Stochastic Forecasts Achieve High Throughput and Low Delay over Cellular Networks

Sprout is an end-to-end transport protocol for interactive applications that desire high throughput and low delay. Sprout works well over cellular wireless networks, where link speeds change dramatically with time, and current protocols build up multi-second queues in network gateways. Sprout does not use TCP-style reactive congestion control; instead the receiver observes the packet arrival times to infer the uncertain dynamics of the network path. This inference is used to forecast how many bytes may be sent by the sender, while bounding the risk that packets will be delayed inside the network for too long. In evaluations on traces from four commercial LTE and 3G networks, Sprout, compared with Skype, reduced self-inflicted end-to-end delay by a factor of 7.9 and achieved 2.2× the transmitted bit rate on average. Compared with Google’s Hangout, Sprout reduced delay by a factor of 7.2 while achieving 4.4× the bit rate, and compared with Apple’s Facetime, Sprout reduced delay by a factor of 8.7 with 1.9× the bit rate. Although it is end-to-end, Sprout matched or outperformed TCP Cubic running over the CoDel active queue management algorithm, which requires changes to cellular carrier equipment to deploy. We also tested Sprout as a tunnel to carry competing interactive and bulk traffic (Skype and TCP Cubic), and found that Sprout was able to isolate client application flows from one another.

[1]  Janardhan R. Iyengar,et al.  Low Extra Delay Background Transport (LEDBAT) , 2012, RFC.

[2]  Luca De Cicco,et al.  A Google Congestion Control Algorithm for Real-Time Communication , 2012 .

[3]  Van Jacobson,et al.  Controlling queue delay , 2012, Commun. ACM.

[4]  Ratul Mahajan,et al.  High Performance Vehicular Connectivity with Opportunistic Erasure Coding , 2012, USENIX ATC.

[5]  J Gettys,et al.  Bufferbloat: Dark Buffers in the Internet , 2011, IEEE Internet Computing.

[6]  Cheng Jin,et al.  FAST TCP: Motivation, Architecture, Algorithms, Performance , 2006, IEEE/ACM Transactions on Networking.

[7]  Qian Zhang,et al.  A Compound TCP Approach for High-Speed and Long Distance Networks , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[8]  Arun Venkataramani,et al.  Proceedings of the 5th Symposium on Operating Systems Design and Implementation Tcp Nice: a Mechanism for Background Transfers , 2022 .

[9]  Kang G. Shin,et al.  The BLUE active queue management algorithms , 2002, TNET.

[10]  R. Srikant,et al.  Analysis and design of an adaptive virtual queue (AVQ) algorithm for active queue management , 2001, SIGCOMM '01.

[11]  H. Balakrishnan,et al.  Dynamic behavior of slowly-responsive congestion control algorithms , 2001, SIGCOMM '01.

[12]  Deepak Bansal,et al.  Binomial congestion control algorithms , 2001, Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213).

[13]  Mark Handley,et al.  Equation-based congestion control for unicast applications , 2000, SIGCOMM.

[14]  Matthew S. Grob,et al.  CDMA/HDR: a bandwidth-efficient high-speed wireless data service for nomadic users , 2000, IEEE Commun. Mag..

[15]  L. Peterson,et al.  TCP Vegas: new techniques for congestion detection and avoidance , 1994, SIGCOMM.

[16]  V. Paxson,et al.  Wide-area traffic: the failure of Poisson modeling , 1994, SIGCOMM.

[17]  FloydSally,et al.  Random early detection gateways for congestion avoidance , 1993 .

[18]  Kyunghan Lee,et al.  Tackling bufferbloat in 3G/4G mobile networks , 2012 .

[19]  Srinivasan Keshav,et al.  Packet-Pair Flow Control , 2003 .

[20]  QUTdN QeO,et al.  Random early detection gateways for congestion avoidance , 1993, TNET.

[21]  V. Jacobson,et al.  Congestion avoidance and control , 1988, SIGCOMM '88.

[22]  D. K. Cox,et al.  Long-range dependence: a review , 1984 .