  As the scale of distributed training grows, communication becomes a
bottleneck. To accelerate the communication, recent works introduce In-Network
Aggregation (INA), which moves the gradients summation into network
middle-boxes, e.g., programmable switches to reduce the traffic volume.
However, switch memory is scarce compared to the volume of gradients
transmitted in distributed training. Although literature applies methods like
pool-based streaming or dynamic sharing to tackle the mismatch, switch memory
is still a potential performance bottleneck. Furthermore, we observe the
under-utilization of switch memory due to the synchronization requirement for
aggregator deallocation in recent works. To improve the switch memory
utilization, we propose ESA, an $\underline{E}$fficient Switch Memory
$\underline{S}$cheduler for In-Network $\underline{A}$ggregation. At its cores,
ESA enforces the preemptive aggregator allocation primitive and introduces
priority scheduling at the data-plane, which improves the switch memory
utilization and average job completion time (JCT). Experiments show that ESA
can improve the average JCT by up to $1.35\times$.
