A REINFORCEMENT LEARNING (RL) MODEL FOR DYNAMIC OPTIMIZATION OF SWITCH PORT/QUEUE BUFFER ALLOCATION
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. DETAILED DESCRIPTION Typically, switch buffer management algorithms and templates allocate the per port priority queue(s) buffer at the Line Card boot up phase. Thereafter, irrespective of the traffic profile observed, the buffer allocation remains unchanged. Consequently, the high and low priority queues suffer from sub optimal buffer allocation often resulting in frame drops. The techniques described herein provide a reinforcement learning model for dynamic optimization of switch port/queue buffer allocation. In the Artificial Intelligence/Machine Learning (AI/ML) realm, the problem can be framed as a Reinforcement Learning (RL) Model that interacts in a dynamic environment and takes corrective actions to ensure the high/low priority queue buffer allocation is optimized to minimize the frame loss. The dynamic environment monitors the buffer allocation, queue occupancy, queue priority, Quality of Service (QOS) configuration and per port and per queue traffic counters. The environment robustness and sophistication can be enhanced by including more relevant state. The model dynamically adjusts the buffer allocation in reaction to the detection of frame loss. The buffer allocation adjustment continues till the frame loss is 2 Pasha: A REINFORCEMENT LEARNING (RL) MODEL FOR DYNAMIC OPTIMIZATION OF S Published by Technical Disclosure Commons, 2018 2 5756 rectified. The RL model can tap into the available buffer pool or steals from a port/queue that might have excess buffer allocated. The canonical Q learning (model free) RL design for the agent and environment interaction is shown in Figure 1 below.