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 network ﬂows for control and bandwidth- hungry ﬂows for distributed learning. Unfortunately, existing wireless network supporting systems cannot coordinate these two network ﬂows to meet their own requirements: prioritized contention systems (e.g., EDCA) prevent latency-sensitive messages from timely acquiring the wireless channel because multiple wireless network interface cards (WNICs) with bandwidth- hungry messages are contending for the channel; global planning systems (e.g., SchedWiFi) have to reserve a notable time window in the shared channel for each latency-sensitive ﬂow, suffering from severe bandwidth degradation (up to 42%). We present the coordinated preemption method to meet both requirements for latency-sensitive ﬂows and bandwidth-hungry ﬂows. Globally (among multiple robots), coordinated preemption eliminates unnecessary contention of bandwidth-hungry ﬂows by making them transmit in a round-robin manner, such that latency-sensitive ﬂows have the highest chance to win the contention against bandwidth-hungry 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 latency-sensitive ﬂows from the upper application and conservatively limits packets of bandwidth-hungry ﬂows buffered in the WNIC only before latency-sensitive packets arriving, reducing the bandwidth devoted to preemption. C OORP , our implementation of coordinated preemption, reduced the violation of latency requirements from 53.9% (EDCA) to 8.8% (comparable to SchedWiFi). Regarding learning quality, C OORP achieved a comparable (at times the same) learning reward with EDCA, which grew up to 76% faster than SchedWiFi.