Edge network computing system with deep reinforcement learning based task scheduling
US12223336B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 30, 2021 |
| Grant date | Feb 11, 2025 |
| Priority date | — |
| Expiry date | Aug 19, 2043 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F2209/509
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
An edge network computing system includes: a plurality of terminal devices; a plurality of edge servers connected to the terminal device through an access network; and a plurality of cloud servers connected to the plurality of edge servers through a core network. Each edge server is configured to: receive a plurality of computing tasks originated from one of the plurality of terminal devices; use a deep Q-learning neural network (DQN) with experience replay to select one of the plurality of could servers to offload a portion of the plurality of computing tasks; and send the portion of the plurality of computing tasks to the selected cloud server and forward results of the portion of the plurality of computing tasks received from the selected cloud server to the originating terminal device.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.