FMNC – Fast Mobile Network Characterization

The goal of our work on Fast Mobile Network Characterization is to explore the degree to which we can construct / explore interesting mechanisms for the purpose of characterizing the performance of various mobile links.  In short, the work explores the extent to which we can replace tools such as iPerf and Speedtest.net with ultra-lightweight and high speed bandwidth characterization mechanisms for both WiFi and cellular links.  Our work on FMNC can be grouped into the following categories:

  • Core System: The core of FMNC is a libpcap-based multi-threaded architecture that allows us to appropriately shape and structure outbound packets for the purpose of network performance characterization.  A significant body of our approaches lie on the notion of sliced, structured, and reordered packet trains which draw inspiration from our past work on RIPPS (Rogue Identifying Packet Payload Slicing) and TCP Sting.   A key focus of our work is to make the work deployable and as such nearly all of the techniques work within the context of a HTTP GET, effectively allowing any web browser or simple client-side app to deploy our technique.  Work is currently underway to create libraries for both iOS and Android as well as reference clients.
  • Active WiFi Characterization: In our AIMC and FMNC approaches, we explore WiFi characterization via active probing for varying network speeds.  The FMNC approach focuses on characterizing at speeds between 0-11 Mb/s while AIMC extends the work to allow for characterization of higher network speeds at the cost of additional bandwidth.    Our work on active WiFi characterization leverages the underlying Frame Aggregation (FA) mechanisms introduced by 802.11e to discern the available bandwidth on a given WiFi link.  Through the FMNC core system, we are able to characterize a link within a single HTTP GET with the FMNC work characterizing a link with less than 100 KB of bandwidth consumption and in less than 250 ms.
  • Active Cellular Characterization: Cellular characterization introduces unique challenges versus WiFi in that the scheduler present at the eNodeB and the underlying link / competing nodes introduce significant noise to the characterization process.  Our work on cellular characterization (name withheld as the work is currently under review) introduces new packet sequences to better discern cellular performance as well as alternative approaches for bandwidth characterization at our back end to compensate for such noise.
  • Passive WiFi Characterization: In our FMNC / AIMC works, we actively perturb the link during the course of a HTTP GET to drive the network to detect when significant aggregation occurs.  In our passive approach, we explore whether or not one can simply eavesdrop on a given WiFi link to discern link capacity.  We show that it is indeed possible through the processing of Block ACKs and clever processing to discern residual link capacity and can do this rapidly enough to be useful during the course of a normal WiFi scan interval by a mobile device.
  • Case Studies: We have also explored several case studies including performance at our University Relations Tent as well as applications to AP-side information sharing (Passpoint) and streaming video (HTTP DASH) adaptation.

Publications

  • Two submissions under review – active cellular and passive WiFi characterization
  • [FMNC] L. Song, A. Striegel, “A Lightweight Scheme for Rapid and Accurate WiFi Path Characterization,” in Proc. of ICCCN 2018, Hangzhou, China, August 2018. (Invited Paper)
  • [Case Study] L. Song, A. Striegel, “SEWS: A Channel-Aware Stall-Free WiFi Video Streaming Mechanism,” in Proc. of NOSSDAV 2018, Amsterdam, Netherlands, June 2018.
  • [AIMC] L. Song, A. Striegel, “Leveraging Frame Aggregation for Estimating WiFi Available Bandwidth,” in Proc. of IEEE SECON, San Diego, CA, June 2017.
  • [Case Study] L. Song, A. Striegel, “Leveraging Frame Aggregation to Improve Access Point Selection,” in Proc. of MobiWorld (workshop at INFOCOM 2017), Atlanta, Georgia, 2017.
  • L. Song, A. Striegel, “Systems and Methods for Rapidly Estimating Available Bandwidth on a WiFi Link,” Patent Application 15/967,532, applied on April 30, 2018.

Code

We are happy to share the codebase for FMNC (back-end) and clients under development on request.

People

  • PI – Aaron Striegel, Dept. of Computer Science and Engineering, University of Notre Dame
  • Grad Student – Lixing Song, Dept. of Computer Science and Engineering, University of Notre Dame
  • Grad Student – Xueheng Hu, Dept. of Computer Science and Engineering, University of Notre Dame
  • Grad Student – Shangyue Zhu, Dept. of Computer Science and Engineering, University of Notre Dame
  • REU Student – Spencer Spitz, Colgate University
  • REU Student – Jose Abraham Leon, Dept. of Computer Science and Engineering, University of Notre Dame
  • External Collaborator – Emir Halepovic – AT&T Labs

News

WiFi is Hot!

[September 1, 2018] For each home game at Notre Dame, our group provides WiFi for the University Relations tent on Irish Green.  For a high profile game such as the Michigan game, there are a few more challenges with the crowd density as well as nearby other tests.  Our WiFi gear has been newly re-racked […]

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Presentation at ICCCN 2018 on FMNC

[August 1st, 2018] Great conference at ICCCN 2018 in Hangzhou, China.  Had a chance to catch up with lots of familiar faces now having attended ICCCN for the past 7 years straight.  Great to meet up with my former student, Qi Liao, who also had a paper at ICCCN.  On Wednesday, I presented our base […]

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Presentation at NOSSDAV 2018

[June 15, 2018] Great workshop in Amsterdam for NOSSDAV 2018. I had the pleasure of presenting my student Lixing’s paper entitled “SEWS: A Channel-Aware Stall-Free WiFi Video Streaming Mechanism” at the workshop in addition to serving as a session chair for the first session.  Very well attended workshop and lots of great questions.

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Paper Accepted – NOSSDAV 2018

[April 24, 2018] Congrats to Lixing Song on getting his paper entitled “SEWS: A Channel-Aware Stall-Free WiFi Video Streaming Mechanism” accepted to appear at NOSSDAV 2018 in Amsterdam in June.

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Paper: Leveraging Frame Aggregation for Estimating WiFi Available Bandwidth

(March 14, 2017) Our paper entitled “Leveraging Frame Aggregation for Estimating WiFi Available Bandwidth” was accepted into IEEE SECON 2017 (26% acceptance rate).  The work is the second one under the Fast Mobile Network Characterization umbrella.  This work focuses on the potential to use reflected aggregation (client-mod free) as observed by A-MPDU frame characteristics for […]

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Paper: Leveraging Frame Aggregation to Improve Access Point Selection

(March 3, 2017) Our paper entitled “Leveraging Frame Aggregation to Improve Access Point Selection” has been accepted to appear at the INFOCOM International Workshop on Mobility Management in the Networks of the Future World (MobiWorld) workshop at INFOCOM 2017 to be held in Atlanta, GA in early May.

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