Abstract
Techniques are described herein for a reinforcement learning (RL) model for dynamic optimization of switch port/queue buffer allocation. According to the described techniques, a trained neural network model can be installed on a switch or on a network management server to dynamically adjust the shared/dedicated buffer allocation for the low/high priority queues in case of a frame loss. The dynamic buffer adjustment continues till the port/queue no longer experience a frame loss.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Pasha, Imran, "A REINFORCEMENT LEARNING (RL) MODEL FOR DYNAMIC OPTIMIZATION OF SWITCH PORT/QUEUE BUFFER ALLOCATION", Technical Disclosure Commons, (December 06, 2018)
https://www.tdcommons.org/dpubs_series/1760