Coorp: Satisfying Low-Latency and High-Throughput Requirements of Wireless Network for Coordinated Robotic Learning

In coordinated robotic learning, multiple robots share the same wireless channel for communication, and bring together latency-sensitive (LS) network flows for control and bandwidth-hungry (BH) flows for distributed learning. Unfortunately, existing wireless network supporting systems cannot coordinate these two network flows to meet their own requirements: 1) prioritized contention systems (e.g., EDCA) prevent LS messages from timely acquiring the wireless channel because multiple wireless network interface cards (WNICs) with BH messages are contending for the channel 2) global planning systems (e.g., SchedWiFi) have to reserve a notable time window in the shared channel for each LS flow, suffering from severe bandwidth degradation (up to 42%). We present the coordinated preemption method to meet both requirements for LS flows and BH flows. Globally (among multiple robots), coordinated preemption eliminates unnecessary contention of BH flows by making them transmit in a round-robin manner, such that LS flows have the highest chance to win the contention against BH flows, without sacrificing overall bandwidth from the perspective of coordinated robotic learning applications. Locally (within the same robot), coordinated preemption in real time predicts the periodic transmission of LS flows from the upper application and conservatively limits packets of BH flows buffered in the WNIC only before LS packets arriving, reducing the bandwidth devoted to preemption. COORP, our implementation of coordinated preemption, reduced the violation of latency requirements from 53.9% (EDCA) to 8.8% (comparable to SchedWiFi). Regarding learning quality, COORP achieved a comparable (at times the same) learning reward with EDCA, which grew up to 76% faster than SchedWiFi.

Automated summary

In coordinated robotic learning, multiple robots share the same wireless channel for communication. Existing wireless network systems cannot coordinate the latency-sensitive (LS) network flows and bandwidth-hungry (BH) flows effectively. We propose the coordinated preemption method to meet the requirements of both LS and BH flows, achieving better performance in terms of latency violation and learning reward compared to existing systems.