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首页> 外文期刊>Simulation modelling practice and theory: International journal of the Federation of European Simulation Societies >An empirical study of modeling self-management capabilities in autonomic systems using case-based reasoning
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An empirical study of modeling self-management capabilities in autonomic systems using case-based reasoning

机译：基于案例推理的自主系统建模自我管理能力的实证研究

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Autonomic systems promise to inject self-managing capabilities in software systems. The major objectives of autonomic computing are to minimize human intervention and to enable a seamless self-adaptive behavior in the software systems. To achieve self-managing behavior, various methods have been exploited in past. Case-based reasoning (CBR) is a problem solving paradigm of artificial intelligence which exploits past experience, stored in the form of problem-solution pairs. We have applied CBR based modeling approach to achieve autonomicity in software systems. The proposed algorithms have been described and CBR implementation on externalization and internalization architectures of autonomic systems using two case studies RUBiS and Autonomic Forest Fire Application (AFFA) have been shown. The study highlights the effect of 10 different similarity measures, the role of adaptation and the effect of changing nearest neighborhood cardinality for a CBR solution cycle in autonomic managers. The results presented in this paper show that the proposed CBR based autonomic model exhibits 90-98% accuracy in diagnosing the problem and planning the solution.
机译：自主系统有望在软件系统中注入自我管理功能。自主计算的主要目标是最大程度地减少人为干预，并在软件系统中实现无缝的自适应行为。为了实现自我管理行为，过去已经利用了各种方法。基于案例的推理（CBR）是一种人工智能的问题解决范式，它利用过去的经验以问题-解决方案对的形式存储。我们已应用基于CBR的建模方法来实现软件系统的自主性。描述了所提出的算法，并使用两个案例研究RUBiS和自主森林火灾应用程序（AFFA）在自主系统的外部化和内部化体系结构上实现了CBR实现。该研究强调了自主管理者中CBR解决方案周期的10种不同相似性度量的效果，适应的作用以及改变最近邻域基数的影响。本文提出的结果表明，所提出的基于CBR的自主模型在诊断问题和计划解决方案方面显示出90-98％的准确性。

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