Applying evolutionary computation to mitigate uncertainty in dynamically-adaptive, high-assurance middleware

McKinley, Philip K.; Cheng, Betty H. C.; Ramirez, Andres J.; Jensen, Adam C.

doi:10.1007/s13174-011-0049-4

Cited by 9 publications

(10 citation statements)

References 33 publications

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“…• requirements reflection [81][82][83][84] • architectural styles for runtime adaptation [85,86] • digital evolution of behavioral models for autonomic systems [87][88][89][90][91] • evolutionary computation for DASs [92,93].…”

Section: Dass-based Methods That Support Self-* Propertiesmentioning

confidence: 99%

“…Recently, there has been considerable interest within the software engineering research community (e.g., [87][88][89][90][91][92][93] In [97], a taxonomy of potential sources of uncertainty from the DASs perspective has been presented with techniques for mitigating them. Evolutionary computation is a subfield of computer science which applies the basic principles of genetic evolution to problem-solving [91].…”

Section: Dass-based Methods That Support Self-* Propertiesmentioning

confidence: 99%

“…In a related method to the preceding method, in [92,93] the authors describe a process and a suite of tools to support the development of DASs. Their top-down approach starts with requirements and moves through reconfigurable designs at runtime.…”

Section: Evolutionary Computation For Dassmentioning

confidence: 99%

“…The sources of uncertainty in these aspects can happen at runtime, design time and requirements, and the authors try to address uncertainty with three enabling technologies: model-based development, assurance and dynamic adaptation. They highlight several evolutionary computation methods, such as genetic algorithms, genetic programming, artificial life and digital evolution, and evolved artificial neural networks [92,93]. Novel evolutionary algorithms have been harnessed at both design and runtime.…”

Section: Evolutionary Computation For Dassmentioning

confidence: 99%

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A survey of autonomic computing methods in digital service ecosystems

Abeywickrama

Ovaska

2016

SOCA

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Service engineering of digital service ecosystems can be associated with several challenges, such as change and evolution of requirements; gathering of quality requirements and assessment; and uncertainty caused by dynamic nature and unknown deployment environment, composition and users. Therefore, the complexity and dynamics in which these digital services are deployed call for solutions to make them autonomic. Until now there has been no upto-date review of the scientific literature on the application of the autonomic computing initiative in the digital service ecosystems domain. This article presents a review and comparison of autonomic computing methods in digital service ecosystems from the perspective of service engineering, i.e., requirements engineering and architecting of services. The review is based on systematic queries in four leading scientific databases and Google Scholar, and it is organized in four thematic research areas. A comparison framework has been defined which can be used as a guide for comparing the different methods selected. The goal is to discover which methods are suitable for the service engineering of digital service ecosystems with autonomic computing capabilities, highlight what the shortcomings of the methods are, and identify which research activities need to be conducted in order to overcome these shortcomings. The comparison reveals that none of the existing methods entirely fulfills the requirements that are defined in the comparison framework.B Dhaminda B. Abeywickrama

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Section: Dass-based Methods That Support Self-* Propertiesmentioning

confidence: 99%

Section: Dass-based Methods That Support Self-* Propertiesmentioning

confidence: 99%

Section: Evolutionary Computation For Dassmentioning

confidence: 99%

Section: Evolutionary Computation For Dassmentioning

confidence: 99%

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A survey of autonomic computing methods in digital service ecosystems

Abeywickrama

Ovaska

2016

SOCA

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“…In particular, reflective middleware technologies [60] use causally connected selfrepresentations [16] to support the inspection and adaptation of the middleware system [89]. Components defined at the model level are directly mapped to specific artifacts that realize those components at the implementation level.…”

Section: Research Challengesmentioning

confidence: 99%

Mechanisms for Leveraging Models at Runtime in Self-adaptive Software

et al. 2014

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Abstract. Modern software systems are often required to adapt their behavior at runtime in order to maintain or enhance their utility in dynamic environments. Models at runtime research aims to provide suitable abstractions, techniques, and tools to manage the complexity of adapting software systems at runtime. In this chapter, we discuss challenges associated with developing mechanisms that leverage models at runtime to support runtime software adaptation. Specifically, we discuss challenges associated with developing effective mechanisms for supervising running systems, reasoning about and planning adaptations, maintaining consistency among multiple runtime models, and maintaining fidelity of runtime models with respect to the running system and its environment. We discuss related problems and state-of-the-art mechanisms, and identify open research challenges.

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