From Wetware to Software: A Cybernetic Perspective of Self-adaptive Software

Laws, Andy; Taleb-Bendiab, A.; Wade, S.; Reilly, Denis

doi:10.1007/3-540-36554-0_19

Cited by 11 publications

(11 citation statements)

References 15 publications

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“…VSM can also be used in a constructive way, as a design pattern (Herring and Kaplan 2001;Laws et al 2001Laws et al , 2003Shackleton et al 2004). Its main perceived role here, however, is in the refinement of a system design in support of SSM.…”

Section: Proposed Design Methodsmentioning

confidence: 97%

“…A viable system is one that is robust against internal malfunction or external disturbance; it has the ability to respond and adapt to unexpected stimuli, allowing it to survive in a changing and unpredictable environment (Beer 1979(Beer , 1981(Beer , 1984(Beer , 1985Herring and Kaplan 2001;Herring 2002;Laws et al 2001Laws et al , 2003. Stafford Beer developed the Viable System Model as a way to describe the essential elements of a viable system, with particular reference to an organisation or organism (Beer 1984).…”

Section: Vsm and Autonomic Systemsmentioning

confidence: 98%

“…The original model identified five necessary and sufficient subsystems that together maintain overall system viability. This was later extended to a sixth subsystem as indicated in the summary in Table 1 (Laws et al 2003).…”

Section: Vsm and Autonomic Systemsmentioning

confidence: 99%

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Autonomic system design based on the integrated use of SSM and VSM

et al. 2006

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This paper describes an approach to autonomic systems development that involves the integrated use of two general systems design techniques: Soft Systems Methodology (SSM) and the Viable System Model (VSM). The paper summarizes the relevant aspects of each technique and describes how they can be used together to create design models of an autonomic system and its environment. The discussion is illustrated through a consideration of the development of a generic computing system to help manage the technology used in an organisation.

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Section: Proposed Design Methodsmentioning

confidence: 97%

Section: Vsm and Autonomic Systemsmentioning

confidence: 98%

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Autonomic system design based on the integrated use of SSM and VSM

et al. 2006

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“…Generally speaking, self-adaptive software system modifies its own structure or behavior to correct faults, to improve performance or other attributes, or to adapt the product to a changing operating environment [12]. So we will exemplify our approach from these points of view.…”

Section: B Aop-based Runtime Adaptationmentioning

confidence: 99%

Increase Software Dependability via AOP-Based Runtime Adaptation

Tang¹,

Li²,

Zhu³

2015

LNSE

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Abstract-Increasinglycomplex application systems operating in dynamic environments are forcing researchers to find ways of developing and evolving dependable software systems dynamically. Runtime Adaptation is a critical feature for dependable application systems to cope with changes in the operating conditions and to meet the required dependability. AOP can improve the modularity of self-adaptive software by encapsulating the crosscutting concerns into aspects cleanly. Therefore, in this paper we take the advantages of AOP and self-adaptive technique, and propose an AOP-based dynamic adaptation approach. We show how to increase system dependability by runtime adaptation from the perspective of system internal state and external environment. Meanwhile, we illustrate our approach by applying it to a simplified on-line train ticket reservation system.

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“…For instance, self-adaptive software can be implemented by communicating software agents [187,177]. Laws et al follow this direction by proposing agent models for such systems [194] and highlighting links to cybernetics, complex adaptive systems, and biologically inspired algorithms [141,193]. Cybernetic research provides some fundamental insights into adaptive systems.…”

Section: Self-adaptive Software and Autonomous Computingmentioning

confidence: 99%

Automatic Algorithm Selection for Complex Simulation Problems

Ewald¹

2012

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To select the most suitable simulation algorithm for a given task is often difficult. This is due to intricate interactions between model features, implementation details, and runtime environment, which may strongly affect the overall performance. An automated selection of simulation algorithms supports users in setting up simulation experiments, without demanding expert knowledge on simulation.The first part of the thesis surveys existing approaches to solve the algorithm selection problem and discusses techniques to analyze simulation algorithm performance. A unified categorization of existing algorithm selection techniques is worked out, as these stem from various research domains (e.g., finance, artificial intelligence).The second part introduces a software framework for automatic simulation algorithm selection and describes its constituents, as well as their integration into the modeling and simulation framework JAMES II. The implemented selection mechanisms are able to cope with three situations: a) no prior knowledge is available, b) the impact of problem features on performance is unknown, and c) a relationship between problem features and algorithm performance can be established empirically.An experimental evaluation of the developed methods concludes the thesis. It is shown that an automated algorithm selection may significantly increase the overall performance of a simulation system. Some of the presented mechanisms also support the research on simulation methods, as they facilitate their development and evaluation. ZusammenfassungDie Auswahl des passendsten Simulationsalgorithmus für eine bestimmte Aufgabe ist oftmals schwierig. Dies liegt an der komplexen Interaktion zwischen Modelleigenschaften, Implementierungsdetails und der Laufzeitumgebung, welche die Gesamtleistung stark beeinflussen kann. Eine automatisierte Auswahl von Simulationsalgorithmen unterstützt die Anwender eines Simulationssystems, indem sie eine geeignete Konfiguration des Systems ohne zusätzliches Fachwissen aus dem Bereich Simulation ermöglicht.Der erste Teil der Arbeit befasst sich eingehend mit Vorarbeiten zur automatischen Algorithmenauswahl, sowie mit der Leistungsanalyse von Simulationsalgorithmen. Eine einheitliche Kategorisierung wird erarbeitet um die aus den verschiedensten Fachgebieten (z.B. Finanzwesen, Künstliche Intelligenz) stammenden Ansätze zur Algorithmenauswahl einzuordnen.Der zweite Teil der Arbeit stellt ein Rahmenwerk zur automatischen Auswahl von Simulationsalgorithmen vor und beschreibt dessen Bestandteile, einschließlich ihrer Integration ins Modellierungsund Simulationsrahmenwerk JAMES II. Die realisierten Auswahlmechanismen beziehen sich dabei auf drei Situationen: a) keinerlei Vorwissen ist vorhanden, b) der Einfluss von Problemeigenschaften auf die Leistung ist unbekannt, und c) ein Zusammenhang zwischen Problemeigenschaften und Algorithmenleistung konnte empirisch ermittelt werden.Eine experimentelle Untersuchung der entwickelten Methoden schließt die Arbeit ab. Es wird gezeigt, dass eine aut...

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From Wetware to Software: A Cybernetic Perspective of Self-adaptive Software

Cited by 11 publications

References 15 publications

Autonomic system design based on the integrated use of SSM and VSM

Autonomic system design based on the integrated use of SSM and VSM

Increase Software Dependability via AOP-Based Runtime Adaptation

Automatic Algorithm Selection for Complex Simulation Problems

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