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A Smart Diagnostic Model for an Autonomic Service Bus Based on a Probabilistic Reasoning Approach

机译:基于概率推理方法的自主服务总线智能诊断模型

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摘要

The growing complexity and scale of systems implies challenges to include Autonomic Computing capabilities that help to maintain or improve the performance, availability and reliability characteristics. The autonomic management of a system can be defined deterministically based on experiment observations on the system and possible results of associated plans. However in dynamic environments with changing conditions and requirements, a better technique to diagnose observations and learn about the functioning conditions of the managed system is needed to guide the autonomic management. In the case of medical diagnostic, tests have included statistical and probabilistic models to aid and improve the results and select better medical treatments. In this paper we also adopt a probabilistic approach to define a Bayesian network from monitored data of an Enterprise Service Bus under different workload conditions. This model is used by the Autonomic Service Bus as a knowledge base to diagnose the cause of degradation problems and repair them. Experimental results assess the effectiveness of our approach.
机译:系统的复杂性和规模不断增长,带来了挑战,要包括自动计算功能,以帮助维持或改善性能,可用性和可靠性特征。可以基于对系统的实验观察以及相关计划的可能结果,确定性地定义系统的自主管理。但是,在条件和要求不断变化的动态环境中,需要一种更好的技术来诊断观察结果并了解受管系统的运行状况,以指导自主管理。就医学诊断而言,测试包括统计和概率模型,以帮助和改善结果并选择更好的治疗方法。在本文中,我们还采用一种概率方法,通过在不同工作负载条件下从企业服务总线的监视数据中定义贝叶斯网络。自主服务总线使用此模型作为知识库来诊断退化问题的原因并进行修复。实验结果评估了我们方法的有效性。

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