SYSTEM AND METHOD FOR EXTRACTING AND FORECASTING COMPUTING RESOURCE DATA SUCH AS CPU CONSUMPTION USING AUTOREGRESSIVE METHODOLOGY

• US 20010013008A1
• Filed: 02/27/1998
• Published: 08/09/2001
• Est. Priority Date: 02/27/1998
• Status: Active Grant

• Current Assignee: Verizon Patent and Licensing Incorporated (Verizon Communications Inc.)
• Original Assignee: MCI Communications Corporation (Verizon Communications Inc.)
• Inventors: WACLAWSKI, ANTHONY C.
• Granted Patent: US 6,574,587 B2
• Time in Patent Office: Days
• US Class Current: 705/10
• CPC Class Codes: G06F 11/3409 for performance assessment
• CPC Class Codes: G06F 11/3423 where the assessed time is active or idle time
• 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 2201/835 Timestamp
• CPC Class Codes: G06F 2201/875 Monitoring of systems including the internet

Abstract

A system and method for extracting and forecasting computing resource data such as workload consumption of mainframe computing resources using an autoregressive model. The system and method forecast mainframe central processing unit (CPU) consumption with ninety-five percent accuracy using historical performance data. The system and method also provide an upper ninety-five percent confidence level and a lower ninety-five percent confidence level. The system and method retrieve performance records from a computer platform in one second intervals, statistically collapses the one second performance data into fifteen minute performance data, statistically collapses the fifteen minute performance data into one week performance data, and generates a time series equivalent to collecting performance data at one week intervals. The system and method ensure that the resulting time series is statistically stationary, and applies an autoregressive construct to the time series to generate forecast of future CPU utilization, as well as to generate reports and graphs comparing actual vs. forecast CPU utilization. Because the system and method rely on electronically generated empirical historical computer performance data as an input, they provide a turnkey solution to CPU consumption forecasting that can be implemented easily by any system network manager.

Claims

1. In a computing platform having a plurality of resources, a method for forecasting at least one aspect of the plurality of resources, the method comprising the steps of:

collecting at intervals a metric from a computing platform resource; and

applying an autoregressive modeling tool to the collected metrics to produce a forecast of the metric of the plurality of resources.

2. The method according to

claim 1

, further comprising the step of statistically collapsing the collected metrics to produce a first time series.

3. The method according to

claim 1

, further comprising the step of outputting the forecast of the metric.

4. The method according to

claim 1

, further comprising the step of extracting the first time series from the database.

5. The method according to

claim 1

, further comprising the step of statistically collapsing the first time series to produce a second series.

6. The method according to

claim 1

, further comprising the step of ensuring that the first time series is statistically stationary.

7. A system to forecast performance of at least one computing platform resource, comprising:

a computing platform resource having at least one performance metric; and

an autoregressive modeling tool for receiving a representation of the performance metric and providing a forecast of the computing platform resource.

8. The system of

claim 7

, wherein the autoregressive modeling tool is an autoregressive moving average modeling tool.

9. The system of

claim 7

, wherein the autoregressive modeling tool is an autoregressive integrated moving average modeling tool.

10. The system of

claim 7

, wherein the performance metric represents utilization of at least one computing platform resource.

11. The system of

claim 7

, wherein the performance metric represents one of redundancy or efficiency of at least one computing platform resource.

12. The system of

claim 7

, wherein the performance metric represents utilization and the computing platform resource comprises a central processing unit.

13. The system of

claim 7

, wherein the performance metric represents users logged on to the Internet.

14. The system of

claim 7

, wherein the performance metric represents Internet traffic.

15. A system to forecast at least one aspect of a plurality of computing platform resources, comprising:

a resource manager;

a first statistical collapser coupled to the resource manager;

a time series analyzer coupled to the first statistical collapser; and

an autoregressive modeling tool coupled to the time series analyzer.

16. The system according to

claim 15

, further comprising a database coupled to the first statistical collapser.

17. The system according to

claim 15

, further comprising a data extractor coupled to the database.

18. The system according to

claim 15

further comprising a second statistical collapser coupled to the data extractor.

19. The system according to

claim 15

, further comprising a time point converter coupled to the time series analyzer.

20. The system according to

claim 15

, wherein the first statistical collapser comprises a fifteen minute time series generator.

21. The system according to

claim 15

, wherein the second statistical collapser comprises a weekly time series generator.

22. The system according to

claim 15

, wherein the time series analyzer comprises a time series differencer.

23. The system according to

claim 15

, wherein the autoregressive modeling tool comprises an autoregressive moving average modeling tool.

24. The system according to

claim 15

, wherein the autoregressive modeling tool comprises an autoregressive integrated moving average modeling tool.

25. In a computing platform having a plurality of resources, a method for forecasting at least one aspect of the plurality of resources, the method comprising the steps of:

statistically collapsing a metric of a computing platform resource; and

applying an autoregressive modeling tool to a series of the statistically collapsed metrics to produce a forecast of the metric.

26. The method according to

claim 25

, further comprising the step of collecting at intervals the metric from the computing platform resource.

27. The method according to

claim 25

, further comprising the step of storing the series in a database.

28. The method according to

claim 25

, further comprising the step of extracting the series from the database.

29. The method according to

claim 25

, further comprising the step of statistically collapsing the series to produce a set of points.

30. The method according to

claim 25

, further comprising the step of ensuring that the set of points is statistically stationary.

31. In a computing platform having a plurality of resources, a method for producing a series of data values of the computing platform resources, the method comprising the steps of:

receiving a series a metrics representing at least one computing platform resource;

calculating a set of mean or median values based on the series of metrics;

determining a set of mean or median values for each calculated set of values;

collating the determined values to produce a series of data values representing the series of metrics; and

storing the series of metrics in a database.

32. The method according to

claim 31

, further comprising the step of collapsing the stored series of metrics to produce a first time series.

33. The method according to

claim 31

, further comprising the step of collapsing the first time series to produce a second time series.

34. The method according to

claim 31

, further comprising the step of applying an autoregressive model to the second time series.