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.

Submitted by Fabio Sobreira Campos da Costa (fabio.sobreira@ufpe.br) on 2016-07-12T12:20:38Z No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Thesis - André Luis Cavalcanti Moreira.pdf: 3666881 bytes, checksum: 956e0e6be2bd9f076c0d30eea9d3ea25 (MD5) / Made available in DSpace on 2016-07-12T12:20:38Z (GMT). No. of bitstreams: 2 license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Thesis - André Luis Cavalcanti Moreira.pdf: 3666881 bytes, checksum: 956e0e6be2bd9f076c0d30eea9d3ea25 (MD5) Previous issue date: 2015-08-28 / Several works study the performance of Content Delivery Networks (CDNs) under various network infrastructure and demand conditions. Many strategies have been proposed to deal with aspects inherent to the CDN distribution model. Though mostly very effective, a traditional CDN approach of statically positioned elements often fails to meet quality of experience (QoE) requirements when network conditions suddenly change. CDN adaptation is a key feature in this process and some studies go even further and try to also deal with demand elasticity by providing an elastic infrastructure (cloud computing) to such CDNs. Each Content Provider (CP) gets served only the amount of storage space and network throughput that it needs and pays only for what has been used. Some IaaS providers offer simple CDN services on top of their infrastructure. However, in general, there is a lack of PaaS tools to create rapidly a CDN. There is no standard or open source software able to deliver CDN as a service for each tenant through well-known managers. A PaaS CDN should be able to implement content delivery service in a cloud environment, provision and orchestrate each tenant, monitor usage and make decisions on planning and dimensioning of resources. This work introduces a framework for the allocation of resources of a CDN in a multi-tenant environment. The framework is able to provision and orchestrate multi-tenant virtual CDNs and can be seen as a step towards a PaaS CDN. A simple dot product based module for network change detection is presented and a more elaborate multi-tenant resource manager model is defined. We solve the resulting ILP problem using both branch and bound as well as an efficient cache slicing algorithm that employs a three phase heuristic for orchestration of multi-tenant virtual CDNs. We finally show that a distributed algorithm with limited local information may be also offer reasonable resource allocation while using limited coordination among the different nodes. A self-organization behavior emerges when some of the nodes reach consensus. / Vários trabalhos estudam o desempenho de Redes de Distribuição de Conteúdo (CDN) em diferentes condições e demanda e de infraestrutura. Muitas estratégias têm sido propostas para lidar com aspectos inerentes ao modelo de distribuição de CDN. Embora essas técnicas sejam bastante eficazes, uma abordagem tradicional de elementos estaticamente posicionados numa CDN muitas vezes não consegue atender os requisitos de qualidade de experiência (QoE) quando as condições da rede mudam repentinamente. Adaptação CDN é uma característica fundamental neste processo e alguns estudos vão ainda mais longe e tentam lidar com a elasticidade da demanda, proporcionando uma infraestrutura elástica (computação em nuvem) para a CDN. Cada provedor de conteúdo obtém apenas a quantidade de armazenamento e de rede necessários, pagando apenas pelo efetivo uso. Alguns provedores IaaS oferecem serviços de CDN sobre suas estruturas. No entanto, em geral, não existe padrão ou softwares de código aberto capazes de entregar serviços de CDN por meio de gerenciadores. Uma CDN PaaS deve ser capaz de fornecer um serviço de entrega de conteúdo em um ambiente de nuvem, provisionar e orquestrar cada tenant, monitorar uso e tomar decisões de planejamento e dimensionamento de recursos. Este trabalho apresenta um framework para alocação de recursos de uma CDN em ambiente multi-tenant. O framework é capaz de provisionar e orquestrar CDNs virtuais e pode ser visto como um passo em direção a uma PaaS CDN. Um módulo baseado em simples produto escalar para detecção de mudanças na rede é apresentado, bem como um modelo mais elaborado de gerenciamento de recursos. Resolvemos o problema ILP resultante dessa abordagem por meio de um algoritmo de divisão de cache que emprega uma heurística em três fases para a orquestração de CDN virtuais. Por fim, mostramos uma outra abordagem com algoritmo distribuído que usa informação local e que também oferece uma alocação razoável usando coordenação limitada entre os diferentes nós. Um comportamento de auto-organização surge quando alguns desses nós chegam a um consenso.

Software maintenance is a significant part of software system lifetime. Softwaresystem lifetime incorporates many processes including software adaptation and software evolution. These processes collide into one another and create confusion as theboundaries that separate them are often difficult to distinguish. Knowing what exactly these concepts indicate and how they are related can bring simplicity to futuredevelopment of adaptive systems. The following document presents a performed systematic literature review, which aims to outline the similarities and the differences ofadaptation and evolution and further explain how they are related. The results of thestudy show that adaptation and evolution have become more entwined with growthof interest to self-managing dynamic software.

The execution of service oriented applications is based on the invocation of services that perform tasks for the application. Often, multiple service providers provide the same service, with the main differentiating factor being their Quality of Service. Moreover, the quality of a service provider varies over time as it is influenced by environmental factors such as service load, software crashes, and changes in pricing. Therefore, obtaining consistent good quality for each of the required services is not an easy task. Service invocation patterns had been proposed to aid service oriented applications in this task. We can use these invocation patterns to help the application decide which service it should invoke. However, the evaluation of the applications behavior using the invocation patterns when the quality of the service providers changes over time is a problem that has not been addressed yet. To address this, this thesis investigates the utilization and generation of service invocation patterns. Concretely, I have implemented 1) a simulator of service oriented applications that use invocations patterns and 2) an adaptation engine that dynamically generates invocations patterns. The latter can be used to update the values in the service invocation patterns when the quality of the service providers changes. I have implemented the patterns according to existing definitions and integrated them into the application. I have also conducted series of experiments in order to examine the effect of self-adaptation using invocation patterns on the quality of the application. The results of the experiments conducted in this thesis indicate that adaptive invocation patterns have a positive effect on the quality of the application.

Cyber-physical systems (CPS) need to be designed to deal with various forms of uncertainty associated with data contributing to the system's knowledge of the environment. Dealing with uncertainty requires adopting an appropriate model, which then allows making the right decisions and carrying out the right actions (possibly affecting the environment) based on imperfect information. However, choosing and incorporating a suitable model into CPS design is difficult, because it requires identifying the kind of uncertainty at hand as well as knowledge of suitable models and their application to dealing with the uncertainty. While inspiration can be found in other CPS designs, the details of dealing with uncertainty in another CPS can be confounded by domain-specific terminology, context, and requirements. To make this aspect of CPS design less daunting, we aim at providing an overview of approaches dealing with uncertainty in the design of CPS targeting collective behavior. To this end, we present a systematic review of relevant scientific projects with industrial leadership and synthesis of relations between system features, the kinds of uncertainty, and methods used to deal with it. The results provide an overview of uncertainty across different domains and challenges and reason about a guide for...

The project aims to propose MAPE based hybrid elasticity management framework on the basis of valuable insights accrued during systematic analysis of relevant literature. Each stage of MAPE process acts independently as a black box in proposed framework, while dealing with neighbouring stages. Thus, being modular in nature; underlying algorithms in any of the stage can be replaced with more suitable ones, without affecting any other stage. The hybrid framework enables proactive and reactive autoscaling approaches to be implemented simultaneously within same system. Proactive approach is incorporated as a core decision making logic on the basis of forecast data, while reactive approach being based upon actual data would act as a damage control measure; activated only in case of any problem with proactive approach. Thus, benefits of both the worlds; pre-emption as well as reliability can be achieved through proposed framework. It uses time series analysis (moving average method / exponential smoothing) and threshold based static rules (with multiple monitoring intervals and dual threshold settings) during analysis and planning phases of MAPE loop, respectively. Mathematical illustration of the framework incorporates multiple parameters namely VM initiation delay / release criterion, network latency, system oscillations, threshold values, smart kill etc. The research concludes that recommended parameter settings primarily depend upon certain autoscaling objective and are often conflicting in nature. Thus, no single autoscaling system with similar values can possibly meet all objectives simultaneously, irrespective of reliability of an underlying framework. The project successfully implements complete cloud infrastructure and autoscaling environment over experimental platforms i-e OpenStack and CloudSim Plus. In nutshell, the research provides solid understanding of autoscaling phenomenon, devises MAPE based hybrid elasticity management framework and explores its implementation potential over OpenStack and CloudSim Plus.

With the increase in applications of wireless sensor networks, data extraction and visualisation have become a key issue to develop and operate these networks. Wireless sensor networks typically gather data at a discrete number of locations. By bestowing the ability to predict inter-node values upon the network, it is proposed that it will become possible to build applications that are unaware of the concrete reality of sparse data. The aim of this thesis is to develop a service for maximising information return from large scale wireless sensor networks. This aim will be achieved through the development of a distributed information extraction and visualisation service called the mapping service. In the distributed mapping service, groups of network nodes cooperate to produce local maps which are cached and merged at a sink node, producing a map of the global network. Such a service would greatly simplify the production of higher-level information-rich representations suitable for informing other network services and the delivery of field information visualisations. The proposed distributed mapping service utilises a blend of both inductive and deductive models to successfully map sense data and the universal physical principles. It utilises the special characteristics of the application domain to render visualisations in a map format that are a precise reflection of the concrete reality. This service is suitable for visualising an arbitrary number of sense modalities. It is capable of visualising from multiple independent types of the sense data to overcome the limitations of generating visualisations from a single type of a sense modality. Furthermore, the proposed mapping service responds to changes in the environmental conditions that may impact the visualisation performance by continuously updating the application domain model in a distributed manner. Finally, a newdistributed self-adaptation algorithm, Virtual Congress Algorithm,which is based on the concept of virtual congress is proposed, with the goal of saving more power and generating more accurate data visualisation.

Made available in DSpace on 2014-07-29T14:57:47Z (GMT). No. of bitstreams: 1 dissertacao-lucas-provensi.pdf: 5576607 bytes, checksum: 06104d3cf6798c4eddf94b82f7e2be4f (MD5) Previous issue date: 2009-04-06 / The distributed computing environment today is characterized by increasing diversity, the dynamism and increasingly complex applications. Applications for this type of environment they need autonomy to conduct management activities such as installation, configuration and maintenance, with the aim of decreasing the cost and effort involved in such activities. In mobile environments, for example, applications need to adapt due to changes in the environment (available bandwidth, signal level, etc..), Which can affect the quality of service offered to users. This paper presents a proposal for self-adaptation based on the architecture of Meta-ORB reflective middleware. The fluffy main purpose is to provide support to self-adaptation for applications that have requirements of service quality. In this paper, the concept of adaptation policies was introduced in the meta-model middleware, allowing the developer to define both the model of adaptive behavior as their applications using the same modeling language. This unified model is available at run time, being used by a mechanism of self-adaptation to the embedded middleware. The proposed architecture was implemented in a prototype called MetaORB.NET, employed in a case study to evaluate the use of architecture in the development of self-adaptive applications / O ambiente de computação distribuída atual é caracterizado pela crescente heterogeneidade, pelo dinamismo e por aplicações cada vez mais complexas. Aplicações para esse tipo de ambiente precisam de autonomia para realizar atividades de gerenciamento, tais como instalação, configuração e manutenção, com o propósito de diminuir o custo e o esforço envolvidos em tais atividades. Em ambientes móveis, por exemplo, as aplicações precisam se adaptar em função de mudanças no ambiente (largura de banda disponível, nível do sinal, etc.), que podem afetar a qualidade do serviço oferecida aos usuários. Este trabalho apresenta uma proposta para auto-adaptação baseada na arquitetura de middleware reflexivo Meta-ORB. O fofo principal da proposta é prover suporte à auto-adaptação para aplicações que apresentam requisitos de qualidade de serviço. Neste trabalho, o conceito de políticas de adaptação foi introduzido no meta-modelo do middleware, permitindo que o desenvolvedor defina tanto o modelo das aplicações quanto seu comportamento adaptativo usando a mesma linguagem de modelagem. Esse modelo unificado fica disponível em tempo de execução, sendo usado por um mecanismo de auto-adaptação incorporado ao middleware. A arquitetura proposta foi implementada em um protótipo chamado MetaORB.NET, empregado em um estudo de caso para avaliar o uso da arquitetura no desenvolvimento de aplicações auto-adaptativas.

Vývoj v informačním systému neustále zvyšuje nároky na účinnou, efektivní a adaptivní spolupráci mezi komponenty systému, aby se vyrovnal se změnami v systému a zaručil tak nejlepší výkon. K dosažení těchto požadavků byly zavedeny dva hlavní přístupy. Přístup k adaptaci umožňuje informačnímu systému přizpůsobit se změnám v kontextu informací systému a jeho okolního prostředí na základě adaptační strategie. Přístup ke zvyšování informovanosti zase napomáhá sledovat informace o kontextu a rozpoznat změny, které mohou proces adaptace vyvolat. V této práci představujeme obecný kontextově orientovaný model vlastní adaptivní architektury pro podporu softwarového systému s adaptačními funkcemi, které zaručují dostupnost systému, provozní podmínky a výkon. Navíc poskytujeme dvě případové studie v reálném životě jako důkaz konceptu použitelnosti našeho navrhovaného adaptačního přístupu.

<p>  </p> <p>Manufacturing flexibility and human-centered designs are promising approaches to face the demand for individualized products. Human-robot assembly cells still lack flexibility and adaptability (VDI, 2017) using static control architectures (Bessler et al., 2020). Autonomous adaptation to human operators in short time horizons increases the willingness to work with cobots. Besides, monotonous static assembling in manufacturing operations does not accommodate the human way of working. Therefore, Human-Robot Collaboration (HRC) workstations require a work behavior adaptation accommodating varying work behavior regarding human mental and physical conditions (Weiss et al., 2021). The thesis presents the development of a cyber-physical HRC assembly station.</p> <p>Moreover, the thesis includes an experimental study investigating the influence of a cobot’s speed on human work behavior. The Cyber-Physical System (CPS) integrates the experiment's findings with event-based software architecture and a semantic knowledge representation. Thereby, the work focuses on demonstrating the feasibility of the CPS and the semantic model, allowing the self-adaptation of the system. Finally, the conclusion identifies the need for further research in human work behavior detection and fuzzy decision models. Such detection and decision models could improve self-adaptation in human-centered assembly systems.</p>