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公开(公告)号：US08880933B2
公开(公告)日：2014-11-04
申请号：US13080393
申请日：2011-04-05
CPC分类号： G06N5/025 , G06F11/079 , G06F11/1658 , H04L41/0636 , H04L43/04
摘要： The problem signature extraction technique extracts problem signatures from trace data collected from an application. The technique condenses the manifestation of a network, software or hardware problem into a compact signature, which could then be used to identify instances of the same problem in other trace data. For a network configuration, the technique uses as input a network-level packet trace of an application's communication and extracts from it a set of features. During the training phase, each application run is manually labeled as GOOD or BAD, depending on whether the run was successful or not. The technique then employs a learning technique to build a classification tree not only to distinguish between GOOD and BAD runs but to also sub-classify the BAD runs into different classes of failures. Once a classification tree has been learned, problem signatures are extracted by walking the tree, from the root to each leaf.
摘要翻译： 问题签名提取技术从应用程序收集的跟踪数据中提取问题签名。 该技术将网络，软件或硬件问题的表现集中到紧凑签名中，然后可以将其用于识别其他跟踪数据中相同问题的实例。 对于网络配置，该技术用作输入应用程序通信的网络级数据包跟踪，并从中提取一组特征。 在培训阶段，每个应用程序运行都会手动标记为GOOD或BAD，具体取决于运行是否成功。 然后，该技术采用学习技术来构建分类树，不仅可以区分GOOD和BAD运行，而且还将BAD运行次分类到不同类别的故障中。 一旦学习了分类树，就可以通过将树从根移到每个叶来提取问题签名。
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公开(公告)号：US20120260141A1
公开(公告)日：2012-10-11
申请号：US13080393
申请日：2011-04-05
IPC分类号： G06F11/34
CPC分类号： G06N5/025 , G06F11/079 , G06F11/1658 , H04L41/0636 , H04L43/04
摘要： The problem signature extraction technique extracts problem signatures from trace data collected from an application. The technique condenses the manifestation of a network, software or hardware problem into a compact signature, which could then be used to identify instances of the same problem in other trace data. For a network configuration, the technique uses as input a network-level packet trace of an application's communication and extracts from it a set of features. During the training phase, each application run is manually labeled as GOOD or BAD, depending on whether the run was successful or not. The technique then employs a learning technique to build a classification tree not only to distinguish between GOOD and BAD runs but to also sub-classify the BAD runs into different classes of failures. Once a classification tree has been learned, problem signatures are extracted by walking the tree, from the root to each leaf.
摘要翻译： 问题签名提取技术从应用程序收集的跟踪数据中提取问题签名。 该技术将网络，软件或硬件问题的表现集中到紧凑签名中，然后可以将其用于识别其他跟踪数据中相同问题的实例。 对于网络配置，该技术用作输入应用程序通信的网络级数据包跟踪，并从中提取一组特征。 在培训阶段，每个应用程序运行都会手动标记为GOOD或BAD，具体取决于运行是否成功。 然后，该技术采用学习技术来构建分类树，不仅可以区分GOOD和BAD运行，而且还将BAD运行次分类到不同类别的故障中。 一旦学习了分类树，就可以通过将树从根移到每个叶来提取问题签名。
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