Automated scaling of multi-tier applications using reinforced learning
US9412075B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 23, 2013 |
| Grant date | Aug 9, 2016 |
| Priority date | — |
| Expiry date | Apr 19, 2034 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F3/0685
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A module and method for automatically scaling a multi-tier application, wherein each tier of the multi-tier application is supported by at least one virtual machine, selects one of reinforced learning and heuristic operation based on a policy to recommend a scaling action from a current state of the multi-tier application. If reinforced learning is selected, the reinforced learning is applied to select the scaling action from a plurality of possible actions for the multi-tier application in the current state. If heuristic operation is selected, the heuristic operation is applied to select the scaling action using a plurality of defined heuristics.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.