公开(公告)号：US20150248319A1

公开(公告)日：2015-09-03

申请号：US14679108

申请日：2015-04-06

申请人： Hitachi, Ltd.

发明人： Takayuki Nagai ,  Masataka Nagura ,  Kiminori Sugauchi ,  Takaki Kuroda

IPC分类号： G06F11/07

CPC分类号： H04L41/0213 ,  G05B23/02 ,  G06F11/0709 ,  G06F11/079 ,  G06N5/025

摘要： In analyzing an elaborate computer system which requires large-scale or numerous event propagation models, a law-of-causality matrix gains size, so that significant amount of storage resources has been used in a management computer. To solve such a problem, the management computer to manage the computer system stores topologies, event propagation models, and causality information including one or more causal relations in the storage resources, determines, when the management computer analyzes or detects an event, whether a causal relation has already been created for the event to be analyzed, and creates the causal relation based on a topology and event propagation models, if not yet.

摘要翻译： 在分析需要大规模或多个事件传播模型的复杂计算机系统时，因果关系矩阵获取大小，从而在管理计算机中使用了大量的存储资源。 为了解决这样的问题，管理计算机管理计算机系统的存储拓扑，事件传播模型和包括存储资源中的一个或多个因果关系的因果关系信息在管理计算机分析或检测事件时确定是否因果 已经为要分析的事件创建了关系，并且基于拓扑和事件传播模型创建因果关系（如果尚未）。

公开(公告)号：US09294338B2

公开(公告)日：2016-03-22

申请号：US14679108

申请日：2015-04-06

申请人： Hitachi, Ltd.

发明人： Takayuki Nagai ,  Masataka Nagura ,  Kiminori Sugauchi ,  Takaki Kuroda

IPC分类号： G06F15/173 ,  H04L12/24 ,  G05B23/02 ,  G06N5/02 ,  G06F11/07

CPC分类号： H04L41/0213 ,  G05B23/02 ,  G06F11/0709 ,  G06F11/079 ,  G06N5/025

摘要： In analyzing an elaborate computer system which requires large-scale or numerous event propagation models, a law-of-causality matrix gains size, so that significant amount of storage resources has been used in a management computer. To solve such a problem, the management computer to manage the computer system stores topologies, event propagation models, and causality information including one or more causal relations in the storage resources, determines, when the management computer analyzes or detects an event, whether a causal relation has already been created for the event to be analyzed, and creates the causal relation based on a topology and event propagation models, if not yet.