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

• US 9,465,713 B2
• Filed: 12/12/2011
• Issued: 10/11/2016
• Est. Priority Date: 12/24/2010
• Status: Active Grant

• Current Assignee: NEC Asia Pacific Pte Limited (NEC Corporation)
• Original Assignee: NEC Corporation
• Inventors: Tonouchi, Toshio
• Primary Examiner(s): Craig, Dwin M
• Assistant Examiner(s): OCHOA, JUAN CARLOS
• Application Number:
  US13/824,539
• Publication Number:
  US 20130191107A1
• Time in Patent Office: 1,765 Days
• US Class Current: 1/1
• CPC Class Codes: G06F 11/3409 for performance assessment
• CPC Class Codes: G06F 11/3447 Performance evaluation by modeling
• CPC Class Codes: G06F 11/3452 Performance evaluation by statistical analysis
• CPC Class Codes: G06F 11/3476 Data logging G06F11/14, G06F11/2205 take precedence
• CPC Class Codes: G06F 11/3485 for I/O devices
• CPC Class Codes: G06F 2201/81 Threshold
• CPC Class Codes: G06F 2201/88 Monitoring involving counting

Abstract

An object of the present invention is to improve prediction accuracy for a performance value of a monitoring target system even if the performance value is substantially different for each of patterns of use. A monitoring data analyzing apparatus includes a regression-model recalculating section 14 configured to combine regression models, which are generated for each of a plurality of groups into which log data including monitoring data in a monitoring target system set as a target of performance management is classified, using the log data belonging to the groups corresponding to the regression models and test target log data, which is the log data set as a target of a performance test, to recalculate the regression models.

Claims

1. A monitoring data analyzing apparatus comprising:

a processor; and

memory for storing processor executable instructions that perform a step described in the following sections;

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;

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;

a regression-model recalculating section configured to combine regression models, which are generated for each of the groups into which the log data is classified, using the log data belonging to the groups corresponding to the regression models and test target log data, which is the log data set as a target of a performance test, to recalculate the regression models;

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 models as characteristic data items; and

a performance-value calculating section configured to calculate a performance value, which is an objective variable of the regression models, by using the regression models recalculated by the regression-model recalculating section,

wherein the regression-model recalculating section calculates, for each of the groups, ratios of values of the characteristic data items with respect to the explanatory variable and combines 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 combines 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 to recalculate the regression models; and

performs the performance test based on at least the recalculated regression models.

2. The monitoring data analyzing apparatus according to claim 1, 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.

3. The monitoring data analyzing apparatus according to claim 1, 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.

4. 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 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 models included in the test target log data.

5. A monitoring data analyzing method comprising the step of:

storing executable instructions that perform a step described in the following sections;

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;

executing a regression analysis of the log data and generating a regression model for each of the groups;

combining regression models, which are generated for each of the groups into which the log data is classified, using the log data belonging to the groups corresponding to the regression models and test target log data, which is the log data set as a target of a performance test, to recalculate the regression models;

extracting out of data items included in the log data, the data items related to an explanatory variable of the regression models as characteristic data items;

calculating, a performance value, which is an objective variable of the regression models, by using the recalculated regression models; and

calculating for each of the groups, ratios of values of the characteristic data items with respect to the explanatory variable and combining 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 combining 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 to recalculate the regression models; and

performing the performance test based on at least the recalculated regression models.

6. A non-transitory computer readable medium storing a monitoring data analyzing program for causing a computer to execute the step described in claim 5.