Machine learning prediction of virtual computing instance transfer performance
US10853116B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jul 19, 2018 |
| Grant date | Dec 1, 2020 |
| Priority date | — |
| Expiry date | Nov 21, 2038 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F2201/815
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The disclosure provides an approach for preventing the failure of virtual computing instance transfers across data centers. In one embodiment, a flow control module collects performance information primarily from components in a local site, as opposed to components in a remote site, during the transfer of a virtual machine (VM) from the local site to the remote site. The performance information that is collected may include various performance metrics, each of which is considered a feature. The flow control module performs feature preparation by normalizing feature data and imputing missing feature data, if any. The flow control module then inputs the prepared feature data into machine learning model(s) which have been trained to predict whether a VM transfer will succeed or fail, given the input feature data. If the prediction is that the VM transfer will fail, then remediation actions may be taken, such as slowing down the VM transfer.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.