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Towards efficient content
dissemination over disruption
tolerant networks
PhD Thesis

Candidate: Amir Krifa, INRIA
Supevis...
Mobile Networking Traffic Growth

Shift
Access to novel applications (social
networks, blogs, music …)

Generation of unpr...
The DTN concept
Take advantage of increasing mobile nodes resources
Rely on nodes mobility to route messages through
disco...
DTNs: Not as futuristic as it
sounds !

World’s First Flying FileSharing Drones in Action @
GLOW Festival 2011
Netherlands...
Challenges
Challenges:
 Disruption and dynamic environment

-> long-term storage +

replication (Routing Algorithms: Glob...
Outline of the talk
Point-to-point (Node Centric) communications
 Optimal solution that requires global knowledge (GBSD)
...
Outline of the talk
Point-to-point (Node Centric) communications
 Optimal solution that requires global knowledge (GBSD)
...
Methodology
Suppose first global knowledge
Take a global routing metric as the delay or delivery rate
Find what is the bes...
Case of delivery rate
Message has the same limited lifetime (TTL)
Suppose global knowledge on m and n
Assumption: meeting ...
Case of delivery rate
 We differentiate:
k(t) P
i * Δ(n (T ))
Δ(DR)  
i i
i1 ni(Ti)

Δ(n (T ))
i i

-1 : drop
0 : no...
Case of delivery delay
 GBSD (DD):
The best message to drop is the one having the minimum
partial derivative:



1 1...
Outline of the talk
Point-to-point (Node Centric) communications
 Optimal solution that requires global knowledge (GBSD)
...
Distributed version:
How to calculate n and m ?
n = number of copies of a message
m = number of nodes that have seen the m...
Distributed version (DR)
Suppose m and n follow two random variables M and N

Estimated delivery rate = Mean delivery rate...
Distributed version:
Message utility expressions
(H
BS

D)

 For the delivery rate:

M(T)  exp  λR N(T)



λR E...
Outline of the talk
Point-to-point (Node Centric) communications
 Optimal solution that requires global knowledge (GBSD)
...
Validation Setup
 DTN architecture added to the NS-2 simulator
 Random Waypoint and KAIST real mobility trace
 Wireless...
Delivery Rate
 HBSD outperforms existing protocols (RAPID and Epidemic based on
FIFO/drop-tail) and performs close to the...
How HBSD utilities look like ?
Reduce the number of sources to 15 and decrease the CBR rate of
sources from 10 to 2 messag...
How HBSD utilities look like ?
We fix the number of sources to 50 (high congestion regime)
help the message
over younger o...
Implementation / Web page
And is also available for the DTN2 architecture as an
external router (in C++)
Code has been rec...
Outline of the talk
Point-to-point (Node Centric) communications
 Optimal solution that requires global knowledge (GBSD)
...
Previous context: Node Centric
Node Centric vs Content Centric communications
- Source: N2
- Destination: N4

1
2

N2

N5
...
New context: Content centric
Node Centric vs Content Centric communications
Madonna M. Album
Muse +
Madonna M.
Album

Mado...
Outline of the talk
Point-to-point (Node Centric) communications
 Optimal solution that requires global knowledge (GBSD)
...
MobiTrade
MobiTrade turns each node into a merchant fetching the content that
has the highest chance to be sold to its goo...
Outline of the talk
Point-to-point (Node Centric) communications
 Optimal solution that requires global knowledge (GBSD)
...
Collaborative experimental scenario
 MobiTrade architecture added to the NS-3 simulator
 Synthetic mobility model HCMM
...
How MobiTrade performs in a
Collaborative scenario ?
MobiTrade efficiently outperforms the two versions of Podcasting
TFT ...
Experimental scenarios including
selfish users
Scenarios

SS1

Nbr. Of Users:

40 CU + 10 SU

40 CU + 10SU

CU: 2/20 – SU:...
Does MobiTrade keep the system
resources safe ?
Enabling the TFT mechanism blocks selfish users and makes MobiTrade redisp...
Does MobiTrade keep the system
resources safe ?
When TFT is used, the performance of collaborative users is not harmed,
wh...
Implementation / Web page
MobiTrade available for the Android platform
Code, papers, presentations are available at:
http:...
Outline of the talk
Point-to-point (Node Centric) communications
 Optimal solution that requires global knowledge (GBSD)
...
Conclusion
A deep study of content sharing in DTN(s) for both:
 Point-to-point communication model
 Point-to-multipoint ...
Perspectives
MobiTrade
Non altruistic

GB

SD

Collaborative

/HB

Ongoing …

SD

Point-to- point

Point-to-multipoint

36...
Perspectives
With respect to GBSD/HBSD:
 Tune the utilities of our resources management policies in order to
take into ac...
References
Amir Krifa, Chadi Barakat, Thrasyvoulos Spyropoulos, “MobiTrade: Trading Content in
Disruption Tolerant Network...
Thank you !

39/39
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Towards efficient content dissemination over DTN

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Transcript of "Towards efficient content dissemination over DTN"

  1. 1. Towards efficient content dissemination over disruption tolerant networks PhD Thesis Candidate: Amir Krifa, INRIA Supevisor: Chadi Barakat, INRIA Monday, April 23 2012
  2. 2. Mobile Networking Traffic Growth Shift Access to novel applications (social networks, blogs, music …) Generation of unprecedented amounts of mobile data Complementary architecture ? Second class customer 2/39 First class customer
  3. 3. The DTN concept Take advantage of increasing mobile nodes resources Rely on nodes mobility to route messages through disconnected networks  A node can be a human carrying a laptop or SmartPhone, a bus, a car, etc At the opposite of existing networks, no end-to-end path is required during the communication  Hop-by-Hop networking  Message replication 3/39
  4. 4. DTNs: Not as futuristic as it sounds ! World’s First Flying FileSharing Drones in Action @ GLOW Festival 2011 Netherlands Wildlife tracking systems: ZebraNet, Env. Monitoring, etc 4/39
  5. 5. Challenges Challenges:  Disruption and dynamic environment -> long-term storage + replication (Routing Algorithms: Global Optimal, Epidemic, Spry and Wait, etc …) RAPID By Levine et al.  Long-term storage + replication – -> buffers congestion (Drop Policies: Drop Oldest, Drop Last …) – -> lack of Bandwidth (Scheduling: FIFO …)  Mobile devices controlled by rational people 5/39 -> selfishness
  6. 6. Outline of the talk Point-to-point (Node Centric) communications  Optimal solution that requires global knowledge (GBSD)  Distributed version that works in practice (HBSD)  Validation results Point-to-multi-points (Content Centric) communications  The content centric context  MobiTrade: optimal resources management solution  Validation results Conclusion and Perspectives 6/39
  7. 7. Outline of the talk Point-to-point (Node Centric) communications  Optimal solution that requires global knowledge (GBSD)  Distributed version that works in practice (HBSD)  Validation results Point-to-multi-points (Content Centric) communications  The content centric context  MobiTrade: optimal resources management solution  Validation results Conclusion and Perspectives 7/39
  8. 8. Methodology Suppose first global knowledge Take a global routing metric as the delay or delivery rate Find what is the best policy to drop and schedule  Which message should be dropped/scheduled first and that leads to the best gain in the considered global metric,  Model this gain as a per-message utility function. Try to estimate the global knowledge using global information BUT on old messages … 8/39
  9. 9. Case of delivery rate Message has the same limited lifetime (TTL) Suppose global knowledge on m and n Assumption: meeting times have an exponential tail In case of congestion, the global delivery rate is : At least one copy of message i Will be delivered Message i is not delivered yet m (T ) K(t) K(t) m (T )   i i  * 1 exp  λn (T )R    i i DR   P   1 i i1  L 1   i i i   L 1  i1   Message i will be delivered 9/39 Message i has been already delivered
  10. 10. Case of delivery rate  We differentiate: k(t) P i * Δ(n (T )) Δ(DR)   i i i1 ni(Ti) Δ(n (T )) i i -1 : drop 0 : no action +1 : replication  GBSD (DR): The best message to drop is the one having the minimum partial derivative:   m (T )   1 i i λR exp  λn (T )R    i i i L 1  i     And the message to schedule first is the one maximizing it 10/39
  11. 11. Case of delivery delay  GBSD (DD): The best message to drop is the one having the minimum partial derivative:   1 1 mi(Ti)      n2(T )λ  L 1    i i And the message to schedule first is the one maximizing it  For more details: Amir Krifa, Chadi Barakat, Thrasyvoulos Spyropoulos, “Message Drop and Scheduling in DTNs: Theory and Practice”, in IEEE Transactions on Mobile Computing (TMC). 11/39
  12. 12. Outline of the talk Point-to-point (Node Centric) communications  Optimal solution that requires global knowledge (GBSD)  Distributed version that works in practice (HBSD)  Validation results Point-to-multi-points (Content Centric) communications  The content centric context  MobiTrade: optimal resources management solution  Validation results Conclusion and Perspectives 12/39
  13. 13. Distributed version: How to calculate n and m ? n = number of copies of a message m = number of nodes that have seen the message Flood information on messages (like in RAPID by UMASS)  takes long time to converge  The information is stale by the time it reaches everyone Our solution:  Still flood information on messages  BUT, Estimate n and m at a given elapsed time from what has happened to old messages at the same elapsed time 13/39
  14. 14. Distributed version (DR) Suppose m and n follow two random variables M and N Estimated delivery rate = Mean delivery rate ˆ ˆ m(T)  1 exp  λn(T)R   m(T)  E1 M(T)  1 exp  λN(T)R    M(T)       ˆ     1           i   L 1 i   L 1    L 1   L 1               We set the estimator of m to its expectation (justified by a Gaussion distribution) ˆ m(T)  m (T)  EM T         14/39
  15. 15. Distributed version: Message utility expressions (H BS D)  For the delivery rate: M(T)  exp  λR N(T)    λR E 1    i i   L 1        ist 2 E L 1M(T) N(T) λ L 1 L 1 m (T)             H  For the delivery delay: or y Ba se d SD        Expectation calculated by summing over old messages 15/39
  16. 16. Outline of the talk Point-to-point (Node Centric) communications  Optimal solution that requires global knowledge (GBSD)  Distributed version that works in practice (HBSD)  Validation results Point-to-multi-points (Content Centric) communications  The content centric context  MobiTrade: optimal resources management solution  Validation results Conclusion and Perspectives 16/39
  17. 17. Validation Setup  DTN architecture added to the NS-2 simulator  Random Waypoint and KAIST real mobility trace  Wireless Range=100m,  CBR sources, random sources and destinations,  Each node maintains a buffer with a capacity of 20 messages Mobility model KAIST Random Waypoint Simulation duration (h): Simulated Surface (km2): 24 - 7 3*3 Number of nodes: 50 70 Average speed (m/s) : 2 - TTL (h) : 4 1 1440 360 Interval CBR (s) (10/TTL): 17/39
  18. 18. Delivery Rate  HBSD outperforms existing protocols (RAPID and Epidemic based on FIFO/drop-tail) and performs close to the optimal GBSD Random Waypoint Almost 60% gain over RAPID 18/39 KAIST Traces Close by 14%
  19. 19. How HBSD utilities look like ? Reduce the number of sources to 15 and decrease the CBR rate of sources from 10 to 2 messages/TTL (Low congestion regime) Schedule Youngest First Drop Oldest For a lightly loaded network, things are easier and simple policies can be applied. 19/39
  20. 20. How HBSD utilities look like ? We fix the number of sources to 50 (high congestion regime) help the message over younger ones penalize – help - penalize prefer younger ones For a highly loaded network (complex function) 20/39
  21. 21. Implementation / Web page And is also available for the DTN2 architecture as an external router (in C++) Code has been recently tested in the Scorpion testbed at the University of California Santa Cruz Code, papers, presentations are available at: http://planete.inria.fr/HBSD_DTN2/ 21/39
  22. 22. Outline of the talk Point-to-point (Node Centric) communications  Optimal solution that requires global knowledge (GBSD)  Distributed version that works in practice (HBSD)  Validation results Point-to-multi-points (Content Centric) communications  The content centric context  MobiTrade: optimal resources management solution  Validation results Conclusion and Perspectives 22/39
  23. 23. Previous context: Node Centric Node Centric vs Content Centric communications - Source: N2 - Destination: N4 1 2 N2 N5 N1 N4 2 1 1 1 N3 2 1 2 - Source: N1 - Destination: N5 23/39
  24. 24. New context: Content centric Node Centric vs Content Centric communications Madonna M. Album Muse + Madonna M. Album Madonna + Muse M. Madonna M. Album Album Track – 1 Track – 2 … Track – 1 Track – 2 … Madonna M. Album Muse M. Album Track – 3 Track – 4 … Muse Madonna Muse + M. Album M. Album Track – 1 Track – 2 … Track – 1 Track – 2 … Selfish user ! [Question]: which channels and how Store Local and a node Channels much of each shouldForeign carry in !its! Make Everybody happy ? -> Store Local and Foreign Channels buffer, Selfishto maximize its future Block so as users ! (TFT) reward ?? 24/39
  25. 25. Outline of the talk Point-to-point (Node Centric) communications  Optimal solution that requires global knowledge (GBSD)  Distributed version that works in practice (HBSD)  Validation results Point-to-multi-points (Content Centric) communications  The content centric context  MobiTrade: optimal resources management solution  Validation results Conclusion and Perspectives 25/39
  26. 26. MobiTrade MobiTrade turns each node into a merchant fetching the content that has the highest chance to be sold to its good clients MobiTrade calculates one utility per channel that defines:  The optimal amount of storage to allocate / channel  Drop policy + Scheduling policy Node Storage Xi αi B  B  Xj * j Channel 1 Channel 2 Channel 3 Channel 4 MobiTrade approximates the Optimal U. based on the amount of exchanged content per channel @ each meeting while ensuring that selfish users are blocked  For more details: Amir Krifa, Chadi Barakat, Thrasyvoulos Spyropoulos, “MobiTrade: Trading Content in Disruption Tolerant Networks”, in proceedings of ACM CHANTS, Las Vegas, September 2011. 26/39
  27. 27. Outline of the talk Point-to-point (Node Centric) communications  Optimal solution that requires global knowledge (GBSD)  Distributed version that works in practice (HBSD)  Validation results Point-to-multi-points (Content Centric) communications  The content centric context  MobiTrade: optimal resources management solution  Validation results Conclusion and Perspectives 27/39
  28. 28. Collaborative experimental scenario  MobiTrade architecture added to the NS-3 simulator  Synthetic mobility model HCMM  50 users distributed into 5 groups. The simulation area is divided into a 10*10 grid of cells (5000 meters wide).  Wireless Range = 60m . Simulation Scenario Nbr. Of Users: Requested CH(s)/User: 50 2 Size of CH(s): Compare to Podcasting (PodNet project) 20 Average Delivery Rate (DR): amount of content received for channels a node requested / total amount of content generated for these channels 28/39
  29. 29. How MobiTrade performs in a Collaborative scenario ? MobiTrade efficiently outperforms the two versions of Podcasting TFT causes a drop in performance among CU Drop of 6% Importance of FC(s) Almost 2x gain 29/39
  30. 30. Experimental scenarios including selfish users Scenarios SS1 Nbr. Of Users: 40 CU + 10 SU 40 CU + 10SU CU: 2/20 – SU: 2/10 (SU and CU channels differ) CU, SU: 2/20 (among same channels) CU: 20 – SU: 40 Requested CH(s): Size of CH(s): SS2 CU, SU: 20 We deem such scenarios as the norm rather than the exception in the real world 30/39
  31. 31. Does MobiTrade keep the system resources safe ? Enabling the TFT mechanism blocks selfish users and makes MobiTrade redispatch/reuse the saved resources among the channels shared by collaborative users SS1: CU ask for 2/20 channels and SU ask for 2/10 different channels Impact on selfish users Impact on collaborative users 31/39
  32. 32. Does MobiTrade keep the system resources safe ? When TFT is used, the performance of collaborative users is not harmed, while the one of selfish users drops severely, by up to 2x for a storage of 110 contents. SS2: each user ask for 2/20 channels Impact on SU: Drop by up to 2x No Impact on CU 32/39
  33. 33. Implementation / Web page MobiTrade available for the Android platform Code, papers, presentations are available at: http://planete.inria.fr/MobiTrade/  App Screenshots: 33/39
  34. 34. Outline of the talk Point-to-point (Node Centric) communications  Optimal solution that requires global knowledge (GBSD)  Distributed version that works in practice (HBSD)  Validation results Point-to-multi-points (Content Centric) communications  The content centric context  MobiTrade: optimal resources management solution  Validation results Conclusion and Perspectives 34/39
  35. 35. Conclusion A deep study of content sharing in DTN(s) for both:  Point-to-point communication model  Point-to-multipoint communication model New resources management policies in two versions:  Optimal one that is based on global knowledge  Practical one that efficient approximate the optimal policy GBSD/HBSD MobiTrade Validation via simulations based on synthetic mobility models and real mobility traces Implementation on real word environments (DTN2 and Android) 35/39
  36. 36. Perspectives MobiTrade Non altruistic GB SD Collaborative /HB Ongoing … SD Point-to- point Point-to-multipoint 36/39
  37. 37. Perspectives With respect to GBSD/HBSD:  Tune the utilities of our resources management policies in order to take into account different messages sizes ...  Study and design a congestion level detection mechanism to be able to switch efficiently between resources management policies … With respect to MobiTrade:  Implementing the MobiTrade protocol for other types of devices and experiment with real large scale communities of users...  Consider more complex content structures …  Study of the needed mechanisms to control possible advanced malicious attacks and behaviours that could impair MobiTrade content sharing sessions ... 37/39
  38. 38. References Amir Krifa, Chadi Barakat, Thrasyvoulos Spyropoulos, “MobiTrade: Trading Content in Disruption Tolerant Networks”, in proceedings of ACM Mobicom Workshop on Challenged Networks (CHANTS), Las Vegas, September 2011. Amir Krifa, Chadi Barakat, Thrasyvoulos Spyropoulos, “Message Drop and Scheduling in DTNs: Theory and Practice”, in IEEE Transactions on Mobile Computing. Amir Krifa, Chadi Barakat, Thrasyvoulos Spyropoulos, "Optimal Buffer Management Policy for Delay Tolerant Networks", in proceedings of the 5th IEEE Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON 2008), San Francisco, June 2008. (CA), June 2008. ---- BEST PAPER AWARD Amir Krifa, Chadi Barakat, Thrasyvoulos Spyropoulos, "An Optimal Joint Scheduling and Drop Policy for Delay Tolerant Networks”, in proceedings of the WoWMoM Workshop on Autonomic and Opportunistic Communications, Newport Beach (CA), June 2008. 38/39
  39. 39. Thank you ! 39/39
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