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首页> 外文期刊>IEEE transactions on dependable and secure computing >netCSI: A Generic Fault Diagnosis Algorithm for Large-Scale Failures in Computer Networks
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netCSI: A Generic Fault Diagnosis Algorithm for Large-Scale Failures in Computer Networks

机译：netCSI：计算机网络中大规模故障的通用故障诊断算法

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摘要

We present a framework and a set of algorithms for determining faults in networks when large scale outages occur. The design principles of our algorithm, netCSI, are motivated by the fact that failures are geographically clustered in such cases. We address the challenge of determining faults with incomplete symptom information due to a limited number of reporting nodes. netCSI consists of two parts: a hypotheses generation algorithm, and a ranking algorithm. When constructing the hypothesis list of potential causes, we make novel use of positive and negative symptoms to improve the precision of the results. In addition, we propose and along with a , to reduce the complexity of our algorithm. The ranking algorithm is based on conditional failure probability models that account for the geographic correlation of the network objects in clustered failures. We evaluate the performance of netCSI for networks with both random and realistic topologies. We compare the performance of netCSI with an existing fault diagnosis algorithm, MAX-COVERAGE, and demonstrate an average gain of 128 percent in accuracy for realistic topologies.
机译：我们提出了一种框架和一组算法，用于在发生大规模中断时确定网络中的故障。我们的算法netCSI的设计原理是基于在这种情况下故障在地理位置上聚集的事实。由于报告节点数量有限，我们解决了用不完整的症状信息确定故障的挑战。 netCSI由两部分组成：假设生成算法和排名算法。在构建潜在原因的假设列表时，我们新颖地使用了阳性和阴性症状以提高结果的准确性。另外，我们建议与一起使用，以降低算法的复杂度。排序算法基于条件故障概率模型，该模型考虑了群集故障中网络对象的地理相关性。我们评估具有随机拓扑和实际拓扑的网络的netCSI性能。我们将netCSI的性能与现有的故障诊断算法MAX-COVERAGE进行了比较，并证明了实际拓扑的平均精度提高了128％。

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