Towards efficient contentdissemination over disruption

tolerant networksPhD Thesis

Candidate: Amir Krifa, INRIA Supevisor: Chadi Barakat, INRIA

Monday, April 23 2012

Mobile Networking Traffic Growth

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Generation of unprecedented amounts of mobile data

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

Second class customer

Shift

First class customer

Complementary architecture ?

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

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DTNs: Not as futuristic as it sounds !

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World’s First Flying File-Sharing Drones in Action @

GLOW Festival 2011 Netherlands

Wildlife tracking systems:

ZebraNet, Env. Monitoring, etc

Challenges� Challenges:

Disruption and dynamic environment -> long-term storage + replication (Routing Algorithms: Global Optimal, Epidemic, Spry and Wait, etc …)

Long-term storage + replication

– -> buffers congestion (Drop Policies: Drop Oldest, Drop Last …)

– -> lack of Bandwidth (Scheduling: FIFO …)

Mobile devices controlled by rational people -> selfishness

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RAPIDBy Levine et al.

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

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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

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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 …

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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 :

K(t)

1i

K(t)

1i 1L)i(Tim

i)Ri(Tiλnexp1*1L)i(Tim1iPDR

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Message i will be delivered

Message i has been already delivered

Message i is not delivered yet

At least one copy of message i Will be delivered

Case of delivery rate We differentiate:

))i(TiΔ(n*k(t)

1i )i(TiniPΔ(DR)

GBSD (DR): The best message to drop is the one having the minimum partial derivative:

And the message to schedule first is the one maximizing it

i)Ri(TiλnexpiλR1L)i(Tim1

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))i(TiΔ(n -1 : drop 0 : no action+1 : replication

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).

Case of delivery delay

1L

)i(Tim1)λi(T

2in1

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GBSD (DD): The best message to drop is the one having the minimum partial derivative:

And the message to schedule first is the one maximizing it

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

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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

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Distributed version (DR)� Suppose m and n follow two random variables M and N

Estimated delivery rate = Mean delivery rate

1L

M(T)iλN(T)Rexp11L

M(T)1E1L(T)m

i(T)Rnλexp11L(T)m1 ˆˆˆ

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We set the estimator of m to its expectation (justified by a Gaussion distribution)

TME(T)m(T)m̂

Distributed version:Message utility expressions

N(T)iλRexp1LM(T)1EiλR

(T)m1L1Lλ

2

N(T)M(T)1LE

For the delivery rate:

For the delivery delay:

Expectation calculated by summing over old messages

Histor

y Base

d SD (HBSD)

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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

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Validation Setup

Mobility model KAIST Random WaypointSimulation duration (h): 24 7Simulated Surface (km2): - 3*3

Number of nodes: 50 70Average speed (m/s) : 2 -TTL (h) : 4 1Interval CBR (s) (10/TTL): 1440 360

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

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Delivery Rate

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Almost 60% gain over RAPID

Random Waypoint KAIST Traces

HBSD outperforms existing protocols (RAPID and Epidemic based on FIFO/drop-tail) and performs close to the optimal GBSD

� 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

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For a lightly loaded network, things are easier and simple policies can be applied.

How HBSD utilities look like ?

How HBSD utilities look like ?

� We fix the number of sources to 50 (high congestion regime)

prefer younger ones

help the message over younger ones

penalize – help - penalize

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For a highly loaded network (complex function)

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/

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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

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Previous context: Node Centric

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� Node Centric vs Content Centric communications

N1

1

- Source: N1- Destination: N5

1

1

11

2

N3

N2 N4

N5

2

2

2

- Source: N2- Destination: N4

New context: Content centric

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� Node Centric vs Content Centric communications

Track – 1Track – 2

…

Madonna M. Album

Madonna M. Album

Madonna M. Album

Muse M. Album

Track – 1Track – 2

…

Muse + Madonna M. Album

Track – 1Track – 2

…

Track – 1Track – 2

…

Make Everybody happy ? -> Store Local and Foreign Channels !

Track – 3Track – 4

…

Muse M. Album

Muse + Madonna M.

Album

Muse + Madonna M.

Album

Selfish user !

Store Local and Foreign Channels !

Block Selfish users ! (TFT)

[Question]: which channels and how much of each should a node carry in its buffer, so as to maximize its future reward ??

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

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Node Storage

Channel 1Channel 2Channel 3Channel 4

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

� MobiTrade approximates the Optimal U. based on the amount of exchanged content per channel @ each meeting while ensuring that selfish users are blocked

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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.

BXXBα

jj

i*i

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

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Collaborative experimental scenario

Simulation Scenario

Nbr. Of Users: 50Requested CH(s)/User: 2

Size of CH(s): 20

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Average Delivery Rate (DR): amount of content received for channels a node requested / total amount of content generated for these channels

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 .

Compare to Podcasting (PodNet project)

How MobiTrade performs in a Collaborative scenario ?

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� MobiTrade efficiently outperforms the two versions of Podcasting� TFT causes a drop in performance among CU

Almost 2x gain

Drop of 6%

Importance of FC(s)

Experimental scenarios including selfish users

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Scenarios SS1 SS2

Nbr. Of Users: 40 CU + 10 SU 40 CU + 10SU

Requested CH(s): CU: 2/20 – SU: 2/10 (SU and CU channels differ)

CU, SU: 2/20 (among same channels)

Size of CH(s): CU: 20 – SU: 40 CU, SU: 20

We deem such scenarios as the norm ratherthan the exception in the real world

Does MobiTrade keep the system resources safe ?

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� Enabling the TFT mechanism blocks selfish users and makes MobiTrade re-dispatch/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 collaborative users

Impact on selfish users

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SS2: each user ask for 2/20 channels

� 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.

Impact on SU: Drop by up to 2x

No Impact on CU

Does MobiTrade keep the system resources safe ?

Implementation / Web page

� MobiTrade available for the Android platform

� Code, papers, presentations are available at:

http://planete.inria.fr/MobiTrade/

App Screenshots:

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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

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Conclusion

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� 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

� Validation via simulations based on synthetic mobility models and real mobility traces

� Implementation on real word environments (DTN2 and Android)

GBSD/HBSD MobiTrade

Perspectives

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Non altruistic

Point-to-multipointPoint-to- point

MobiTrade

GBSD/HBSDOngoing …

Collaborative

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 ...

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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.

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Thank you !

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