DQN-based distributed computing network coordinate flow scheduling system and method
US12021751B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 23, 2023 |
| Grant date | Jun 25, 2024 |
| Priority date | — |
| Expiry date | Aug 23, 2043 |
Classification
- Technology area (CPC H)Electricity
- CPC primaryH04L43/16
- WIPO fieldDigital communication
- WIPO sectorElectrical engineering
Abstract
The present application discloses a DQN-based distributed computing network coordinate flow scheduling system and method. The method includes: establishing environmental feature data based on distributed computing task information and a congestion situation of a port queue in a programmable forwarding platform on a data plane, establishing and training a deep reinforcement learning intelligent agent based on an action value network and a target network in DQN, and the deep reinforcement learning intelligent agent outputting abstract actions; receiving, by a policy mapper, the abstract actions and mapping them into an executable coordinate flow scheduling policy; executing, by the programmable forwarding platform, the executable coordinate flow scheduling policy and updating the congestion situation of the port queue; and recording, a policy gainer, a completion time of a distributed computing task as a real-time reward of the deep reinforcement learning intelligent agent and iteratively optimizing the deep reinforcement learning intelligent agent.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.