ExLL: An Extremely Low-latency Congestion Control for Mobile Cellular Networks

Abstract

Since the diagnosis of severe bufferbloat in mobile cellular networks, a number of low-latency congestion control algorithms have been proposed. However, due to the need for continuous bandwidth probing in dynamic cellular channels, existing mechanisms are designed to cyclically overload the network. As a result, it is inevitable that their latency deviates from the smallest possible level (i.e., minimum RTT). To tackle this problem, we propose a new low-latency congestion control, ExLL, which can adapt to dynamic cellular channels without overloading the network. To do so, we develop two novel techniques that run on the cellular receiver: 1) cellular bandwidth inference from the downlink packet reception pattern and 2) minimum RTT calibration from the inference on the uplink scheduling interval. Furthermore, we incorporate the control framework of FAST into ExLL's cellular specific inference techniques. Hence, ExLL can precisely control its congestion window to not overload the network unnecessarily. Our implementation of ExLL on Android smartphones demonstrates that ExLL reduces latency much closer to the minimum RTT compared to other low-latency congestion control algorithms in both static and dynamic channels of LTE networks.