Robust available bandwidth estimation against dynamic behavior of packet scheduler in operational LTE networks

We shed new light on the mechanism behind how the dynamic behavior of a packet scheduler at the link layer in mobile networks degrades the accuracy of conventional available bandwidth (i.e., unused capacity of an end-to-end path) estimation methods that use a probing packet train (i.e., a set of multiple probing packets). Most of the conventional methods, which were originally designed for wired networks, estimate available bandwidth at the receiver by detecting changes of the observed queuing delays of probing packets. They utilize a microscopic approach in which they check the difference of the queuing delay of each packet on a packet-by-packet basis in order to detect the queuing delay changes. We found that the dynamic behavior of a packet scheduler at the link layer dramatically disturbs the queuing delays observed at the receiver. The disturbed queuing delays make it tremendously difficult for the conventional microscopic approach to detect changes of the delays, resulting in degraded estimation accuracy.

[1]  Dimitrios Koutsonikolas,et al.  On the feasibility of bandwidth estimation in wireless access networks , 2011, Wirel. Networks.

[2]  Rodrigo Garrido,et al.  Samsung Galaxy S6 , 2015 .

[3]  Richard G. Baraniuk,et al.  pathChirp: Efficient available bandwidth estimation for network paths , 2003 .

[4]  Mats Björkman,et al.  On measuring available bandwidth in wireless networks , 2008, 2008 33rd IEEE Conference on Local Computer Networks (LCN).

[5]  UmaMaheswari Devi,et al.  On the estimation of available bandwidth in broadband cellular networks , 2014, 2014 Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (SECON).

[6]  Jeroen Wigard,et al.  Comparison of Available Bandwidth Estimation Techniques in Packet-Switched Mobile Networks , 2006, 2006 IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications.

[7]  Peter Steenkiste,et al.  Evaluation and characterization of available bandwidth probing techniques , 2003, IEEE J. Sel. Areas Commun..

[8]  Manish Jain,et al.  End-to-end available bandwidth: measurement methodology, dynamics, and relation with TCP throughput , 2002, SIGCOMM 2002.

[9]  Qiang Xu,et al.  PROTEUS: network performance forecast for real-time, interactive mobile applications , 2013, MobiSys '13.

[10]  Myungjin Lee,et al.  On the impact of 802.11n frame aggregation on end-to-end available bandwidth estimation , 2014, 2014 Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (SECON).

[11]  Hari Balakrishnan,et al.  Stochastic Forecasts Achieve High Throughput and Low Delay over Cellular Networks , 2013, NSDI.

[12]  Henning Wiemann,et al.  The LTE link-layer design , 2009, IEEE Communications Magazine.

[13]  M. Y. Sanadidi,et al.  The probe gap model can underestimate the available bandwidth of multihop paths , 2006, CCRV.

[14]  Takashi Oshiba,et al.  Quick end-to-end available bandwidth estimation for QoS of real-time multimedia communication , 2010, The IEEE symposium on Computers and Communications.

[15]  Ali C. Begen,et al.  An experimental evaluation of rate-adaptation algorithms in adaptive streaming over HTTP , 2011, MMSys.

[16]  Di Wu,et al.  Experimental Comparison of Bandwidth Estimation Tools for Wireless Mesh Networks , 2009, IEEE INFOCOM 2009.

[17]  Feng Qian,et al.  How to Reduce Smartphone Traffic Volume by 30%? , 2013, PAM.

[18]  Ki Hwan Yum,et al.  Bandwidth Estimation in Wireless Lans for Multimedia Streaming Services , 2006, 2006 IEEE International Conference on Multimedia and Expo.

[19]  Takashi Oshiba,et al.  Quick and simultaneous estimation of available bandwidth and effective UDP throughput for real-time communication , 2011, 2011 IEEE Symposium on Computers and Communications (ISCC).

[20]  Josep Mangues-Bafalluy,et al.  Impact of transient CSMA/CA access delays on active bandwidth measurements , 2009, IMC '09.

[21]  Carl Tim Kelley,et al.  Iterative methods for optimization , 1999, Frontiers in applied mathematics.

[22]  kc claffy,et al.  Bandwidth estimation: metrics, measurement techniques, and tools , 2003, IEEE Netw..

[23]  Giuseppe Piro,et al.  Downlink Packet Scheduling in LTE Cellular Networks: Key Design Issues and a Survey , 2013, IEEE Communications Surveys & Tutorials.

[24]  Mark Claypool,et al.  WBest: A bandwidth estimation tool for IEEE 802.11 wireless networks , 2008, 2008 33rd IEEE Conference on Local Computer Networks (LCN).

[25]  Manish Jain,et al.  Ten fallacies and pitfalls on end-to-end available bandwidth estimation , 2004, IMC '04.