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  1. Goal: video streaming in vehicular networks via WiFi Compelling usage scenarios Gas stations and local shops deploy APs to provide video and ads Taxis/buses provide value-added services to passengers Cellular networks: costly ($60 for 5GB/month  0.1Mbps for <5 days!); limited bandwidth Overview

  2. Enabling High-Bandwidth Vehicular Content Distribution U. Shevade, Y. C. Chen, L. Qiu, Y. Zhang, V. Chandar, M. K. Han, H. H. Song, Y. S. Seung UT Austin

  3. Challenges Vehicles move at high speed WiFi contacts are short and intermittent 70% contacts less than 10 seconds Sparse AP coverage Dense coverage over large area expensive Internet access links to APs are bottleneck Naïve solution: Download from Internet during contact Insufficient b/w if data fetched during contact 3

  4. Types of Connectivity AP wireline access: persistent connectivity, but insufficient BW Internet-to-AP throughput is 768Kbps-6Mbps (DSL) Cannot sustain high data rate if data is fetched only during contact AP wireless access: high BW, but short-lived connectivity Our measurements: AP-to-car throughput is 40-56Mbps using 802.11n High vehicular speed  short contact (70% contacts less than 10s) Wireless mesh network: high BW, but low coverage Vehicle relay traffic between APs: high BW, high delay Q: Can we combine multiple types of connectivity to enable high-bandwidth vehicular content delivery?

  5. Synergy among connections High b/w, low coverage High b/w, short-lived Mesh Network AP Wireless VCD High b/w, persistent Internet Access Vehicle Relay Low b/w, persistent High b/w, high delay 5

  6. VCD Architecture Content Source Controller Internet • Download and upload data • Upload GPS location updates, video demands, what car has 6

  7. Contributions New techniques to optimize replication Goal: Fully utilize wireless bandwidth during contact Optimize wireline replication to Internet-connected APs Optimize mesh replication and use it for cooperative caching Replicate using vehicular relays to APs New algorithm for mobility prediction Predict set of APs that will be visited by vehicles Critical for success of replication techniques 7

  8. Wireline Replication At start of interval i Controller collects vehicle demands for interval (i+1) and what content is present at vehicles and APs Predicts set of APs visited by vehicle in interval (i+1) Computes what content should be replicated to which APs During interval i Content servers replicate content to APs During interval (i+1) Vehicle downloads content from APs 8

  9. Optimize Wireline Replication For each interval i, compute replication strategy maximizing user satisfaction for interval (i+1) Interval length, Content present at cars and APs, car demand, AP-to-visit Content to transfer to APs and content to download to cars Total content downloaded to cars weighted by interest, while minimizing the amount of content replicated to APs Total download from AP to car bound by wireless capacity Per-file download to car bound by the difference between file size and what car already has Per-file download to car cannot exceed what AP already has and what is replicated to it from the Internet Per-file replication to AP bound by the difference between file size and what AP already has Total replication to AP does not exceed Internet access link capacity 9

  10. Contributions New techniques to optimize replication Goal: Fully utilize wireless bandwidth during contact Optimize wireline replication to Internet-connected APs Optimize mesh replication and use it for cooperative caching Replicate using vehicular relays to APs New algorithm for mobility prediction Predict set of APs that will be visited by vehicle Critical for success of replication techniques 10

  11. Mesh Networks of APs Seattle, 100m range Seattle, 200m range San Francisco, 100m range San Francisco, 200m range Substantial contact with APs that can potentially form mesh networks • APs are often close enough to form mesh networks CDF of total contact duration with AP connected components 11

  12. Mesh Networks of APs Per-file download to car cannot exceed what AP already has and what is replicated to it from the Internet and from the mesh AP cannot replicate more content over mesh than it has Interference constraint: Total active time of all mesh nodes cannot exceed 100%, assuming all nodes interfere with each other Prefer a replication which uses less mesh traffic among the ones supporting equal traffic demands • Nearby APs can be organized into mesh networks using another wireless card • Replicate content to APs using mesh in addition to Internet link • Fetch missing content from other mesh nodes rather than Internet • Changes to linear program • Constraint C3: • Two new constraints: • Objective function: • Add 12

  13. Contributions New techniques to optimize replication Goal: Fully utilize wireless bandwidth during contact Optimize wireline replication to Internet-connected APs Optimize mesh replication and use it for cooperative caching Replicate using vehicular relays to APs New algorithm for mobility prediction Predict set of APs that will be visited by vehicle Critical for success of replication techniques 13

  14. Vehicular Replication Vehicles act as data relays between APs Simple strategy: epidemic dissemination Vehicle uploads content to AP based on expected future demand at AP AP computes future demand, car notifies what it has AP requests content from the car Vehicle downloads content from AP First the files it is interested In remaining time, download content randomly 14

  15. Mobility Prediction Predict which APs a car will meet in next interval Challenges: Vehicles move at high speeds GPS location updates from vehicles Low frequency Irregular updates Road and traffic conditions highly dynamic Previous work: 1st and 2nd order Markov models Do not perform well on our dataset 15

  16. Voting among K Nearest Trajectories Exploit history to predict contact: • Past trajectories from other vehicles • Find K trajectories that most closely match the vehicle’s recent history • Obtain future path for K trajectories • Report all APs visited by at least T of K trajectories Vehicle’s near history 16

  17. Mobility Prediction Results • Setup: Gas stations as APs, radio range = 200m, prediction interval 3min #Correctly predicted APs #Total predicted APs #Correctly predicted APs #Total APs actually visited ( 2 ) (1/precision+1/recall) Bus mobility is more predictable 1200 Seattle city buses 500 San Francisco Yellow Cabs Voting among K nearest trajectories performs best for our dataset 17

  18. VCD Implementation Ethernet HP iPaq, HTC Tilt C# on Windows Mobile 6.1 Coordinator LP Server Controller 802.11b APs UDP with congestion control Content servers 802.11n APs Dell, Macbook Pro C++ on Linux C# on Windows XP 18 TCP for control messages, UDP for data

  19. 802.11b Testbed 14 APs deployed in 8 campus buildings APs are in-building, 20-60ft from the road 802.11b radios with fixed data rate of 11Mbps 3 APs in ACES form a mesh network Smartphone clients stream H.264 videos at 64Kbps 13, 14 3 4 5 6 7,8,9,10, 11,12 2 1 19

  20. 802.11n Testbed • 802.11n is the new WLAN standard • Considerable throughput increase over 802.11b/g • Uses MIMO and 20/40MHz channels • Vehicular throughput experiments • Considerable potential throughput increase over 11b • Deployed four 802.11n APs • Laptops used as clients 20

  21. Results – Simulation Wireline+wireless 5.2X baseline 6.3X better than baseline Mesh adds 3-13% Benefit from wireline replication Wireless replication helps! Internet is the bottleneck APs: Coffee Shops, 100m range APs: Gas stations, 100m range VCD achieves higher throughput by combining wireline, wireless and mesh replication Setup: 50 cars, Zipf-like demands, 50% APs not connected to Internet 21

  22. Results – Simulation Mesh benefits 14-20% High Medium Low Video quality over 3G APs: Coffee Shops, 100m range APs: Coffee shops, 200m range Benefits increase with higher range and dense AP deployment Setup: 50 cars, Zipf-like demands, 50% APs not connected to Internet 22

  23. Emulab: Simulator Validation Simulator results within 10% of Emulab results • Setup: 30 APs, 100 cars, 200m range All APs connected to Internet 10% APs connected to Internet 23

  24. Results - Testbed 2.7X 7.8X 802.11b testbed: 8 APs, 3 connected by mesh 802.11n testbed: 4 APs, all connected by mesh 24

  25. Summary: Vehicular Content Distribution • KNT: A new mobility prediction algorithm • Based on voting among K nearest trajectories • 25-94% more accurate than 1st and 2nd order Markov models • A series of novel replication schemes • Optimized wireline replication and mesh replication • Opportunistic vehicular relay based replication • Extensive evaluation: simulation + testbed + emulation • Simulation using San Francisco taxi and Seattle bus traces • 3-6x of no replication, 2-4x of wireline or vehicular alone • Full-fledged prototype deployed on two real testbeds • 14-node 802.11b testbed and 4-node 802.11n testbed • 4.2-7.8x gain over no replication • Emulab emulation with real AP/controller and emulated vehicles • Show system works at scale and is efficient • Validate our trace-driven simulator