1. 1 Resource sharing in mobile wireless networks Maria Papadopouli Computer Science Department Columbia University http://www.cs.columbia.edu/~maria

2. 2 Academic background Columbia University Ph.D. candidate Fall 1996- advisor Prof. Golubchik Fall 1996–1998 advisor Prof. Schulzrinne Fall 1998- New York University M.S. Computer Science May 1994 University of Crete B.S. Computer Science June 1992

3. 3 References on resource sharing in mobile ad hoc networks 1.“ Effects of power conservation, wireless coverage & cooperation on data dissemination among wireless devices “, ACM MobiHoc 2001 2.“ Performance analysis of 7DS a data dissemination & prefetching tool for mobile users”, IEEE Sarnoff 2001, best paper/poster award 3.“7DS in mobile ad hoc networks”, Globecom 2000 4.“Performance of data dissemination among mobile devices”, journal submission, 2002 5.“ Design & implementation of a P2P data dissemination & prefetching tool for mobile users ”, Metro 2001 6.“Network connection sharing in ad hoc wireless network among collaborative hosts”, Nossdav 1999 with Prof. Schulzrinne

4. 4 References on video on demand 7."A Scalable Video on Demand server for a Dynamic Heterogeneous Environment", Lecture Notes in Computer Science, Springer 1998 8."Support of VBR Video Streams Under Disk Bandwidth Limitations", ACM SIGMETRICS Performance Evaluation Review 1997 9. (with also J.C-S. Lui), "A survey of approaches to fault tolerant design of video on demand servers: Techniques, analysis and comparison", Special issue of Parallel Computing Journal on Parallel Data Servers and Applications 1998 with Prof. Golubchik

5. 5 Outline Introduction –Background on wireless data access –Motivation –Overview of 7DS Performance analysis on 7DS Conclusions Future work

6. 6 Background Fast growth in pervasive computing devices Fast wireless data services growth Base stations for wireless WAN will not keep pace – Regulatory, environmental & cost barriers for a dense deployment Users experience intermittent connectivity & limited data access

7. 7 Mobile information access Dependency on infrastructure : Wireless WAN eg 802.11, 3G, CDPD, GSM, Bluetooth, Ricochet Infostations (Rutgers) –When a client is in the proximity of the server, it access the data Peer-to-Peer –Routing in mobile, ad hoc & sensor networks

8. 8 Mobile information access Interactivity model : Synchronous –Users directly access or request the data Asynchronous (using prefetching) –Hoarding (Coda [CMU], Seer [UCLA])

9. 9 Limitations of infostations & wireless WAN No communication infrastructure eg field operation missions, tunnels, subway Emergency Overloaded Expensive Wireless WAN access with low bit rates & high delays

10. 10 Limitations of ad hoc networks All hosts cooperative Complete path for the communication of two hosts Host A Host B

11. 11 Limitations of hoarding Only files Files exist prior to disconnection No dynamic generated information

12. 12 Wireless data services Delay tolerant Location-dependent services User location hints at data needs Overhead to discover, access & update local data

13. 13 Challenge Accelerate data availability & enhance dissemination & discovery of information under bandwidth changes & intermittent connectivity to the Internet due to host mobility considering power, bandwidth & memory constraints of hosts

14. 14 Our Approach Increase data availability by enabling devices to share resources –Information sharing –Message relaying –Bandwidth sharing Self-organizing No infrastructure Exploit host mobility

15. 15 Outline Introduction –Background on wireless data access –Motivation –Overview of 7DS Simulations & Analysis on 7DS –Information dissemination –Message relaying –Bandwidth sharing Conclusions Future work

16. 16 7DS Application Zero infrastructure Relay, search, share & disseminate information Generalization of infostation Sporadically Internet connected Coexists with other data access methods Communicates with peers via a wireless LAN Power/energy constrained mobile nodes

17. 17 Examples of services using 7DS schedule info WAN autonomous cache news events in campus, pictures where is the closest Internet café ? service location queries traffic, weather, maps, routes, gas station pictures, measurements

18. 18 Information sharing with 7DS Host B Host C data cache hit cache miss data Host A query WAN Host A Host D query WLAN

19. 19 7DS options Forwarding Host AHost B query FW query Host C time Querying active (periodic) passive Power conservation on off time communication enabled Cooperation Server to client Peer to peer  server to client only server shares data no cooperation among clients fixed info server (infostation model) mobile info server  peer to peer data sharing among peers

20. 20 Outline Introduction Simulations & Analysis on 7DS –Information dissemination –Message relaying –Bandwidth sharing wireless LAN video on demand environment Conclusions Future work

21. 21 Simulation environment pause time 50 s mobile user speed 0.. 1.5 m/s host density 5.. 25 hosts/km 2 wireless coverage 230 m (H), 115 m (M), 57.5 m (L) ns-2 with CMU mobility, wireless extension & randway model dataholder querier randway model wireless coverage

22. 22 Simulation environment pause time 50 s mobile user speed 0.. 1.5 m/s host density 5.. 25 hosts/km 2 wireless coverage 230 m (H), 115 m (M), 57.5 m (L) ns-2 with CMU mobility, wireless extension pause 1m/s mobile host data holder querier wireless coverage

23. 23 Simulation environment pause time 50 s mobile user speed 0.. 1.5 m/s host density 5.. 25 hosts/km 2 wireless coverage 230 m (H), 115 m (M), 57.5 m (L) ns-2 with CMU mobility, wireless extension v1v1 v2v2 v3v3 wireless coverage data

24. 24 Dataholders (%) after 25 min high transmission power 2 Fixed Info Server Mobile Info Server P2P

25. 25 Scaling properties of data dissemination 2 km 1 km If cooperative host density & transmission power are fixed, data dissemination remains the same R R wireless coverage

26. 26 Scaling properties of data dissemination (cont’d) R R/2 For fixed wireless coverage, the larger the density of cooperative hosts, the more efficient the data dissemination wireless coverage

27. 27 Average delay (s) vs. dataholders (%) one server in 2x2 high transmission power 4 servers in 2x2 medium transmission power Fixed Info Server

28. 28 Average Delay (s) vs Dataholders (%) Peer-to-Peer schemes medium transmission power high transmission power

29. 29 Scaling properties of data dissemination (cont’d) L r R L wireless coverage of info server v x x r/2 R/2 v x x

30. 30 Modeling Fixed Info Server as diffusion-controlled process trapping model with particles C and T (traps) particles C perform random walk in 2D space particles T static, randomly distributed in space of infinite capacity particles T absorb C when C step onto them survival probability  n at long times n log (  n )  -A  n querier  particle C fixed info server  trap trapping  receiving data C T

31. 31 Fixed Info Server simulation and analytical results Probability a host will acquire data by time t follows 1-e -a  t high transmission power

32. 32 Outline Introduction –Background on wireless data access –Motivation –Overview of 7DS –Performance analysis on 7DS –Information dissemination –Message relaying –Network connection sharing Conclusions Future work

33. 33 Message relaying with 7DS Host B Message relaying Host A messages Gateway WAN Host A WLAN

34. 34 Message relaying Take advantage of host mobility to increase throughput Hosts buffer messages & forward them to a gateway Hosts forward their own messages to cooperative relay hosts –Restrict number of times hosts forwards

35. 35 2 Messages (%) relayed after 25 min (average number of buffered messages : 5)

36. 36 Outline Introduction –Background –Motivation –Overview of the system Performance analysis –Information dissemination –Message relaying –Network connection sharing Conclusions Future work

37. 37 Network connection sharing WAN Wireless LAN Host A Host B Host C Host D Hosts A & B dual-homed They act as gateways to WAN for hosts C & D Host E Host F thin WAN links

38. 38 Network connection sharing protocol WAN Host A Host BHost C Host D Host E 1.C sends request for gateway 2.B & A respond advertising their bandwidth in WAN link 4. C selects least loaded gateway (eg A) 5. A  C admission control WLAN thin wireless WAN links

39. 39 Benefits using network connection sharing Statistical multiplexing for bursty traffic Increase bandwidth utilization of the WAN links –80% bandwidth utilization for Pareto traffic –Load balancing across gateways For shared data applications : –Reduction of replicated data –Increase quality of service

40. 40 Outline Introduction –Background on wireless data access –Motivation –Overview of the system Performance analysis –Information dissemination –Message relaying –Network connection sharing Conclusions Future work

41. 41 Conclusions  Dominant parameters: density of cooperative hosts wireless coverage density of cooperative hosts & their mobility  For fixed cooperative hosts density & transmission power : scale area performance same  For fixed wireless coverage density : Density of cooperative host  performance 

42. 42 Conclusions (cont’d)  Probability a host will acquire data by time t in Fixed Info Server : 1-e -a  t Peer-to-Peer : 1-e -at  Message relaying is beneficial : Probability a message will reach the Internet  Utilization of available throughput  by taking advantage of host mobility

43. 43 Future work Location-dependent applications & services Actual traces & models for user mobility, access patterns & data locality Enhanced power conservation mechanism Security & micro-payment issues Extension of network connection protocol Generalization of diffusion models for P2P Adaptive scalable algorithms for information discovery

44. 44 Summary of contributions in video on demand Novel multimedia retrieval scheduling algorithms In multi-disk environments : adapt to bandwidth changes maximize data retrieval for all streams using replication and multi-resolution In single-disk environments : allocate disk bandwidth in a fair manner

45. 45 Thank you!

46. 46 Future work: short term More on power conservation for data dissemination Peer-to-peer scheme using diffusion controlled processes Prototype –Deployment of 7DS in CU campus & in Bremen –Public release of the code Collaborations –IBM, HP, Bertelsmann & Limewire (Gnutella)

47. 47 Future work : longer term Information discovery & dissemination in pervasive computing –Model & abstractions for the quality of information –Tight energy, bandwidth –Privacy & security for mobile, peer-to-peer applications –Scaling & structural properties

48. 48 Preventing DoS attacks receives query multicast query Host QHost R multicast challenge sends response run non-trivial computational task wait to hear if Q is challenged verifies Q’s answer decides to cooperate

49. 49 Electronic check payment receive e-check verify it is genuine store e-check Host QHost R send data send e-check wait for data from R verify R is known to the bank & authorized for 7ds send credentials

50. 50 Token-based payment receive query Host Q Host R send data verify R’s public key wait for data from R check token counter send public key with report form query send query decrease counter send ack increase token counter decrease counter send nack increase token counter send data

51. 51 Information discovery & dissemination in pervasive computing Query & data locality No need of infrastructure — use 7DS Query routing required Use infrastructure of gateways that create peer-to-peer overlay hierarchies in self-organizing manner based on query demand & resources [ Castro, Greenstein, Muntz, Bisdikian, Kermani, Papadopouli “Locating Application Data Across Service Discovery Domains”, MOBICOM’01]

52. 52 7DS Implementation Cache manager (3k lines) GUI server (2k lines) HTTP client & methods (24k lines) Proxy server (1k lines) UDP multicast & unicast (1k) Web client & server (2k) Jar files used (xerces, xml,lucene, html parcer)

53. 53 2 Message relayed to gateway after 25 min

54. 54 Network connection sharing summary 1)Requests for network connection 3)Gateway selection Load balancing criteria 2)Advertisement of gateway availability 4)Admission control using Measured sum [Jamin et al] u   v+r v: measured load r: (peak) rate requested u:utilization target  :bandwidth of WAN link Gateway Client

55. 55 Gateway selection mechanism Load balancing criteria Reduction of the maximum difference in the average load over an interval  across the gateways : max i {L i (  )}-min i {L i (  )}/  L i (  ): average traffic measured at gateway i over interval  Greedy algorithm: Choose the least loaded gateway

56. 56 Network connection sharing Bandwidth Utilization (%) Pkt dropping rate (%) Load balancing criteria (%) Exponential660.0022 Pareto8192 Pareto & exponential: 312 s(ON), 325s (OFF) Pareto, shape par. : 1.2 Flows: 64kb/s, 0.6 s int., avg hold time 5 min Perfect load balancing: 0%

57. 57 Pareto traffic measurement policy T(s), S(s)Link Utilization(%) Pkt loss Rate (%) 60, 400 31 0.09 30, 400 37 0.2 3, 400 81 10 Larger T higher measured load more conservative admission

58. 58 Information discovery & dissemination in pervasive computing Without infrastructure : –7DS exploits query & data object locality & host mobility –Cooperation among hosts based on resources With infrastructure : –Gateways create peer to peer overlay hierarchies in self-organizing manner –Participate based on query demand & resources Castro,Greenstein,Muntz (UCLA), Bisdikian,Kermani(IBM), Papadopouli(Columbia Un.), “Locating Application Data Across Service Discovery Domains”, MOBICOM’01

59. 59 Information discovery in pervasive computing Dynamic nature of the environment: uncertainty, errors, timeliness & redundancy Local autonomy –Partial knowledge, local decisions to achieve a global effect Self-organization to minimize administration overhead Adaptive, scalable algorithms & protocols Castro, Greenstein, Muntz (UCLA), Bisdikian, Kermani (IBM), Papadopouli (Columbia Un.), “ Locating Application Data Across Service Discovery Domains”, MOBICOM 2001.

60. 60 Epidemic model Carrier is “infected”, hosts are “susceptible” Transmit to any give host with probability ha+o(h) in interval h Pure birth process T=time until data has spread among all mobiles E[T]=1/a  i=1 N-1 i(N-1) 1

61. 61 7DS implementation Initial Java implementation on laptop Compaq Ipaq (Linux or WinCE) Inhand Electronics ARM RISC board –Low power –PCMCIA slot for storage, network or GPS

62. 62 Mobility models User mobility : Randway Random direction Boundless simulation area Gauss-Markov with history of previous move Group mobility Column mobility Pursue mobility Nomadic community mobility

63. 63 Subway model Passengers arrive at subway stations –Poisson process 1/  1-3min –ride : 2-6 stops –1 min to leave the platform Subway line –10 stops –Train with 6 cars –Arrives at a stop every 5 minutes Percentage of dataholders after they leave the subway for 1/ = 3 min is 65%

64. 64 Types of attacks in ad hoc networks Basic mechanisms MAC layer Routing mechanisms –Malicious users agree to forward messages but fail to do so –False routing information messages –Selfishness & service enforcement issues Security mechanisms Distributed trusted server under the control of malicious party Public key maliciously replaced

65. 65 Service enforcement Lock out mechanism for selfish or misbehaving users –Denial of service attacks –Locked out node moves away where his behavior is not reported Virtual micro currency mechanism –Incentives to cooperate –Discouraged from overloading the network terminodes.org (EPFL), mojonation.net

66. 66 Virtual micro currency Nodes remunerate each other for the services they provide to each other terminodes.org (EPFL), mojonation.net

67. 67 Information discovery & dissemination in pervasive computing Dynamic nature of the environment Uncertainty, errors, timeliness & redundancy Local autonomy Partial knowledge, local decisions to achieve a global effect Self-organization M inimize administration overhead Adaptive, scalable algorithms & protocols

68. 68 Wireless WAN access Location whatcost UK3G$590/person Germany3G$558/person Italy3G$200/person New YorkVerizon (20MHz) $220/customer Spectrum is very expensive 3G bandwidth is very low (64kbs)

69. 69 Avantgo: wireless service provider

70. 70 Vindigo: wireless service provider

71. 71 NYC wireless public infrastructure