Shinik Park
ABSTRACT
Since the diagnosis of severe bufferbloat in mobile cellular networks, a number of low-latency congestion control algorithms have been proposed. However, due to the need for continuous bandwidth probing in dynamic cellular channels, existing mechanisms are designed to cyclically overload the network. As a result, it is inevitable that their latency deviates from the smallest possible level (i.e., minimum RTT). To tackle this problem, we propose a new low-latency congestion control, ExLL, which can adapt to dynamic cellular channels without overloading the network. To do so, we develop two novel techniques that run on the cellular receiver: 1) cellular bandwidth inference from the downlink packet reception pattern and 2) minimum RTT calibration from the inference on the uplink scheduling interval. Furthermore, we incorporate the control framework of FAST into ExLL's cellular specific inference techniques. Hence, ExLL can precisely control its congestion window to not overload the network unnecessarily. Our implementation of ExLL on Android smartphones demonstrates that ExLL reduces latency much closer to the minimum RTT compared to other low-latency congestion control algorithms in both static and dynamic channels of LTE networks.
Supplemental Material
- 3GPP. 2017. Evolved Universal Terrestrial Radio Access (E-UTRA) and Evolved Universal Terrestrial Radio Access Network (E-UTRAN); Overall description (TS 36.300 v14.6.0 Release 14). (2017). http://www.3gpp.org/dynareport/36300.htm.Google Scholar
- 3GPP. 2017. Evolved Universal Terrestrial Radio Access (E-UTRA); Physical channels and modulation (TS 36.211 v14.6.0 Release 14). (2017). http://www.3gpp.org/dynareport/36211.htm.Google Scholar
- 3GPP. 2017. Evolved Universal Terrestrial Radio Access (E-UTRA); Physical layer procedures (TS 36.213 v14.6.0 Release 14). (2017). http://www.3gpp.org/dynareport/36213.htm.Google Scholar
- 3GPP. 2017. General Packet Radio Service (GPRS) enhancements for Evolved Universal Terrestrial Radio Access Network (E-UTRAN) access (TS 23.401 v14.7.0 Release 14). (2017). http://www.3gpp.org/dynareport/23401.htm.Google Scholar
- N. A. Ali, A. E. M. Taha, and H. S. Hassanein. 2013. Quality of Service in 3GPP R12 LTE-Advanced. IEEE Communications Magazine 51, 8 (2013), 103--109.Google ScholarCross Ref
- Venkat Arun and Hari Balakrishnan. 2018. Copa: Practical Delay-Based Congestion Control for the Internet. In Proc. of USENIX NSDI.Google ScholarDigital Library
- Praveen Balasubramanian. 2017. Updates on Windows TCP. (2017). https://datatracker.ietf.org/meeting/100/materials/slides-100-tcpm-updates-on-windows-tcp.Google Scholar
- Lawrence S. Brakmo, Sean W. O'Malley, and Larry L. Peterson. 1994. TCP Vegas: New Techniques for Congestion Detection and Avoidance. In Proc. of ACM SIGCOMM. Google ScholarDigital Library
- F. Capozzi, G. Piro, L. A. Grieco, G. Boggia, and P. Camarda. 2013. Downlink Packet Scheduling in LTE Cellular Networks: Key Design Issues and a Survey. IEEE Communications Surveys Tutorials 15, 2 (2013), 678--700.Google ScholarCross Ref
- Neal Cardwell, Yuchung Cheng, C Stephen Gunn, Soheil Hassas Yeganeh, and Van Jacobson. 2016. BBR: Congestion-Based Congestion Control. ACM Queue 14, 5 (2016), 50. Google ScholarDigital Library
- Google Developers. 2017. Chrome DevTools. (2017). https://developers.google.com/web/tools/chrome-devtools/.Google Scholar
- Peter X. Gao, Akshay Narayan, Gautam Kumar, Rachit Agarwal, Sylvia Ratnasamy, and Scott Shenker. 2015. pHost: Distributed Near-Optimal Datacenter Transport Over Commodity Network Fabric. In Proc. of ACM CoNEXT. Google ScholarDigital Library
- J. Gettys. 2011. Bufferbloat: Dark Buffers in the Internet. IEEE Internet Computing 15, 3 (May-June 2011), 96. Google ScholarDigital Library
- Yihua Guo, Feng Qian, Qi Alfred Chen, Zhuoqing Morley Mao, and Subhabrata Sen. 2016. Understanding On-device Bufferbloat for Cellular Upload. In Proc. of ACM IMC. Google ScholarDigital Library
- Sangtae Ha, Injong Rhee, and Lisong Xu. 2008. CUBIC: a New TCP-friendly High-speed TCP Variant. ACM SIGOPS Operating Systems Review 42 (July 2008), 64--74. Issue 5. Google ScholarDigital Library
- S. Hemminger. 2005. Netem - emulating real networks in the lab. In Proc. of the Linux Conference.Google Scholar
- Ravi Netravali Hongzi Mao and Mohammad Alizadeh. 2005. Netem - emulating real networks in the lab. In Proc. of the Linux Conference.Google Scholar
- Junxian Huang, Feng Qian, Yihua Guo, Yuanyuan Zhou, Qiang Xu, Z. Morley Mao, Subhabrata Sen, and Oliver Spatscheck. 2013. An In-depth Study of LTE: Effect of Network Protocol and Application Behavior on Performance. In Proc. of ACM SIGCOMM. Google ScholarDigital Library
- Keon Jang Inho Cho and Dongsu Han. 2017. Credit-Scheduled Delay-Bounded Congestion Control for Datacenters. In Proc. of ACM SIGCOMM. Google ScholarDigital Library
- Raj Jain. 1990. The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling. John Wiley and Sons.Google Scholar
- Haiqing Jiang, Yaogong Wang, Kyunghan Lee, and Injong Rhee. 2012. Tackling bufferbloat in 3G/4G networks. In Proc. of ACM IMC. Google ScholarDigital Library
- Chris Johnson. 2012. Long Term Evolution In Bullets. CreateSpace Independent Publishing Platform.Google Scholar
- Wai Kay Leong, Zixiao Wang, and Ben Leong. 2017. TCP Congestion Control Beyond Bandwidth-Delay Product for Mobile Cellular Networks. In Proc. of ACM CoNEXT. Google ScholarDigital Library
- Feng Lu, Hao Du, Ankur Jain, Geoffrey M. Voelker, Alex C. Snoeren, and Andreas Terzis. 2015. CQIC: Revisiting Cross-Layer Congestion Control for Cellular Networks. In Proc. of ACM Hot Mobile. Google ScholarDigital Library
- S. Mohan, R. Kapoor, and B. Mohanty. 2011. Latency in HSPA Data Networks. Technical Report. Qualcomm. https://goo.gl/kiEQrJGoogle Scholar
- NextEPC. 2017. Open source implementation of EPC. (2017). https://www.nextepc.org.Google Scholar
- Afif Osseiran, Jose F. Monserrat, and Werner Mohr. 2011. Mobile and Wireless Communications for IMT-Advanced and Beyond. Wiley, West Sussex, United Kingdom. Google ScholarDigital Library
- M. Simsek, A. Aijaz, M. Dohler, J. Sachs, and G. Fettweis. 2016. 5G-Enabled Tactile Internet. IEEE Journal on Selected Areas in Communications 34, 3 (March 2016), 460--473.Google ScholarDigital Library
- Zhaowei Tan, Yuanjie Li, Qianru Li, Zhehui Zhang, Zhehan Li, and Songwu Lu. 2018. Supporting Mobile VR in LTE Networks: How Close Are We? Proc. ACM Meas. Anal. Comput. Syst. 2, 1 (April 2018), 8:1--8:31. Google ScholarDigital Library
- Jeanette Wannstrom. 2013. Carrier Aggregation explained. (June 2013). http://www.3gpp.org/technologies/keywords-acronyms/101-carrier-aggregation-explained.Google Scholar
- WebPagetest. 2018. WebPagetest Documentation. (2018). https://sites.google.com/a/webpagetest.org/docs/.Google Scholar
- David X. Wei, Cheng Jin, Steven H. Low, and Sanjay Hegde. 2006. FAST TCP: Motivation, Architecture, Algorithms, Performance. IEEE/ACM Transactions on Networking 14 (December 2006), 1246--1259. Issue 6. Google ScholarDigital Library
- Keith Winstein, Anirudh Sivaraman, and Hari Balakrishnan. 2013. Stochastic forecasts achieve high throughput and low delay over cellular networks. In Proc. of USENIX NSDI. Google ScholarDigital Library
- Xiufeng Xie, Xinyu Zhang, and Shilin Zhu. 2017. Accelerating Mobile Web Loading Using Cellular Link Information. In Proc. of ACM MobiSys. Google ScholarDigital Library
- Swarun Kumar Xiufeng Xie, Xinyu Zhang and Li Erran Li. 2015. piStream: Physical Layer Informed Adaptive Video Streaming Over LTE. In Proc. of ACM MobiCom. Google ScholarDigital Library
- Yasir Zaki, Thomas Pötsch, Jay Chen, Lakshminarayanan Subramanian, and Carmelita Görg. 2015. Adaptive Congestion Control for Unpredictable Cellular Networks. In Proc. of ACM SIGCOMM. Google ScholarDigital Library
- Xincheng Zhang. 2018. LTE Optimization Engineering Handbook. Wiley, West Sussex, United Kingdom. Google ScholarDigital Library
Index Terms
- ExLL: an extremely low-latency congestion control for mobile cellular networks
Recommendations
Adaptive Congestion Control for Unpredictable Cellular Networks
SIGCOMM '15: Proceedings of the 2015 ACM Conference on Special Interest Group on Data CommunicationLegacy congestion controls including TCP and its variants are known to perform poorly over cellular networks due to highly variable capacities over short time scales, self-inflicted packet delays, and packet losses unrelated to congestion. To cope with ...
RELD, RTT ECN Loss Differentiation to optimize the performance of transport protocols on wireless networks
One major yet unsolved problem in wired-cum-wireless networks is the classification of losses, which might result from wireless temporary interferences or from network congestion. The transport protocol response to losses should be different for these ...
RoVegas: a router-based congestion avoidance mechanism for TCP Vegas
Transmission control protocol (TCP) Vegas detects network congestion in the early stage and successfully prevents periodic packet loss that usually occurs in TCP Reno. It has been demonstrated that TCP Vegas outperforms TCP Reno in many aspects. However,...
Comments