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A neural-network-based approach for diagnosing hardware faults in cloud systems:

机译：基于神经网络的云系统硬件故障诊断方法：

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In this article, we propose a novel scheme for diagnosing intermittent faults for cloud systems. We have investigated the characteristic of high-level symptomatic behavior on top of a cloud system and identified that (1) arrival counts of high-level symptoms go up with the number of fault injections at different speeds, which may help us to differentiate one fault model from another; (2) the nested level of fatal traps is found to be an indicative of fault duration, which is helpful for fault model diagnosis; (3) fatal traps triggered by certain faulty units is explored, providing useful information for locating faults. Based on these features, an n-dimensional space taking symptom’s arrival rate (grown up skew of the arrival count) as each dimension, which formulates the diagnosis problem as a pattern recognition problem is defined. Then, a backpropagation neural-network-based online hardware fault diagnosis scheme is proposed. Experimental results show that diagnosis accuracy of fault location is 99.2%,...
机译：在本文中，我们提出了一种用于诊断云系统间歇性故障的新颖方案。我们研究了云系统顶部的高级症状行为的特征，并确定了（1）高级症状的到达次数与故障注入速度不同时的数量有关，这可能有助于我们区分一个故障从另一个模型（2）致命陷阱的嵌套水平被发现是故障持续时间的指示，这有助于故障模型的诊断; （3）探索了由某些故障单元触发的致命陷阱，为定位故障提供了有用的信息。基于这些特征，定义了一个以症状的到达率（到达计数的增长偏斜）为每个维度的n维空间，该空间将诊断问题表达为模式识别问题。然后，提出了一种基于反向传播神经网络的在线硬件故障诊断方案。实验结果表明，故障定位的诊断准确率为99.2％。

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