Inferring in-Network Queue Management from End Hosts in Real-Time Communications

Active queue management (AQM) algorithms, widely deployed in the internet, are designed to signal end hosts with network conditions in the format of packet losses. However, real-time communication (RTC) applications adopt delay-sensitive congestion control algorithms (CCAs), which are no longer responsive to losses or explicit notifications from AQMs. Moreover, packet losses introduced by different AQMs will further degrade the performance of RTC applications due to unexpected and unnecessary loss recovery. We are therefore motivated to understand the behaviors of AQMs and take necessary countermeasures for RTC applications proactively. For example, with the help of AQM inference, RTC applications will benefit by using loss recovery mechanisms that adapt to various kinds of AQMs to deal with packet losses. However, it is challenging to infer the AQM from end hosts since numerous AQMs have different configurations after decades of evolution. We analyze the temporal behaviors of loss series, extract the inherent invariant features of different AQMs, and categorize them into three types. Our simulation shows that AQM inference can classify AQMs with an accuracy of 96%. We also evaluate a use case on using the AQM inference to improve the loss recovery mechanism (forward error correction, FEC). Our FEC method based on AQM inference improves the recovery rate by at least 56%, and finally reduces the end-to-end delay by 13%.