approach to the affect-driven self-adaptive domain provides a comprehensive, flexible and reusable infrastructure of components with mechanisms to monitor a user’s affect and his/her contextual interaction with a system, to detect opportunities for improvements, to select a course of action, and to effect changes. It also provides a domain-specific architecture and well-documented process guidelines, which facilitate an understanding of the organization of affect-driven self-adaptive systems and their implementation by systematically customizing the infrastructure to effectively address the particular requirements of specific systems. The software product line approach is evaluated by applying it in the development of learning environments and video games that demonstrate the significant potential of the solution, across diverse development scenarios and applications. The key contributions of this work include extending self-adaptive system modeling, implementing a reusable infrastructure, and leveraging the use of patterns to exploit the commonalities between systems in the affect-driven self-adaptation domain. / Dissertation/Thesis / Doctoral Dissertation Computer Science 2016