RAIDE for engineering architecture-based self-adaptive systems

Cheng, Shang-Wen; Garlan, David; Schmerl, Bradley

doi:10.1109/icse-companion.2009.5071049

Cited by 6 publications

(6 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To carry out our study, we considered two alternative models of Znn, one using impact vectors 11 and the other using the proposed impact models. We manually encoded the two models in the language of PRISM [23] and assessed the quality of the models that we are able to specify in each case by quantifying: (i) impact of tactics on the state of the target system, and (ii) impact of strategies on system utility.…”

Section: Resultsmentioning

confidence: 99%

“…cx cert (lines 8-9) is the predicted response time for client x according to the probabilistic impact models for tactics, whereas cert (line 1) is the overall predicted response time based on probabilistic impact models. sx active (lines [11][12][13][14] indicates whether server x is currently active, and sx f (lines [16][17][18][19] indicates the current fidelity level of server x.…”

Section: Appendix a Znncom Smg Prism Encodingmentioning

confidence: 99%

“…Self-adaptive systems have the ability to autonomously change their behavior in response to changes in their operating conditions, thus preserving the capability of meeting certain requirements. For instance, to provide timely response to service requests, a news website with self-adaptive capabilities can react to high response latencies by activating more servers, or reducing the fidelity of the contents being served [11,22].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptation impact and environment models for architecture-based self-adaptive systems

Cámara

Lopes

Garlan

et al. 2016

Science of Computer Programming

Self Cite

View full text Add to dashboard Cite

Self-adaptive systems have the ability to adapt their behavior to dynamic operating conditions. In reaction to changes in the environment, these systems determine the appropriate corrective actions based in part on information about which action will have the best impact on the system. Existing models used to describe the impact of adaptations are either unable to capture the underlying uncertainty and variability of such dynamic environments, or are not compositional and described at a level of abstraction too low to scale in terms of specification effort required for non-trivial systems. In this paper, we address these shortcomings by describing an approach to the specification of impact models based on architectural system descriptions, which at the same time allows us to represent both variability and uncertainty in the outcome of adaptations, hence improving the selection of the best corrective action. The core of our approach is a language equipped with a formal semantics defined in terms of Discrete Time Markov Chains that enables us to describe both the impact of adaptation tactics, as well as the assumptions about the environment. To validate our approach, we show how employing our language can improve the accuracy of predictions used for decision-making in the Rainbow framework for architecture-based self-adaptation.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Appendix a Znncom Smg Prism Encodingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Adaptation impact and environment models for architecture-based self-adaptive systems

Cámara

Lopes

Garlan

et al. 2016

Science of Computer Programming

Self Cite

View full text Add to dashboard Cite

show abstract

“…Autonomic computing, or self-adaptive systems [10,13,22], have emerged more recently as an alternative to overcome the shortcomings presented by the aforementioned approaches. In particular, architecture-based self-adaptation [17,23,25] leverages architecture models to enable highlevel reasoning about the best way of adapting a system, and is regarded as a promising approach to building resilient software systems in a cost-effective manner.…”

Section: Introductionmentioning

confidence: 99%

Empirical resilience evaluation of an architecture-based self-adaptive software system

Cámara

Correia

Lemos

et al. 2014

Proceedings of the 10th International ACM Sigsoft Conference on Quality of Software Architectures

View full text Add to dashboard Cite

Architecture-based self-adaptation is considered as a promising approach to drive down the development and operation costs of complex software systems operating in ever changing environments. However, there is still a lack of evidence supporting the arguments for the beneficial impact of architecture-based self-adaptation on resilience with respect to other customary approaches, such as embedded code-based adaptation. In this paper, we report on an empirical study about the impact on resilience of incorporating architecture-based self-adaptation in an industrial middleware used to collect data in highly populated networks of devices. To this end, we compare the results of resilience evaluation between the original version of the middleware, in which adaptation mechanisms are embedded at the codelevel, and a modified version of that middleware in which the adaptation mechanisms are implemented using Rainbow, a framework for architecture-based self-adaptation. Our results show improved levels of resilience in architecture-based compared to embedded code-based self-adaptation.

show abstract

“…For instance, to provide timely response to service requests, a news website with self-adaptive capabilities can react to high response latencies by activating more servers, or reducing the fidelity of contents being served [6,12].…”

Section: Introductionmentioning

confidence: 99%

Impact Models for Architecture-Based Self-adaptive Systems

Moreno

Lopes

Garlan

et al. 2015

Formal Aspects of Component Software

Self Cite

View full text Add to dashboard Cite

Abstract. Self-adaptive systems have the ability to adapt their behavior to dynamic operation conditions. In reaction to changes in the environment, these systems determine the appropriate corrective actions based in part on information about which action will have the best impact on the system. Existing models used to describe the impact of adaptations are either unable to capture the underlying uncertainty and variability of such dynamic environments, or are not compositional and described at a level of abstraction too low to scale in terms of specification effort required for non-trivial systems. In this paper, we address these shortcomings by describing an approach to the specification of impact models based on architectural system descriptions, which at the same time allows us to represent both variability and uncertainty in the outcome of adaptations, hence improving the selection of the best corrective action. The core of our approach is an impact model language equipped with a formal semantics defined in terms of Discrete Time Markov Chains. To validate our approach, we show how employing our language can improve the accuracy of predictions used for decisionmaking in the Rainbow framework for architecture-based self-adaptation.

show abstract

RAIDE for engineering architecture-based self-adaptive systems

Abstract: Abstract

Cited by 6 publications

References 4 publications

Adaptation impact and environment models for architecture-based self-adaptive systems

Adaptation impact and environment models for architecture-based self-adaptive systems

Empirical resilience evaluation of an architecture-based self-adaptive software system

Impact Models for Architecture-Based Self-adaptive Systems

Contact Info

Product

Resources

About