SYSTEM, METHOD AND PROGRAM PRODUCT FOR FORECASTING THE DEMAND ON COMPUTER RESOURCES

• US 20080281564A1
• Filed: 05/16/2008
• Published: 11/13/2008
• Est. Priority Date: 07/08/2004
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Suzuki, Yasuhiro, Yamamoto, Katsuyoshi, Yamamoto, Naoya
• Granted Patent: US 7,882,075 B2
• Time in Patent Office: Days
• US Class Current: 703/2
• CPC Class Codes: G06F 11/3409 for performance assessment
• 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 2201/87 Monitoring of transactions
• CPC Class Codes: G06F 2201/875 Monitoring of systems including the internet

Abstract

An automated system and method for predicting a future resource utilization tendency by analyzing a change in resource usage on the basis of a real operation condition in a computer system. A CPU utilization estimating unit acquires a resource log and a transaction log, and estimates utilization for each transaction, and information on the number of executions for each transaction, a processing amount predicting unit performs a statistical time-series analysis on the basis of time-series data of the number of executions for each transaction included in the transaction log and predicts an amount of processing for each type of a transaction, and a resource utilization predicting unit predicts the computer resource utilization or demand on from the estimated resource utilization for each transaction and the future predicted amount of processing for each transaction.

Claims

1-17. -17. (canceled)

18. A system for predicting a demand on a computer resource, comprising:

a storage device for storing a first log and a second log;

first means for acquiring a first log on a use of the resource at a plurality of times, which has been periodically recorded, and a second log on transactions using the resource at corresponding times, which has been periodically recorded;

second means for estimating resource utilization for each type of transaction on the basis of information resource use at the plurality of times included in the first log and information on a number of transactions of each transaction type processed at corresponding times included in the second log by calculating an average value of resource use for a fixed time period, calculating an amount of processing for each transaction type for the fixed time period from the number of transactions of each transaction type processed, and estimating the resource utilization for each type of transaction by a multiple linear regression analysis of the average value of resource use and the amount of processing for each transaction type;

third means for acquiring the second log and, performing a statistical time-series analysis on the basis of time-series data of the number of transactions of each transaction type processed by identifying a plurality of time-series models for each transaction type using the statistical time-series data for the number of transactions of each transaction type processed, comparing each time-series model of the plurality of time-series models for each transaction type to the time-series data of the number of transactions of each transaction type processed to determine an error for each time-series model, selecting a time-series model for each transaction type from the plurality of time-series models for each transaction type which has the smallest error, and predicting a future amount of processing volume for each transaction type using the selected time-series model for each transaction type; and

fourth means for predicting a computer resource demand on the basis of the resource utilization for each transaction type estimated by said second means and the future amount of processing for each transaction type predicted by said third means by summing products of the resource utilization for each transaction type and the future amount of processing for each transaction type.

19. The system according to claim 18, wherein said third means excludes a part of the time-series data, identifies the plurality of time-series models on the basis of remaining data, predicts an amount of processing volume for each transaction type corresponding to the excluded part of the time-series data from each of the plurality of time-series models, and compares a result of the prediction with actual data of the excluded part to determine an error for each time-series model.

20. The system according to claim 19, wherein said third means excludes the last data of a certain period in the time-series data and uses the last data of the certain period for determining an error for each time-series model.

21. The system according to claim 18, wherein the plurality of time-series models identified on the basis of the time-series data are ARIMA models.

22. The system according to claim 18, wherein the fourth means is a server sizing means for deciding a configuration or setting of software or hardware of the computer on the basis of the resource utilization for each transaction type estimated by the second means and the future amount of processing volume for each transaction type predicted by the third means.

23. The system according to claim 18, wherein the fourth means is a server monitoring means for controlling monitor movement of the computer on the basis of the resource utilization for each transaction type estimated by the second means and the future amount of the processing volume for each transaction type predicted by the third means.

24. The system according to claim 18, wherein said resource comprises one of a CPU (central processing unit) utilization, a memory usage, a storage capacity of a storage device, and a network usage.

25. The system according to claim 24, wherein the server monitoring means further determines whether or not a warning is given.

26. A computer readable storage medium having computer readable instructions for carrying out a method for causing a computer to monitor a server, the method comprising the steps:

implementing instructions for acquiring a first log containing resource utilization data for a plurality of times and a second log containing transaction data from the server at corresponding times and storing the first log and the second log in a storage device;

implementing instructions for estimating resource utilization for each type of transaction concerned on the basis of resource utilization data at the plurality of times of the entire server included in the first log and information on a number of executions processed of each transaction type included in the second log by calculating an average value of resource utilization data for a fixed time period, calculating an amount of processing for each transaction type for the fixed time period from the number of executions processed of each transaction type, and estimating the resource utilization for each type of transaction by a multiple linear regression analysis of the average value of resource utilization data and the amount of processing for each transaction type;

implementing instructions for performing a statistical time-series analysis on the basis of time-series data of the number of executions of each transaction type included in the second log by identifying a plurality of time-series models for each transaction type using the time-series data of the number of executions of each transaction type, comparing each time-series model of the plurality of time-series models for each transaction type to the time-series data of the number of executions for each transaction type to determine an error for each time-series model, selecting a time-series model for each transaction type from the plurality of time-series models for each transaction type which has the smallest error, and predicting a future amount of executions for each transaction type using the selected time-series model for each transaction type; and

implementing instructions for predicting server resource utilization on the basis of the resource utilization for each type of transaction estimated by the estimating step and the future amount of executions for each transaction type predicted by the predicting step by summing products of the resource utilization for each type of transaction and the future amount of executions for each transaction type.

27. The computer readable storage medium according to claim 26, wherein the implementing instructions for performing a statistical time-series analysis comprises:

implementing instructions for excluding a part of the time-series data and identifying the plurality of time-series models for each transaction type on the basis of remaining data;

implementing instructions for predicting a future amount of executions for each transaction type corresponding to the excluded part of the time-series data by each of the time-series models; and

implementing instructions for comparing a result of the prediction with actual data of the excluded part to determine an error for each time-series model.

28. The computer readable storage medium according to claim 26, wherein said resource comprises one of a CPU (central processing unit) utilization, a memory usage, a storage capacity of a storage device, and a network usage.