Systems and methods for generating performance prediction model and estimating execution time for applications
US10510007B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Mar 15, 2016 |
| Grant date | Dec 17, 2019 |
| Priority date | — |
| Expiry date | May 10, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F11/3466
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Systems and methods for generating performance prediction model and estimating execution time for applications is provided. The system executes synthetic benchmarks for a first dataset on a first cluster. Each synthetic benchmark includes a MapReduce (MR) job. The system further extracts sensitive parameters for each sub-phase of the MR job, generates a linear regression prediction model for each sub-phase to obtain one or more linear regression prediction models, based on which the system further generates a performance prediction model to be utilized for predicting, using the sensitive parameters, a Hive query execution time of a Directed Acyclic Graph (DAG) of one or more MR jobs executed on a second dataset on a second cluster, wherein the first cluster that includes the first dataset is smaller compared to the second cluster that includes the second dataset.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.