Multipath transport protocols utilize multiple network paths (e.g., WiFi and cellular) to achieve improved performance and reliability, compared with their single-path counterparts. The scheduler of a multipath transport protocol determines how to distribute the data packets onto different paths. However, state-of-the-art multipath schedulers face the challenge when dealing with heterogeneous paths with dynamic path characteristics (i.e., packet loss, fluctuation of delay). In this paper, we propose Peekaboo, a novel learning-based multipath scheduler that is aware of the dynamic characteristics of the heterogeneous paths. Peekaboo is able to learn scheduling decisions to adopt over time based on the current path characteristics and dynamicity levels - from both deterministic and stochastic perspectives. We implement Peekaboo in Multipath QUIC (MPQUIC) and compare it with state-of-the-art multipath schedulers for a wide range of dynamic heterogeneous environments, upon both emulated and real networks. Our results show that Peekaboo outperforms the other schedulers by up to 31.2% in emulated networks and up to 36.3% in real network scenarios.

中文翻译：

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Peekaboo：用于动态异构环境的基于学习的多路径调度

与单路径协议相比，多路径传输协议利用多个网络路径（例如，WiFi 和蜂窝网络）来实现更高的性能和可靠性。多路径传输协议的调度器决定如何将数据包分发到不同的路径上。然而，最先进的多路径调度器在处理具有动态路径特性（即丢包、延迟波动）的异构路径时面临挑战。在本文中，我们提出了 Peekaboo，一种新的基于学习的多路径调度器，它了解异构路径的动态特性。Peekaboo 能够根据当前的路径特征和动态水平，从确定性和随机性的角度学习随着时间的推移采用的调度决策。我们在多路径 QUIC (MPQUIC) 中实现了 Peekaboo，并将其与最先进的多路径调度器进行比较，以适用于各种动态异构环境，模拟网络和真实网络。我们的结果表明，Peekaboo 在模拟网络中的性能优于其他调度程序高达 31.2%，在真实网络场景中高达 36.3%。