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Rapid delay variations in today’s access networks impair the QoE of low-latency, interactive applications, such as video conferencing. To tackle this problem, we propose Athena, a framework that correlates high-resolution measurements from Layer 1 to Layer 7 to remove the fog from the window through which today’s video-conferencing congestion-control algorithms see the network. This cross-layer view of the network empowers the networking community to revisit and re-evaluate their network designs and application scheduling and rate-adaptation algorithms in light of the complex, heterogeneous networks that are in use today, paving the way for network-aware applications and application-aware networks.
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RAVEN: Improving Interactive Latency for the Connected Car
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
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- Award ID(s):
- 1717064
- PAR ID:
- 10113923
- Date Published:
- Journal Name:
- ACM MobiCom 2018
- Page Range / eLocation ID:
- 557 to 572
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3241539.3241571
