Monitoring data analyzing apparatus, monitoring data analyzing method, and monitoring data analyzing program

Patent metadata

Field	Value
Publication number	US-9111227-B2
Application number	US-201113824601-A
Country	US
Kind code	B2
Filing date	Dec 12, 2011
Priority date	Dec 24, 2010
Publication date	Aug 18, 2015
Grant date	Aug 18, 2015

Abstract

An object of the present invention is to reduce a prediction error even if a monitoring target system has a plurality of patterns of use. A monitoring data analyzing apparatus includes a log data file 21 configured to accumulate log data including monitoring data in a monitoring target system set as a target of performance management, a data classifying section 11 configured to classify the log data into a plurality of groups on the basis of characteristics of use status data included in the log data and indicating statuses of use of components of the monitoring target system, and a regression-model generating section 12 configured to execute a regression analysis of the log data and generate a regression model for each of the groups.

First claim

I claim:

1. A monitoring data analyzing apparatus comprising:
a data accumulating section configured to accumulate log data including monitoring data in a monitoring target system set as a target of performance management;
a data classifying section configured to classify the log data into a plurality of groups on the basis of characteristics of use status data included in the log data and indicating statuses of use of components of the monitoring target system; and
a regression-model generating section configured to execute a regression analysis of the log data and generate a regression model for each of the groups.

2. The monitoring data analyzing apparatus according to claim 1 , further comprising a characteristic-data-item extracting section configured to extract, out of data items included in the log data, the data items related to an explanatory variable of the regression model as characteristic data items.

3. The monitoring data analyzing apparatus according to claim 2 , wherein the characteristic-data extracting section calculates, for each of the groups, dependencies on the explanatory variable in the data items of the data forming the group, and extracts data items having the calculated dependencies higher than a predetermined threshold as the characteristic data items.

4. The monitoring data analyzing apparatus according to claim 2 , wherein the characteristic-data extracting section calculates, for each of the groups, correlation coefficients between the data items of the data forming the group and the explanatory variable, and extracts data items having average values of the calculated correlation coefficients larger than a predetermined threshold as the characteristic data items.

5. The monitoring data analyzing apparatus according to claim 1 , further comprising a performance-value calculating section configured to calculate, using the regression model generated by the regression-model generating section, a performance value, which is an objective variable of the regression model.

6. The monitoring data analyzing apparatus according to claim 1 , further comprising an abnormality determining section configured to determine presence or absence of an abnormality in the monitoring target system on the basis of a difference between a value obtained by substituting a value of a data item corresponding to an explanatory variable of the regression model included in a test target log data, which is the log data set as a target of the performance test, in the regression model generated by the regression-model generating section, and a value of a data item corresponding to an objective variable of the regression model included in the test target log data.

7. The monitoring data analyzing apparatus according to claim 2 , further comprising a regression-model recalculating section configured to calculate, for each of the groups, ratios of values of the characteristic data items with respect to the explanatory variable and combine the regression models of the groups using the calculated ratios to calculate regression models per characteristic data item, which are the regression models concerning the characteristic data items, and combine the calculated regression models per characteristic data item according to appearance ratios of values of the characteristic data items included in the test target log data, which is the log data set as a target of the performance test, to recalculate the regression models.

8. The monitoring data analyzing apparatus according to claim 7 , further comprising a performance-value calculating section configured to calculate a performance value, which is an objective variable of the regression model, by using the regression model recalculated by the regression-model recalculating section.

9. The monitoring data analyzing apparatus according to claim 7 , further comprising an abnormality determining section configured to determine presence or absence of an abnormality in the monitoring target system on the basis of a difference between a value obtained by substituting a value of a data item corresponding to an explanatory variable of the regression model included in the test target log data in the regression model recalculated by the regression-model recalculating section, and a value of a data item corresponding to an objective variable of the regression model included in the test target log data.

10. A monitoring data analyzing method comprising the steps of:
accumulating log data including monitoring data in a monitoring target system set as a target of performance management;
classifying the log data into a plurality of groups on the basis of characteristics of use status data included in the log data and indicating statuses of use of components of the monitoring target system; and
executing a regression analysis of the log data and generating a regression model for each of the groups.

11. A monitoring data analyzing program for causing a computer to execute the steps described in claim 10 .

Assignees

• Tonouchi Toshio
• Nec Corp

Inventors

• Tonouchi Toshio

Classifications

• G06N20/00 Primary

  Machine learning · CPC title

• G06F11/3485 Primary

  for I/O devices · CPC title

• G06F11/3409

  for performance assessment · CPC title

• G06F2201/81

  Threshold · CPC title

• G06F2201/88

  Monitoring involving counting · CPC title