Claims
- 1. An automated method of predicting a future attribute associated with an application, the method comprising the steps of:
obtaining a historical data set associated with the application for a given time interval; and predicting the future attribute for the given time interval, based on the historical data set, using a prediction model, the prediction model being checked for stability in the given time interval and altered when determined to be unstable.
- 2. The method of claim 1, further comprising the step of adapting a size of the historical data set for a subsequent time interval based on a result of the predicting step for the given time interval.
- 3. The method of claim 2, wherein the size of the historical data set is also adapted based on at least one confidence value.
- 4. The method of claim 1, wherein the given time interval is approximately one of a second and a minute.
- 5. The method of claim 1, wherein the given time interval is relatively shorter than a time interval used by a seasonal forecasting model.
- 6. The method of claim 1, wherein the future attribute comprises a future workload level.
- 7. The method of claim 1, wherein the prediction model comprises an Auto-Regressive Integrated Moving Average (ARIMA) model.
- 8. The method of claim 1, wherein the prediction model is alterable by reducing an order of the model.
- 9. The method of claim 1, wherein the order of the model is iteratively reducible by an order of one.
- 10. The method of claim 1, wherein the predicting step further comprises the steps of:
estimating one or more model parameters of the prediction model; checking the stability of the one or more model parameters; altering a structure of the prediction model when the model is considered to be unstable; and calculating the future attribute over a prediction horizon when the model is considered to be stable.
- 11. An automated method of predicting a future attribute associated with an application, the method comprising the steps of:
obtaining a historical data set associated with the application for a given time interval; predicting the future attribute for the given time interval based on the historical data set; and adapting a size of the historical data set for a subsequent time interval based on a result of the predicting step for the given time interval.
- 12. The method of claim 11, wherein the size of the historical data set is also adapted based on at least one confidence value.
- 13. An automated method of predicting a future attribute associated with an application, the method comprising the steps of:
obtaining a data set associated with the application; providing forecasting capability for determining the future attribute based on at least a portion of the data set for a first time interval; providing prediction capability for determining the future attribute based on at least a portion of the data set for a second time interval, wherein the second time interval is shorter than the first time interval; and outputting a prediction result representative of the future attribute based on at least one of the forecasting capability and the prediction capability.
- 14. The method of claim 13, further comprising the step of checking the validity of data in the obtained data set.
- 15. The method of claim 13, wherein the forecasting capability comprises use of a seasonal forecasting model.
- 16. The method of claim 13, wherein the prediction capability comprises use of a prediction model.
- 17. The method of claim 16, wherein the prediction model comprises an Auto-Regressive Integrated Moving Average (ARIMA) model.
- 18. Apparatus for predicting a future attribute associated with an application, the apparatus comprising:
a memory; and at least one processor coupled to the memory and operative to: (i) obtain a historical data set associated with the application for a given time interval; and (ii) predict the future attribute for the given time interval, based on the historical data set, using a prediction model, the prediction model being checked for stability in the given time interval and altered when determined to be unstable.
- 19. Apparatus for predicting a future attribute associated with an application, the apparatus comprising:
a memory; and at least one processor coupled to the memory and operative to: (i) obtain a historical data set associated with the application for a given time interval; (ii) predict the future attribute for the given time interval based on the historical data set; and (iii) adapt a size of the historical data set for a subsequent time interval based on a result of the predicting step for the given time interval.
- 20. Apparatus for predicting a future attribute associated with an application, the apparatus comprising:
a memory; and at least one processor coupled to the memory and operative to: (i) obtain a data set associated with the application; (ii) provide forecasting capability for determining the future attribute based on at least a portion of the data set for a first time interval; (iii) provide prediction capability for determining the future attribute based on at least a portion of the data set for a second time interval, wherein the second time interval is shorter than the first time interval; and (iv) output a prediction result representative of the future attribute based on at least one of the forecasting capability and the prediction capability.
- 21. An article of manufacture for predicting a future attribute associated with an application, comprising a machine readable medium containing one or more programs which when executed implement the steps of:
obtaining a historical data set associated with the application for a given time interval; and predicting the future attribute for the given time interval, based on the historical data set, using a prediction model, the prediction model being checked for stability in the given time interval and altered when determined to be unstable.
- 22. An article of manufacture for predicting a future attribute associated with an application, comprising a machine readable medium containing one or more programs which when executed implement the steps of:
obtaining a historical data set associated with the application for a given time interval; predicting the future attribute for the given time interval based on the historical data set; and adapting a size of the historical data set for a subsequent time interval based on a result of the predicting step for the given time interval.
- 23. An article of manufacture for predicting a future attribute associated with an application, comprising a machine readable medium containing one or more programs which when executed implement the steps of:
obtaining a data set associated with the application; providing forecasting capability for determining the future attribute based on at least a portion of the data set for a first time interval; providing prediction capability for determining the future attribute based on at least a portion of the data set for a second time interval, wherein the second time interval is shorter than the first time interval; and outputting a prediction result representative of the future attribute based on at least one of the forecasting capability and the prediction capability.
- 24. A method of attempting to ensure satisfaction of one or more service objectives associated with the execution of an application, the method comprising the steps of:
contracting a service provider to host the application in accordance with the one or more service objectives; and contracting the service provider to implement an automated system for predicting a future workload level associated with the application, the system being operative to: (i) obtain a historical data set associated with the application for a given time interval; and (ii) predict the future workload level for the given time interval, based on the historical data set, using a prediction model, the prediction model being checked for stability in the given time interval and altered when determined to be unstable.
- 25. A method of attempting to ensure satisfaction of one or more service objectives associated with the execution of an application, the method comprising the steps of:
contracting a service provider to host the application in accordance with the one or more service objectives; and contracting the service provider to implement an automated system for predicting a future workload level associated with the application, the system being operative to: (i) obtain a historical data set associated with the application for a given time interval; (ii) predict the future workload level for the given time interval based on the historical data set; and (iii) adapt a size of the historical data set for a subsequent time interval based on a result of the predicting step for the given time interval.
- 26. A method of attempting to ensure satisfaction of one or more service objectives associated with the execution of an application, the method comprising the steps of:
contracting a service provider to host the application in accordance with the one or more service objectives; and contracting the service provider to implement an automated system for predicting a future workload level associated with the application, the system being operative to: (i) obtain a data set associated with the application; (ii) provide forecasting capability for determining the future workload level based on at least a portion of the data set for a first time interval; (iii) provide prediction capability for determining the future workload level based on at least a portion of the data set for a second time interval, wherein the second time interval is shorter than the first time interval; and (iv) output a prediction result representative of the future attribute based on at least on of the forecasting capability and the prediction capability.
- 27. An automated system for predicting a future attribute associated with a measurement source, the system comprising:
a long term forecaster for determining the future attribute based on a long term time interval and at least a portion of an input data set associated with the measurement source; and a short term predictor for determining the future attribute based on a short term time interval and at least a portion of an input data set associated with the measurement source; wherein a prediction result is output representative of the future attribute based on at least on of the long term forecaster and the short term predictor.
- 28. The system of claim 27, wherein the measurement source comprises one or more resources associated with an application.
- 29. The system of claim 28, wherein the future attribute comprises a workload level associated with the application.
- 30. The system of claim 27, wherein the short term predictor comprises an Auto-Regressive Integrated Moving Average (ARIMA) model.
CROSS REFERENCE TO RELATED APPLICATION
[0001] The present application is related to U.S. patent application identified by attorney docket no. YOR920030049US1, entitled “Methods and Apparatus for Managing Computing Deployments in Presence of Variable Workloads,” filed concurrently herewith, the disclosure of which is incorporated by reference herein.