Conference Paper

Distributed Software Maintenance Using an Autonomic System Management Approach based on the Viable System Model.

DOI: 10.1109/ICAS.2006.22 Conference: 2006 International Conference on Autonomic and Autonomous Systems (ICAS 2006), 16-21 July 2006, Silicon Valley, California, USA
Source: DBLP


Most current softwae management solutions are missing a systematic and holistic approach for global system management, and apply only to specijic system components. This limits the system k extensibility, and does not meet interoperability requirements jbm the growing heterogeneity of the operating environment. This paper discusses an alternative approach to common management of evolving distributed software, impired by the research in the field of open systems and cybernetic models. The approach involves adapting Stanford Beer's Yiable System Model (KSly, to fhe concrete needs of distributed sofiware, supporting evolutionary integration of new functionality, while preserving system stability. We introduce meta-data bindings to existing information models as a basis for management of hierarchical and recursive sofiware elements, and an architecture for composition of interacting components with verijication of their capabilities during deployment and runtime. The architecture is designed to satis& criteria of the Eable System Model and to assure conditions necessary for autonomic behavior.

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    ABSTRACT: —This paper proposes an alternative approach to software management in heterogeneous environments. It targets encapsulation and dependency management of component systems using Stanford Beer’s Viable System Model (VSM) as requirements and organizational model. VSM is expressed with meanings of Common Information Model (CIM) extensions that serve as basis for an object-oriented representation of managed components. A control-loop architecture is proposed to facilitate monitoring of heterogeneous component environments using the developed model.
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    ABSTRACT: Presented is research articulating a novel technology progressing resource management within self-organizing systems. Examining both Cybernetic and Autonomic Computing techniques we evolve a set-theory oriented, atomically-derived, emergent model that reflects an algorithmic decomposition of Beer's recursive, multi-agent Viable System Model, pertinent by its composition of multiple and independent entities, sharing one or more objectives. Integrated management promotes each sub-system as a whole within a closed ecological meta-boundary. The relationships between sub-systems is demonstrated via syntax subscripts, while the relationship linking recursive levels is recognized via superscripts. The resultant design grammar endorses autonomy versus governance, exploiting cybernetic, biological and mathematical metaphors, crucially seeking inherent learning and control through system-environment interplay. Focusing on interactions and inter-relationships, the self-organizing environments exhibit evolution of systemic elements, conserving yet managing resources provided by each entity. Research ultimately aspires augm entation of the Autonomic Computing state of the art into the original field of Viable Computing Systems.
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    ABSTRACT: This paper presents a case study considering algorithmic hot swapping in the context of research surpassing Autonomic Computing, towards Viable Computing Systems. Cybernetic, mathematical and biological metaphors are allied to the human autonomic agent capability of the managerial cybernetics underscoring Beer's Viable System Model (VSM). A dual-perspective set theory design grammar model is employed exhibiting relationships between the systems and the recursive levels of the VSM. By incorporating the environment as part of the system, the technique promotes both portability and viability within an initially closed, yet changing, environment. Algorithmic hot swapping has been used to provide a repertoire of tailored responses to environmental change within this context. Systemic emergence and viability is thereby promoted, whilst an associated Learning Classifier System (LCS) is suggested to allow the system to develop an adaptive environmental model of appropriate, optimized responses, similarly demonstrating proof of the temporal and autonomic properties of the VCS concept.
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