Digital Evolution of Behavioral Models for Autonomic Systems

Goldsby, Heather J.; Cheng, Betty H. C.; McKinley, Philip K.; Knoester, David B.; Ofria, Charles

doi:10.1109/icac.2008.26

Cited by 25 publications

(23 citation statements)

References 32 publications

(49 reference statements)

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“…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%

“…• 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%

“…The authors highlight two scalability challenges and present how their method will scale when used with larger applications. The two challenges are, first, allowing organisms to evolve large and increasingly complex diagrams, and second, model checking of the diagrams to verify that the functional properties are satisfied [88]. These potential scalability challenges have been addressed through the model abstraction and incremental development features of their method [88].…”

Section: Digital Evolution Of Behavioral Models For Autonomic Systemsmentioning

confidence: 99%

“…By leveraging the Darwinian evolution, in [87][88][89][90][91] the authors propose a software development methodology capable of producing self-* software. They investigate the application of digital evolution to the design of software that exhibit self-* properties.…”

Section: Digital Evolution Of Behavioral Models For Autonomic Systemsmentioning

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: Digital Evolution Of Behavioral Models For Autonomic Systemsmentioning

confidence: 99%

Section: Digital Evolution Of Behavioral Models For Autonomic Systemsmentioning

confidence: 99%

See 2 more Smart Citations

A survey of autonomic computing methods in digital service ecosystems

Abeywickrama

Ovaska

2016

SOCA

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“…To ensure the generality of the control experiments, in addition to assessing Marple's performance on the door locking system, we also evaluated it on a model of an autonomous robot navigation case study, originally developed by Park et al [21], and later revised and modeled by us [22]. To account for the stochastic nature of the evolutionary process, for each model, we ran 30 control runs and 30 Marple runs.…”

Section: Validationmentioning

confidence: 99%

Automatically Discovering Properties That Specify the Latent Behavior of UML Models

Goldsby

Cheng

2010

Model Driven Engineering Languages and Systems

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Abstract. Formal analysis can be used to verify that a model of the system adheres to its requirements. As such, traditional formal analysis focuses on whether known (desired) system properties are satisfied. In contrast, this paper proposes an automated approach to generating temporal logic properties that specify the latent behavior of existing UML models; these are unknown properties exhibited by the system that may or may not be desirable. A key component of our approach is Marple, a evolutionary-computation tool that leverages natural selection to discover a set of properties that cover different regions of the model state space. The Marple-discovered properties can be used to refine the models to either remove unwanted behavior or to explicitly document a desirable property as required system behavior. We use Marple to discover unwanted latent behavior in two applications: an autonomous robot navigation system and an automotive door locking control system obtained from one of our industrial collaborators.

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Engineering Resilient Space Systems

Day¹,

Ingham²,

Murray³

et al. 2015

INSIGHT

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Several distinct trends will influence space exploration missions in the next decade. Destinations are becoming more remote and mysterious, science questions more sophisticated, and, as mission experience accumulates, the most accessible targets are visited, advancing the knowledge frontier to more difficult, harsh, and inaccessible environments. This leads to new challenges including: hazardous conditions that limit mission lifetime, such as high radiation levels surrounding interesting destinations like Europa or toxic atmospheres of planetary bodies like Venus; unconstrained environments with navigation hazards, such as free-floating active small bodies; multielement missions required to answer more sophisticated questions, such as Mars Sample Return (MSR); and long-range missions, such as Kuiper belt exploration, that must survive equipment failures over the span of decades. These missions will need to be successful without a priori knowledge of the most efficient data collection techniques for optimum science return. Science objectives will have to be revised 'on the fly', with new data collection and navigation decisions on short timescales. Yet, even as science objectives are becoming more ambitious, several critical resources remain unchanged. Since physics imposes insurmountable light-time delays, anticipated improvements to the Deep Space Network (DSN) will only marginally improve the bandwidth and communications cadence to remote spacecraft. Fiscal resources are increasingly limited, resulting in fewer flagship missions, smaller spacecraft, and less subsystem redundancy. As missions visit more distant and formidable locations, the job of the operations team becomes more challenging, seemingly inconsistent with the trend of shrinking mission budgets for operations support. How can we continue to explore challenging new locations without increasing risk or system complexity? These challenges are present, to some degree, for the entire Decadal Survey mission portfolio, as documented in Vision and Voyages for Planetary Science in the

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Digital Evolution of Behavioral Models for Autonomic Systems

Cited by 25 publications

References 32 publications

A survey of autonomic computing methods in digital service ecosystems

A survey of autonomic computing methods in digital service ecosystems

Automatically Discovering Properties That Specify the Latent Behavior of UML Models

Engineering Resilient Space Systems

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