Machine-learning-based load balancing for cloud-based disaster recovery apparatuses, processes and systems
US12223362B2 · kind B2 · utility
Assignee
Inventor
Key dates
| Filing date | Aug 10, 2021 |
| Grant date | Feb 11, 2025 |
| Priority date | — |
| Expiry date | Oct 21, 2042 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F2209/508
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The Machine-Learning-Based Load Balancing for Cloud-Based Disaster Recovery Apparatuses, Processes and Systems (“MLLB”) transforms workload agent installation request, AWCD training request, NWCD training request, asset workload classification request, node workload classification request, asset virtualization request inputs via MLLB components into workload agent installation response, AWCD training response, NWCD training response, asset workload classification response, node workload classification response, asset virtualization response outputs. An asset virtualization request datastructure is obtained. A set of asset workload classification labels for the asset determined using an asset workload classification datastructure is retrieved. A set of node workload classification labels for each node in a set of available compute nodes determined using a node workload classification datastructure is retrieved. A set of compatible candidate compute nodes is determined using a set of capacity threshold rules. A virtual machine corresponding to the asset is instantiated on a selected candidate compute node.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.