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首页> 外文期刊>Parallel and Distributed Systems, IEEE Transactions on >Adaptive Algorithms for Diagnosing Large-Scale Failures in Computer Networks
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Adaptive Algorithms for Diagnosing Large-Scale Failures in Computer Networks

机译：诊断计算机网络中大规模故障的自适应算法

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摘要

We propose a greedy algorithm, Cluster-MAX-COVERAGE (CMC), to efficiently diagnose large-scale clustered failures. We primarily address the challenge of determining faults with incomplete symptoms. CMC makes novel use of both positive and negative symptoms to output a hypothesis list with a low number of false negatives and false positives quickly. CMC requires reports from about half as many nodes as other existing algorithms to determine failures with 100 percent accuracy. Moreover, CMC accomplishes this gain significantly faster (sometimes by two orders of magnitude) than an algorithm that matches its accuracy. When there are fewer positive and negative symptoms at a reporting node, CMC performs much better than existing algorithms. We also propose an adaptive algorithm called Adaptive-MAX-COVERAGE (AMC) that performs efficiently during both independent and clustered failures. During a series of failures that include both independent and clustered, AMC results in a reduced number of false negatives and false positives.
机译：我们提出一种贪心算法，即Cluster-MAX-COVERAGE（CMC），以有效地诊断大规模的群集故障。我们主要解决确定具有不完整症状的故障的挑战。 CMC新颖地使用了阳性和阴性症状，可以快速输出假阴性和假阳性数量少的假设列表。 CMC要求来自其他现有算法的大约一半节点的报告才能以100％的准确性确定故障。此外，CMC比匹配其精度的算法要快得多（有时两个数量级）来实现此增益。当在报告节点上出现的阳性和阴性症状较少时，CMC的性能将比现有算法好得多。我们还提出了一种称为Adaptive-MAX-COVERAGE（AMC）的自适应算法，该算法在独立故障和群集故障期间均能高效执行。在包括独立故障和群集故障的一系列故障中，AMC导致误报和误报数量减少。

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