Augmenting mobile 3 g using wifi

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Ke Huang, UMass Lowell 91.650 Spring 2011

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Augmenting mobile 3 g using wifi

  1. 1. Augmenng  Mobile  3G  Using  WiFi By:  Aruna  Balasubramanian,  Ratul  Mahajan,  Arun   Venkataramani Presenter:  Ke  HuangTuesday, April 12, 2011 1
  2. 2. Demand  for  mobile  access  growing                        www.totaltele.com h+p://www.readwriteweb.com 2Tuesday, April 12, 2011 2
  3. 3. Demand  for  mobile  access  growing                        www.totaltele.com h+p://www.readwriteweb.com 900  million  mobile  broadband  subscripons  today….                                                                                                                                                         www.3gamericas.org                                                       2Tuesday, April 12, 2011 2
  4. 4. Mobile  demand  is  projected  to  far   3Tuesday, April 12, 2011 3
  5. 5. Mobile  demand  is  projected  to  far   www.rysavy.com Current  spectrum 409.5  MHz Unallocated  spectrum   230  MHz (including  whitespaces) Projected  demand  by   800  MHz  –   2016   1000  MHz 3Tuesday, April 12, 2011 3
  6. 6. Mobile  demand  is  projected  to  far   www.nyCmes.com www.rysavy.com Current  spectrum 409.5  MHz Unallocated  spectrum   230  MHz (including  whitespaces) Projected  demand  by   800  MHz  –   2016   1000  MHz 3Tuesday, April 12, 2011 3
  7. 7. Mobile  demand  is  projected  to  far   www.nyCmes.com www.rysavy.com Current  spectrum 409.5  MHz Unallocated  spectrum   230  MHz (including  whitespaces) Projected  demand  by   800  MHz  –   2016   1000  MHz www.nyCmes.com“In  light  of  the  limited  natural  resource  of  spectrum,  we  have  to  look  at   the  ways  of  conserving  spectrum”  -­‐-­‐  Mark  Siegel  (AT&T) Reducing  cellular  spectrum  ulizaon  is  key! 3Tuesday, April 12, 2011 3
  8. 8. How  can  we  reduce  spectrum  usage? blogs.chron.com 1.  Behavioral 2.  Economic 3.  Technical 4Tuesday, April 12, 2011 4
  9. 9. How  can  we  reduce  spectrum  usage? blogs.chron.com 1.  Behavioral www.usatoday.com 2.  Economic 3.  Technical 4Tuesday, April 12, 2011 4
  10. 10. How  can  we  reduce  spectrum  usage? blogs.chron.com 1.  Behavioral www.usatoday.com 2.  Economic 3.  Technical 4Tuesday, April 12, 2011 4
  11. 11. Augmenng  Mobile  3G  using  WiFi                            Offload  data  to  WiFi  when  possible                              Focus  on  vehicular  mobility 5Tuesday, April 12, 2011 5
  12. 12. Offloading  3G  data  to  WiFi 6Tuesday, April 12, 2011 6
  13. 13. Offloading  3G  data  to  WiFi 6Tuesday, April 12, 2011 6
  14. 14. Offloading  3G  data  to  WiFi 6Tuesday, April 12, 2011 6
  15. 15. Offloading  3G  data  to  WiFi 6Tuesday, April 12, 2011 6
  16. 16. Offloading  3G  data  to  WiFi 6Tuesday, April 12, 2011 6
  17. 17. Offloading  3G  data  to  WiFi 6Tuesday, April 12, 2011 6
  18. 18. Related  work  on  mulple  interfaces Improving  performance  using  handoffs  based  on   current  condions Reducing  power  consumpon  by  switching   across  mulple  interfaces   7Tuesday, April 12, 2011 7
  19. 19. Related  work  on  mulple  interfaces Improving  performance  using  handoffs  based  on   current  condions Reducing  power  consumpon  by  switching   across  mulple  interfaces   This  work: 1.How  much  3G  data  can  be  offloaded  to  WiFi? 2.How  to  offload  without  hurng  applicaons? 7Tuesday, April 12, 2011 7
  20. 20. Contribuons Measurement:    Joint  study  of  3G  and  WiFi   connecvity Across  three  cies:  Amherst,  Seagle,  SFO System:  Wiffler,  to  offload  3G  data  to  WiFi  while   respecng  applicaon  constraints   Deployed  on  20  vehicles 8Tuesday, April 12, 2011 8
  21. 21. Measurement  setup Testbed:  Vehicles  with  3G  and  WiFi  (802.11b)  radios Amherst:  20  buses  +  1  car,  Seagle:  1  car,  SFO:  1  car Soiware:  Simultaneously  probes  3G  and  WiFi  for   Availability,  loss  rate,  throughput Duraon:  3000+  hours  of  data  over  12+  days 9Tuesday, April 12, 2011 9
  22. 22. Open  WiFi  availability  low,  but  useful Availability    =  fracon  of  1-­‐second  intervals  when  at  least   one  packet  received                                             Availability (%) 10Tuesday, April 12, 2011 10
  23. 23. Open  WiFi  availability  low,  but  useful Availability    =  fracon  of  1-­‐second  intervals  when  at  least   one  packet  received                                             86% Availability (%) 10Tuesday, April 12, 2011 10
  24. 24. Open  WiFi  availability  low,  but  useful Availability    =  fracon  of  1-­‐second  intervals  when  at  least   one  packet  received                                             86% Availability (%) 11% 10Tuesday, April 12, 2011 10
  25. 25. Open  WiFi  availability  low,  but  useful Availability    =  fracon  of  1-­‐second  intervals  when  at  least   one  packet  received                                             86% Availability (%) 11% 7% 10Tuesday, April 12, 2011 10
  26. 26. Open  WiFi  availability  low,  but  useful Availability    =  fracon  of  1-­‐second  intervals  when  at  least   one  packet  received                                             86% Availability (%) 3G+WiFi  combinaCon  be+er  than  sum  pf  parts 11% 7% 10Tuesday, April 12, 2011 10
  27. 27. WiFi  loss  rate  is  higher Loss  rate  =  Fracon  of  packets  lost  at  10  probes/sec Cumulative fraction WiFi 3G 11Tuesday, April 12, 2011 11
  28. 28. WiFi  loss  rate  is  higher Loss  rate  =  Fracon  of  packets  lost  at  10  probes/sec Cumulative fraction 28%   WiFi 3G 11Tuesday, April 12, 2011 11
  29. 29. WiFi  loss  rate  is  higher Loss  rate  =  Fracon  of  packets  lost  at  10  probes/sec Cumulative fraction 28%   WiFi 8%   3G 11Tuesday, April 12, 2011 11
  30. 30. WiFi  (802.11b)  throughput  is  lower Throughput  =  Total  data  received  per  second WiFi Cumulative fraction 3G Upstream WiFi Cumulative fraction 3G Downstream 12Tuesday, April 12, 2011 12
  31. 31. WiFi  (802.11b)  throughput  is  lower Throughput  =  Total  data  received  per  second WiFi Cumulative fraction 3G Upstream WiFi Cumulative fraction 3G Downstream 12Tuesday, April 12, 2011 12
  32. 32. WiFi  (802.11b)  throughput  is  lower Throughput  =  Total  data  received  per  second WiFi Cumulative fraction 3G Upstream 0.35 WiFi Cumulative fraction 3G Downstream 12Tuesday, April 12, 2011 12
  33. 33. WiFi  (802.11b)  throughput  is  lower Throughput  =  Total  data  received  per  second WiFi Cumulative fraction 3G Upstream 0.35 0.72 WiFi Cumulative fraction 3G Downstream 12Tuesday, April 12, 2011 12
  34. 34. WiFi  (802.11b)  throughput  is  lower Throughput  =  Total  data  received  per  second WiFi Cumulative fraction 3G Upstream 0.35 0.72 WiFi Cumulative fraction 3G Downstream 12Tuesday, April 12, 2011 12
  35. 35. Implicaons  of  measurement  study Strawman  augmentaon:  Use  WiFi  when   available Can  offload  only  ~11%  of  the  me Can  hurt  applicaons  because  of  WiFi’s  higher  loss   rate  and  lower  throughput 13Tuesday, April 12, 2011 13
  36. 36. Key  ideas  in  Wiffler Increase  savings  for  delay-­‐ Reduce  damage  for  delay-­‐ tolerant  applicaons sensive  applicaons Problem:  Using  WiFi  only   Problem:  Using  WiFi   when  available  saves   whenever  available  can   ligle  3G  usage hurt  applicaon  quality Soluon:  Exploit  delay-­‐ Soluon:  Fast  switch  to  3G   tolerance  to  wait  to   when  WiFi  delays   offload  to  WiFi  when   availability  predicted exceed  threshold 14Tuesday, April 12, 2011 14
  37. 37. Predicon-­‐based  offloading D  =  Delay-­‐tolerance  threshold  (seconds) S  =  Data  remaining  to  be  sent  (bytes) Each  second, 1. If  (WiFi  available),  send  data  on  WiFi   2. Else  if  (W(D)  <  S),  send  data  on  3G 3. Else  wait  for  WiFi. 15Tuesday, April 12, 2011 15
  38. 38. Predicon-­‐based  offloading D  =  Delay-­‐tolerance  threshold  (seconds) S  =  Data  remaining  to  be  sent  (bytes) Each  second, 1. If  (WiFi  available),  send  data  on  WiFi   2. Else  if  (W(D)  <  S),  send  data  on  3G Predicted  WiFi   transfer  size  in   3. Else  wait  for  WiFi. next  D  seconds   15Tuesday, April 12, 2011 15
  39. 39. Predicng  WiFi  capacity History-­‐based  predicon  of  #  of  APs  using  last  few   AP  encounters   WiFi  capacity  =  (expected  #APs)  x  (capacity  per  AP) Simple  predictor  yields  low  error  both  in  Amherst   and  Seagle 16Tuesday, April 12, 2011 16
  40. 40. Predicng  WiFi  capacity History-­‐based  predicon  of  #  of  APs  using  last  few   AP  encounters   WiFi  capacity  =  (expected  #APs)  x  (capacity  per  AP) Simple  predictor  yields  low  error  both  in  Amherst   and  Seagle Negligible  benefits  with  more  sophiscated  predicon,  eg   future  locaon  predicon  +  AP  locaon  database 16Tuesday, April 12, 2011 16
  41. 41. Fast  switching  to  3G Problem: WiFi  losses  bursty  =>  high  retransmission  delay Approach: If  no  WiFi  link-­‐layer  ACK  within  50ms,  switch  to  3G Else,  connue  sending  on  WiFi 17Tuesday, April 12, 2011 17
  42. 42. Wiffler  implementaon Wiffler   proxy § Predicon-­‐based  offloading  upstream  +  downstream §  Fast  switching  only  upstream Ø Implemented  using  signal-­‐upon-­‐ACK  in  driver 18Tuesday, April 12, 2011 18
  43. 43. Evaluaon  Roadmap Predicon-­‐based  offloading Deployment  on  20  DieselNet  buses  in  150  sq.  mi   region  around  Amherst Trace-­‐driven  evaluaon  using  throughput  data Fast  switching Deployment  on  1  car  in  Amherst  town  center Trace-­‐driven  evaluaon  using  measured  loss/delay   trace  using  VoIP-­‐like  probe  traffic 19Tuesday, April 12, 2011 19
  44. 44. Deployment  results Data  offloaded  to  WiFi Wiffler’s  predicon-­‐based  offloading 30% WiFi  when  available 10% File  transfer  size:  5MB;  Delay  tolerance:  60  secs;     Inter-­‐transfer  gap:  random  with  mean  100  secs 20Tuesday, April 12, 2011 20
  45. 45. Deployment  results Data  offloaded  to  WiFi Wiffler’s  predicon-­‐based  offloading 30% WiFi  when  available 10% File  transfer  size:  5MB;  Delay  tolerance:  60  secs;     Inter-­‐transfer  gap:  random  with  mean  100  secs %  Cme  good  voice  quality   Wiffler’s  fast  switching 68% WiFi  when  available  (no  switching) 42% VoIP-­‐like  traffic:  20-­‐byte  packet  every  20  ms   20Tuesday, April 12, 2011 20
  46. 46. Trace-­‐driven  evaluaon Parameters  varied Workload,  AP  density,  delay-­‐tolerance,  switching  threshold Strategies  compared  to  predicon-­‐based  offloading: WiFi  when  available Adapted-­‐Breadcrumbs:  Future  locaon  predicon  +  AP  locaon   database Oracle  (Impraccal):  Perfect  predicon  w/  future  knowledge 21Tuesday, April 12, 2011 21
  47. 47. Wiffler  increases  data  offloaded  to  WiFi Workload:  Web  traces  obtained  from  commuters   22Tuesday, April 12, 2011 22
  48. 48. Wiffler  increases  data  offloaded  to  WiFi Workload:  Web  traces  obtained  from  commuters   14% WiFi  when   available  yields   ligle  savings 22Tuesday, April 12, 2011 22
  49. 49. Wiffler  increases  data  offloaded  to  WiFi Workload:  Web  traces  obtained  from  commuters   Wiffler  close  to   42% Oracle 14% 22Tuesday, April 12, 2011 22
  50. 50. Wiffler  increases  data  offloaded  to  WiFi Workload:  Web  traces  obtained  from  commuters   Wiffler  close  to   42% Oracle Sophiscated   14% predicon  yields   negligible  benefit 22Tuesday, April 12, 2011 22
  51. 51. Wiffler  increases  data  offloaded  to  WiFi Workload:  Web  traces  obtained  from  commuters   Wiffler  close  to   42% Oracle Sophiscated   14% predicon  yields   negligible  benefit Wiffler  increases  delay  by  10  seconds  over  Oracle.   22Tuesday, April 12, 2011 22
  52. 52. Even  more  savings  in  urban  centers 23Tuesday, April 12, 2011 23
  53. 53. Fast  switching  improves  quality  of   delay-­‐sensive  applicaons 24Tuesday, April 12, 2011 24
  54. 54. Fast  switching  improves  quality  of   delay-­‐sensive  applicaons 58% 24Tuesday, April 12, 2011 24
  55. 55. Fast  switching  improves  quality  of   delay-­‐sensive  applicaons 58% 40% 24Tuesday, April 12, 2011 24
  56. 56. Fast  switching  improves  quality  of   delay-­‐sensive  applicaons 73% 58% 40% 24Tuesday, April 12, 2011 24
  57. 57. Fast  switching  improves  quality  of   delay-­‐sensive  applicaons 73% 58% 40% 30%  data  offloaded  to  WiFi  with  40ms  switching  threshold 24Tuesday, April 12, 2011 24
  58. 58. Future  work Reduce  energy  to  search  for  usable  WiFi Improve  performance/usage  by  predicng  user   accesses  to  prefetch  over  WiFi Incorporate  evolving  metrics  of  cost  for  3G  and   WiFi  usage 25Tuesday, April 12, 2011 25
  59. 59. Summary Augmenng  3G  with  WiFi  can  reduce  pressure  on   cellular  spectrum Measurement  in  3  cies  confirms  WiFi  availability  and   performance  poorer,  but  potenally  useful Wiffler:  Predicon-­‐based  offloading  and  fast  switching   to  offload  without  hurng  applicaons 26Tuesday, April 12, 2011 26
  60. 60. Summary Augmenng  3G  with  WiFi  can  reduce  pressure  on   cellular  spectrum Measurement  in  3  cies  confirms  WiFi  availability  and   performance  poorer,  but  potenally  useful Wiffler:  Predicon-­‐based  offloading  and  fast  switching   to  offload  without  hurng  applicaons Questions? 26Tuesday, April 12, 2011 26
  61. 61. Thank you!Tuesday, April 12, 2011 27

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