Methods and systems for reducing metrics used to monitor resources

• US 9,317,387 B2
• Filed: 06/11/2013
• Issued: 04/19/2016
• Est. Priority Date: 06/11/2013
• Status: Active Grant

• Current Assignee: Vmware LLC (Broadcom, Inc.)
• Original Assignee: VMware, Inc. (Broadcom, Inc.)
• Inventors: Kariv, Asaf
• Primary Examiner(s): HA, DAC V
• Application Number:
  US13/914,873
• Publication Number:
  US 20140365637A1
• Time in Patent Office: 1,043 Days
• US Class Current: 1/1
• CPC Class Codes: G06F 11/3003 Monitoring arrangements specially adapted to the computing system or computing system component being monitored
• CPC Class Codes: G06F 11/3093 Configuration details thereof, e.g. installation, enabling, spatial arrangement of the probes
• CPC Class Codes: G06F 11/3096 wherein the means or processing minimize the use of computing system or of computing system component resources, e.g. non-intrusive monitoring which minimizes the probe effect: sniffing, intercepting, indirectly deriving the monitored data from other directly available data
• CPC Class Codes: G06F 11/3409 for performance assessment

Abstract

Computational methods and systems for identifying a subset of a set of metrics that can be used to monitor a resource are described. The subset is representative of the information provided by the full set of metrics. Correlations are calculated for each pair of metrics and metrics with the highest correlations to other metrics in the set of metrics are deleted to obtain the representative subset of metrics. Deletion of metrics from the set of metrics may be optimized for accuracy or cost. The smaller representative subset of metrics can then be used to monitor the resource.

Claims

1. A data-processing system comprising:

one or more processors;

one or more computer-readable media; and

a routine stored in the computer-readable media that when executed on the one or more processors,

receive a set of metrics from a resource, each metric represents a measurable component of the resource;

calculates a set of correlation magnitudes, each correlation magnitude calculated for a pair of metrics in the set of metrics stored in the one or more computer-readable media;

identifies a largest correlation magnitude in the set of correlation magnitudes stored in the one or more computer-readable media;

identifies a first metric and a second metric associated with the largest correlation magnitude;

deletes one of the first and second metric from the set of metrics based on which of the first and second metrics has larger correlation magnitudes with other metrics in the set of metrics; and

deletes the correlation magnitudes associated with which of the first and second metrics has the largest correlation magnitudes from the set of correlation magnitudes.

2. The system of claim 1 wherein deletes the first or second metric from the set of metrics further comprises

calculates a first sum of correlation magnitudes associated with the first metric;

calculates a second sum of correlation magnitudes associated with the second metric;

when the first sum is larger than the second sum, deletes the first metric from the set of metrics; and

when the second sum is larger than the first sum, deletes the second metric from the set of metrics.

3. The system of claim 1 wherein deletes the first or second metric from the set of metrics further comprises

calculates a first average of correlation magnitudes associated with the first metric;

calculates a second average of correlation magnitudes associated with the second metric;

when the first average is larger than the second average, deletes the first metric from the set of metrics; and

when the second average is larger than the first average, deletes the second metric from the set of metrics.

4. The system of claim 1 further comprises:

receives metric data values associated with a resource, each metric data value corresponds to a metric in a set of metrics stored in the computer-readable medium;

calculates the set of correlation magnitudes, each correlation magnitude associated with a pair of metrics in the set of metrics; and

stores the correlation magnitudes in the one or more computer-readable media.

5. The system of claim 4 wherein calculates the set of correlation magnitudes, each correlation magnitude associated with a pair of metrics in the set of metrics farther comprises:

for each pair of metrics in the set of metrics,

calculates a correlation between a pair metrics based on the metric values associated with the pair of metrics;

calculates an absolute value of each correlation to give a correlation magnitude associated with the pair of metrics; and

stores a relation in the computer-readable medium that identifies the pair of metrics associated with the correlation magnitude.

6. The system of claim 1 further comprises repeating identifies the largest correlation magnitude, deletes the first or second metric from the set of metrics, and deletes the correlation magnitudes associated with which of the first or second metrics has the largest correlation magnitudes until the set of metrics is composed of a user-defined number of metrics.

7. The system of claim 1 further comprises repeating identifies the largest correlation magnitude, deletes the first or second metric from the set of metrics, and deletes the correlation magnitudes associated with which of the first or second metrics has the largest correlation magnitudes until the set of metrics reaches a user-defined minimum accuracy.

8. A method carried out within a computer system having one or more processors and an electronic memory that analyzes digitally encoded data stored in one or more computer-readable media, the method comprising:

receiving a set of metrics from a resource, each metric represents a measurable component of the resource;

calculating a set of correlation magnitudes, each correlation magnitude calculated for a pair of metrics in the set of metrics stored in the one or more computer-readable media;

identifying a largest correlation magnitude in the set of correlation magnitudes stored in the one or more computer-readable media;

identifying a first metric and a second metric associated with the largest correlation magnitude;

deleting one of the first and second metric from the set of metrics, based on which of the first and second metrics has larger correlation magnitudes with other metrics in the set of metrics; and

deleting the correlation magnitudes associated with which of the first or second metrics has the largest correlation magnitudes from the set of correlation magnitudes.

9. The method of claim 8 wherein deleting the first or second metric from the set of metrics further comprises

calculating a first sum of correlation magnitudes associated with the first metric;

calculating a second sum of correlation magnitudes associated with the second metric;

when the first sum is larger than the second sum, deleting the first metric from the set of metrics; and

when the second sum is larger than the first sum, deleting the second metric from the set of metrics.

10. The method of claim 8 wherein deleting the first or second metric from the set of metrics further comprises

calculating a first average of correlation magnitudes associated with the first metric;

calculating a second average of correlation magnitudes associated with the second metric;

when the first average is larger than the second average, deleting the first metric from the set of metrics; and

when the second average is larger than the first average, deleting the second metric from the set of metrics.

11. The method of claim 8 further comprises:

receiving metric data values associated with a resource, each metric data value corresponds to a metric in a set of metrics stored in the computer-readable medium;

calculating the set of correlation magnitudes, each correlation magnitude associated with a pair of metrics in the set of metrics; and

storing the correlation magnitudes in the one or more computer-readable media.

12. The method of claim 11 wherein calculating the set of correlation magnitudes, each correlation magnitude associated with a pair of metrics in the set of metrics further comprises:

for each pair of metrics in the set of metrics,

calculating a correlation between a pair metrics based on the metric values associated with the pair of metrics;

calculating an absolute value of each correlation to give a correlation magnitude associated with the pair of metrics; and

storing a relation in the computer-readable medium that identifies the pair of metrics associated with the correlation magnitude.

13. The method of claim 8 further comprises repeatedly identifying the largest correlation magnitude, deleting the first or second metric from the set of metrics, and deleting the correlation magnitudes associated with which of the first or second metrics has the largest correlation magnitudes until the set of metrics is composed of a user-defined number of metrics.

14. The method of claim 8 further comprises repeatedly identifying the largest correlation magnitude, deleting the first or second metric from the set of metrics, and deleting the correlation magnitudes associated with which of the first or second metrics has the largest correlation magnitudes until the set of metrics reaches a user-defined minimum accuracy.

15. A non-transitory computer-readable medium encoded with machine-readable instructions that implement a method carried out by one or more processors of a computer system to perform the operations of

receiving a set of metrics from a resource, each metric represents a measurable component of the resource;

calculating a set of correlation magnitudes, each correlation magnitude calculated for a pair of metrics in the set of metrics stored in the one or more computer-readable media;

identifying a largest correlation magnitude in the set of correlation magnitudes stored in the one or more computer-readable media;

identifying a first metric and a second metric associated with the largest correlation magnitude;

deleting one of the first and second metric from the set of metrics, based on which of the first and second metrics has larger correlation magnitudes with other metrics in the set of metrics; and

deleting the correlation magnitudes associated with which of the first and second metrics has the largest correlation magnitudes from the set of correlation magnitudes.

16. The method of claim 15 wherein deleting the first or second metric from the set of metrics further comprises

calculating a first sum of correlation magnitudes associated with the first metric;

calculating a second sum of correlation magnitudes associated with the second metric;

when the first sum is larger than the second sum, deleting the first metric from the set of metrics; and

when the second sum is larger than the first sum, deleting the second metric from the set of metrics.

17. The medium of claim 15 wherein deleting the first or second metric from the set of metrics further comprises

calculating a first average of correlation magnitudes associated with the first metric;

calculating a second average of correlation magnitudes associated with the second metric;

when the first average is larger than the second average, deleting the first metric from the set of metrics; and

when the second average is larger than the first average, deleting the second metric from the set of metrics.

18. The medium of claim 15 further comprises:

receiving metric data values associated with a resource, each metric data value corresponds to a metric in a set of metrics stored in the computer-readable medium;

calculating the set of correlation magnitudes, each correlation magnitude associated with a pair of metrics in the set of metrics; and

storing the correlation magnitudes in the one or more computer-readable media.

19. The medium of claim 18 wherein calculating the set of correlation magnitudes, each correlation magnitude associated with a pair of metrics in the set of metrics further comprises:

for each pair of metrics in the set of metrics,

calculating a correlation between a pair metrics based on the metric values associated with the pair of metrics;

calculating an absolute value of each correlation to give a correlation magnitude associated with the pair of metrics; and

storing a relation in the computer-readable medium that identifies the pair of metrics associated with the correlation magnitude.

20. The medium of claim 15 further comprises repeatedly identifying the largest correlation magnitude, deleting the first or second metric from the set of metrics, and deleting the correlation magnitudes associated with which of the first or second metrics has the largest correlation magnitudes until the set of metrics is composed of a user-defined number of metrics.

21. The medium of claim 15 further comprises repeatedly identifying the largest correlation magnitude, deleting the first or second metric from the set of metrics, and deleting the correlation magnitudes associated with which of the first or second metrics has the largest correlation magnitudes until the set of metrics reaches a user-defined minimum accuracy.