19,147 research outputs found
Achieving Autonomic Computing through the Use of Variability Models at Run-time
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/10251/7484Palanci
Adaptable transition systems
We present an essential model of adaptable transition systems inspired by white-box approaches to adaptation and based on foundational models of component based systems. The key feature of adaptable transition systems are control propositions, imposing a clear separation between ordinary, functional behaviours and adaptive ones. We instantiate our approach on interface automata yielding adaptable interface automata, but it may be instantiated on other foundational models of component-based systems as well. We discuss how control propositions can be exploited in the specification and analysis of adaptive systems, focusing on various notions proposed in the literature, like adaptability, control loops, and control synthesis
Self-Learning Cloud Controllers: Fuzzy Q-Learning for Knowledge Evolution
Cloud controllers aim at responding to application demands by automatically
scaling the compute resources at runtime to meet performance guarantees and
minimize resource costs. Existing cloud controllers often resort to scaling
strategies that are codified as a set of adaptation rules. However, for a cloud
provider, applications running on top of the cloud infrastructure are more or
less black-boxes, making it difficult at design time to define optimal or
pre-emptive adaptation rules. Thus, the burden of taking adaptation decisions
often is delegated to the cloud application. Yet, in most cases, application
developers in turn have limited knowledge of the cloud infrastructure. In this
paper, we propose learning adaptation rules during runtime. To this end, we
introduce FQL4KE, a self-learning fuzzy cloud controller. In particular, FQL4KE
learns and modifies fuzzy rules at runtime. The benefit is that for designing
cloud controllers, we do not have to rely solely on precise design-time
knowledge, which may be difficult to acquire. FQL4KE empowers users to specify
cloud controllers by simply adjusting weights representing priorities in system
goals instead of specifying complex adaptation rules. The applicability of
FQL4KE has been experimentally assessed as part of the cloud application
framework ElasticBench. The experimental results indicate that FQL4KE
outperforms our previously developed fuzzy controller without learning
mechanisms and the native Azure auto-scaling
Context-aware adaptation in DySCAS
DySCAS is a dynamically self-configuring middleware for automotive control systems. The addition of autonomic, context-aware dynamic configuration to automotive control systems brings a potential for a wide range of benefits in terms of robustness, flexibility, upgrading etc. However, the automotive systems represent a particularly challenging domain for the deployment of autonomics concepts, having a combination of real-time performance constraints, severe resource limitations, safety-critical aspects and cost pressures. For these reasons current systems are statically configured. This paper describes the dynamic run-time configuration aspects of DySCAS and focuses on the extent to which context-aware adaptation has been achieved in DySCAS, and the ways in which the various design and implementation challenges are met
Uma solução de implantação auto-adaptativa para plataformas Android
Orientador: Cecília Mary Fischer RubiraDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Os dispositivos móveis, hoje em dia, fornecem recursos semelhantes aos de um computador pessoal de uma década atrás, permitindo o desenvolvimento de aplicações complexas. Consequentemente, essas aplicações móveis podem exigir tolerar falhas em tempo de execução. No entanto, a maioria das aplicações móveis de hoje são implantados usando configurações estáticas, tornando difícil tolerar falhas durante a sua execução. Nós propomos uma infraestrutura de implantação auto-adaptativa para lidar com este problema. A nossa solução oferece um circuito autônomo que administra o modelo de configuração atual da aplicação usando um modelo de características dinâmico associado com o modelo arquitetônico da mesma. Em tempo de execução, de acordo com a seleção dinâmica de características, o modelo arquitetônico implantado na plataforma se re-configura para fornecer uma nova solução. Uma aplicação Android foi implementada utilizando a solução proposta, e durante sua execução, a disponibilidade de serviços foi alterada, de tal forma que sua configuração corrente foi dinamicamente alterada para tolerar a indisponibilidade dos serviçosAbstract: Mobile devices, nowadays, provide similar capabilities as a personal computer of a decade ago, allowing the development of complex applications. Consequently, these mobile applications may require tolerating failures at runtime. However, most of the today¿s mobile applications are deployed using static configurations, making difficult to tolerate failure during their execution. We propose an adaptive deployment infrastructure to deal with this problem. Our solution offers an autonomic loop that manages the current configuration model of the application using a dynamic feature model associated with the architectural model. During runtime, according to the dynamic feature selection, the deployed architectural model can be modified to provide a new deployment solution. An Android application was implemented using the proposed solution, and during its execution, the services availability was altered so that its current configuration was changed dynamically in order to tolerate the unavailability of servicesMestradoCiência da ComputaçãoMestre em Ciência da Computação131830/2013-9CNP
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