Efficient machine learning for network optimization
US11153196B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Apr 21, 2020 |
| Grant date | Oct 19, 2021 |
| Priority date | — |
| Expiry date | Apr 21, 2040 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L45/64
- WIPO fieldDigital communication
- WIPO sectorElectrical engineering
Abstract
An autonomous controller for SDN, virtual, and/or physical networks can be used to optimize a network automatically and determine new optimizations as a network scales. The controller trains models that can determine in real-time the optimal path for the flow of data from node A to B in an arbitrary network. The controller processes a network topology to determine relative importance of nodes in the network. The controller reduces a search space for a machine learning model by selecting pivotal nodes based on the determined relative importance. When a demand to transfer traffic between two hosts is detected, the controller utilizes an AI model to determine one or more of the pivotal nodes to be used in routing the traffic between the two hosts. The controller determines a path between the two hosts which comprises the selected pivotal nodes and deploys a routing configuration for the path to the network.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.