A performance prediction model for a target data analytics application, where: (i) a reference data analytics application similar to the target data analytics application is determined; (ii) a configuration-performance data pair of the target data analytics application are acquired; and (iii) the performance prediction model for the target data analytics application is determined based on the configuration-performance data pair of the target data analytics application and a configuration-performance data pair of the at least one reference data analytics application. This can reduce the time required to accumulate the configuration-performance data pairs for determining the performance prediction model by combining the configuration-performance data pairs of the existing data analytics applications, thereby accelerating determination of the performance prediction model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for determining a performance prediction model for a target data analytics application, comprising: selecting a first reference data analytics application, from a plurality of data analytics application, with the selection being based, at least in part, on similarity to the target data analytics application; acquiring a configuration-performance data pair of the target data analytics application, the configuration-performance data pair including configuration data of the target data analytics application's own runtime environment and performance data of the target data analytics application in its own runtime environment; and determining the performance prediction model for the target data analytics application based, at least in part, on the configuration-performance data pair of the target data analytics application and a configuration-performance data pair of the first reference data analytics application. 2. The method according to claim 1, wherein the selection of the first reference data analytics application includes: acquiring performance data of the target data analytics application in the same runtime environment as that of the existing data analytics applications; acquiring degrees of similarity between the target data analytics application and the existing data analytics applications according to the performance data of the target data analytics application and the performance data of the existing data analytics applications; and determining the first reference data analytics application according to the degrees of similarity between the target data analytics application and the existing data analytics applications. 3. The method according to claim 2, wherein the acquisition of the performance data of the target data analytics application includes: running the target data analytics application in the same runtime environment; collecting size information and processing time information of data processed by the target data analytics application; and calculating the performance data based on the size information and the processing time information of the processed data. 4. The method according to claim 1, wherein the acquisition of the configuration-performance data pair of the target data analytics application includes: configuring a plurality of runtime environments for the target data analytics application; running the target data analytics application in the plurality of runtime environments; acquiring the performance data of the target data analytics application in the plurality of runtime environments; and associating the configuration data of the plurality of runtime environments with the corresponding performance data in the plurality of runtime environments to form the configuration-performance data pairs. 5. The method according to claim 1, wherein the determination of the performance prediction model for the target data analytics application includes: determining the performance prediction model for the target data analytics application by using at least one of the following: instance-based transfer learning, feature-based transfer learning, parameter-based transfer learning, and/or relationship-based transfer learning. 6. The method according to claim 1, wherein the determination of the performance prediction model for the target data analytics application includes: generating a first regression model by using the configuration-performance data pair of the first reference data analytics application; generating a second regression model by using the configuration-performance data pair of the target data analytics application; and determining the performance prediction model for the target data analytics application based on the first regression model and the second regression model. 7. The method according to claim 6, wherein the determination of the performance prediction model for the target data analytics application further includes: normalizing the configuration-performance data pair of the first reference data analytics application prior to generating the first regression model; and normalizing the configuration-performance data pair of the target data analytics application prior to generating the second regression model. 8. An apparatus for determining a performance prediction model for a target data analytics application, the apparatus comprising: an application determining module configured to determine a first reference data analytics application, from a plurality of data analytics application, based, at least in part, on similarity to the target data analytics application; a data acquiring module configured to acquire a configuration-performance data pair of the target data analytics application, the configuration-performance data pair including configuration data of the target data analytics application's own runtime environment and performance data of the target data analytics application in its own runtime environment; and a model determining module configured to determine the performance prediction model for the target data analytics application based, at least in part, on the configuration-performance data pair of the target data analytics application and a configuration-performance data pair of the first reference data analytics application. 9. The apparatus according to claim 8, wherein the application determining module comprises: an acquiring unit configured to acquire performance data of the target data analytics application in the same runtime environment as that of the existing data analytics applications; a degree of similarity acquiring unit configured to acquire degrees of similarity between the target data analytics application and the existing data analytics applications according to the performance data of the target data analytics application and the performance data of the existing data analytics applications; and an application determining unit configured to determine the first reference data analytics application according to the degrees of similarity between the target data analytics application and the existing data analytics applications. 10. The apparatus according to claim 9, wherein the acquiring unit comprises: a running unit configured to run the target data analytics application in the same runtime environment; a collecting unit configured to collect size information and processing time information of data processed by the target data analytics application; and a calculating unit configured to calculate the performance data based on the size information and the processing time information of the processed data. 11. The apparatus according to claim 8, wherein the data acquiring module comprises: a configuring unit configured to configure a plurality of runtime environments for the target data analytics application; a running unit configured to run the target data analytics application in the plurality of runtime environments; an acquiring unit configured to acquire the performance data of the target data analytics application in the plurality of runtime environments; and an associating unit configured to associate the configuration data of the plurality of runtime environments with the corresponding performance data in the plurality of runtime environments to form the configuration-performance data pairs. 12. The apparatus according to claim 8, wherein the model determining module is configured to determine the performance prediction model for the target data analytics application by using at least one of instance-based transfer learning, feature-based transfer learning, parameter-based transfer learning, and relationship-based transfer learning. 13. The apparatus according to claim 8, wherein the model determining module comprises: a generating unit configured to generate a first regression model by using the configuration-performance data pair of the first reference data analytics application, and generate a second regression model by using the configuration-performance data pair of the target data analytics application; and a model determining unit configured to determine the performance prediction model for the target data analytics application based on the first regression model and the second regression model. 14. The apparatus according to claim 13, wherein the model determining module further comprises: a normalizing unit configured to normalize the configuration-performance data pair of the first reference data analytics application prior to generating the first regression model, and normalize the configuration-performance data pair of the target data analytics application prior to generating the second regression model. 15. A computer program product for determining a performance prediction model for a target data analytics application, the computer program product comprising a computer readable storage medium having stored thereon: first program instructions programmed to select a first reference data analytics application, from a plurality of data analytics application, with the selection being based, at least in part, on similarity to the target data analytics application; second program instructions programmed to acquire a configuration-performance data pair of the target data analytics application, the configuration-performance data pair including configuration data of the target data analytics application's own runtime environment and performance data of the target data analytics application in its own runtime environment; and third program instructions programmed to determining the performance prediction model for the target data analytics application based, at least in part, on the configuration-performance data pair of the target data analytics application and a configuration-performance data pair of the first reference data analytics application. 16. The product according to claim 15, wherein the selection of the first reference data analytics application includes: acquiring performance data of the target data analytics application in the same runtime environment as that of the existing data analytics applications; acquiring degrees of similarity between the target data analytics application and the existing data analytics applications according to the performance data of the target data analytics application and the performance data of the existing data analytics applications; and determining the first reference data analytics application according to the degrees of similarity between the target data analytics application and the existing data analytics applications. 17. The product according to claim 16, wherein the acquisition of the performance data of the target data analytics application includes: running the target data analytics application in the same runtime environment; collecting size information and processing time information of data processed by the target data analytics application; and calculating the performance data based on the size information and the processing time information of the processed data. 18. The product according to claim 15, wherein the acquisition of the configuration-performance data pair of the target data analytics application includes: configuring a plurality of runtime environments for the target data analytics application; running the target data analytics application in the plurality of runtime environments; acquiring the performance data of the target data analytics application in the plurality of runtime environments; and associating the configuration data of the plurality of runtime environments with the corresponding performance data in the plurality of runtime environments to form the configuration-performance data pairs. 19. The product according to claim 15, wherein the determination of the performance prediction model for the target data analytics application includes: determining the performance prediction model for the target data analytics application by using at least one of the following: instance-based transfer learning, feature-based transfer learning, parameter-based transfer learning, and/or relationship-based transfer learning. 20. The product according to claim 15, wherein the determination of the performance prediction model for the target data analytics application includes: generating a first regression model by using the configuration-performance data pair of the first reference data analytics application; generating a second regression model by using the configuration-performance data pair of the target data analytics application; and determining the performance prediction model for the target data analytics application based on the first regression model and the second regression model.