Proactive Buffer Management of Shared-Memory Switches for Distributed Deep Learning
Ye Jin, Yajun Peng, Yijun Li, Jiawei Huang · 2024
Each output port in a shared memory switch can compete for shared memory pool resources. The allocation strategy of the shared buffer directly affects the ability of each output port to absorb network traffic. Due to the unpredictability of traditional network traffic, existing switch buffer management strategies take a passive approach, allocating buffers to each port only after traffic arrives. This passive response has the problem of untimely buffer allocation and cannot effectively absorb burst traffic. Distributed deep learning follows a specific training pattern, and network traffic exhibits obvious periodic characteristics during transmission. Thus, we propose a Proactive Dynamic Threshold (PDT) strategy, which realizes the pre-adjustment of switch port threshold by detecting the traffic characteristics of distributed training.