Legilimens: An Agile Transport for Background Traffic in Cellular Networks

M. Chaudhry, S. Mathew, Shanyu Zhou, V. Gopalakrishnan, Emir Halepovic, H. Seferoglu, Balajee Vamanan
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Abstract

Large data transfers can result in significant congestion and performance degradation for interactive end-user applications such as web browsing and streaming. While there are existing TCP congestion control algorithms for delivery of large volume data (e.g., LEDBAT, TCP-LP), our results show that these protocols are not effective in cellular networks due to variability in radio channel conditions and the use of cellular schedulers in base stations. We propose Legilimens, an agile TCP variant for cellular downlink transfers, which not only retains desirable properties of existing approaches, but also exploits the properties of the cellular schedulers to estimate load and capacity and addresses the challenges in cellular networks. As a result, Legilimens is able to deliver traffic using only the spare capacity on the downlink. We conduct extensive evaluations of Legilimens in multiple settings—in a large cellular network for real-world performance, on the PhantomNet emulator for controlled experiments, and ns-3 simulator for scaled experiments—all of which demonstrate that Legilimens is superior to existing protocols in transferring large volumes of data without interfering with regular user traffic. Compared to existing low-priority protocols, Legilimens improves the throughput of background flows by 2x on average (up to 5x) without degrading the performance of foreground flows across all the three testbeds.
摄神:蜂窝网络中后台流量的敏捷传输
大数据传输可能会导致交互终端用户应用程序(如web浏览和流)出现严重的拥塞和性能下降。虽然现有的TCP拥塞控制算法用于传输大容量数据(例如,LEDBAT, TCP- lp),但我们的研究结果表明,由于无线信道条件的可变性和基站中蜂窝调度器的使用,这些协议在蜂窝网络中并不有效。我们提出了一种用于蜂窝下行链路传输的敏捷TCP变体Legilimens,它不仅保留了现有方法的理想特性,而且利用了蜂窝调度器的特性来估计负载和容量,并解决了蜂窝网络中的挑战。因此,Legilimens能够仅使用下行链路上的备用容量来传输流量。我们在多种环境下对摄神取物进行了广泛的评估——在大型蜂窝网络中进行真实性能测试,在PhantomNet模拟器上进行控制实验,在ns-3模拟器上进行规模实验——所有这些都表明摄神取物在传输大量数据而不干扰常规用户流量方面优于现有协议。与现有的低优先级协议相比,Legilimens将后台流的吞吐量平均提高了2倍(最高可达5倍),而不会降低所有三个测试平台上前台流的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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