RAVEN: Improving Interactive Latency for the Connected Car
RAVEN: Improving Interactive Latency for the Connected Car
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DOI：
10.1145/3241539.3241571
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发表时间：
2018-10
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影响因子：
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通讯作者：
Hyunjong Lee;J. Flinn;Basavaraj Tonshal
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文献类型：
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作者：
Hyunjong Lee;J. Flinn;Basavaraj Tonshal
如今销售的车辆越来越多地是联网汽车：它们通过内置WiFi和蜂窝接口提供车辆到基础设施的连接，并充当车内设备的移动的热点。我们研究了当今互联汽车的连接质量，重点关注面向用户的延迟敏感型应用。我们发现，网络延迟在短时间尺度上变化显著且不可预测，并且高尾部延迟大大降低了用户体验。我们还发现，由于商业WiFi产品，可用的覆盖范围选项有所增加，并且网络选项之间的延迟变化并不相关。基于这些发现，我们开发了RAVEN，一个内核中的MPTCP调度器，当网络延迟预测的置信度较低时，通过使用冗余传输来减轻尾部延迟和网络的不可预测性。RAVEN有几个新颖的设计特点。它透明地运行，无需应用程序修改或提示，以改善交互延迟。它无缝支持三个或更多无线网络。它的内核实现允许主动取消由于冗余而变得不必要的传输。最后，它明确地考虑了测量的年龄如何影响预测的置信度，从而更好地处理不经常传输的交互式应用程序和表现出暂时性能不佳的网络。仿真和现场车辆实验中的语音、音乐和推荐应用程序的结果显示，应用程序响应时间有了实质性的改善。
Increasingly, vehicles sold today are connected cars: they offer vehicle-to-infrastructure connectivity through built-in WiFi and cellular interfaces, and they act as mobile hotspots for devices in the vehicle. We study the connection quality available to connected cars today, focusing on user-facing, latency-sensitive applications. We find that network latency varies significantly and unpredictably at short time scales and that high tail latency substantially degrades user experience. We also find an increase in coverage options available due to commercial WiFi offerings and that variations in latency across network options are not well-correlated. Based on these findings, we develop RAVEN, an in-kernel MPTCP scheduler that mitigates tail latency and network unpredictability by using redundant transmission when confidence about network latency predictions is low. RAVEN has several novel design features. It operates transparently, without application modification or hints, to improve interactive latency. It seamlessly supports three or more wireless networks. Its in-kernel implementation allows proactive cancellation of transmissions made unnecessary through redundancy. Finally, it explicitly considers how the age of measurements affects confidence in predictions, allowing better handling of interactive applications that transmit infrequently and networks that exhibit periods of temporary poor performance. Results from speech, music, and recommender applications in both emulated and live vehicle experiments show substantial improvement in application response time.