Using redundancy to cope with failures in a delay tolerant network
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Using Redundancy to Cope with Failures in a Delay Tolerant Network. Sushant Jain, Michael Demmer, Rabin Patra, Kevin Fall Source: www.cs.utexas.edu/~lili/ classes/F05/slides/dtn-yogita.ppt . Outline of Discussion . Introduction Erasure Coding Formal Problem Statement Path Failure Models

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Using redundancy to cope with failures in a delay tolerant network l.jpg

Using Redundancy to Cope with Failures in a Delay Tolerant Network

Sushant Jain, Michael Demmer, Rabin Patra, Kevin Fall

Source: www.cs.utexas.edu/~lili/ classes/F05/slides/dtn-yogita.ppt


Outline of discussion l.jpg
Outline of Discussion Network

  • Introduction

  • Erasure Coding

  • Formal Problem Statement

  • Path Failure Models

  • Evaluation

  • Related Work

  • Conclusion

  • Future Development


Introduction l.jpg
Introduction Network

  • Routing in Delay Tolerant Network (DTN) in presence of path failures is difficult

  • Retransmissions cannot be used for reliable delivery

    • Timely feedback may not be possible

  • How to achieve reliability in DTN?

    • Replication, Erasure coding


Erasure coding l.jpg
Erasure Coding Network

  • N block message is encoded into large (>N) number of code blocks.

  • Message can be decoded when fraction 1/r or more blocks are received. Replication factor = r

  • Allocation of code blocks over different links not simple.


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Bernoulli Path Failure, are identical and independent Network

  • Family of allocation strategies is used

    for kth strategy

  • Probability of success of kth strategy



Slide9 l.jpg

Bernoulli Path Failure, are different contd.

Formulation of Mixed Integer Program (MIP)

Objective Function:


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Partial Path Failures contd.

Objective: Maximize Sharpe Ratio



Evaluation l.jpg
Evaluation contd.

  • Three scenarios used for evaluation:

    • DTN routing over data MULEs

      • Path independent, data loss Bernoulli

    • DTN routing over set of city buses

      • Paths dependent, data loss Bernoulli

    • DTN routing large sensor network

      • Partial path failures


Data mule scenario l.jpg
Data MULE Scenario contd.

  • Simulation Setup:

    1km x 1km planar area, source and destination at opposite corners.

    Message size 10KB, Contact bandwidth 100Kbps, Storage capacity of MULE 1MB

    Velocity of MULE 10m/s.

  • Probability of success of ith path is

  • Di is the delay in distribution by ith MULE, T is the message expiration time

pi = Prob(Di ≤ T),


Mule density l.jpg
MULE Density contd.





Bus network scenario l.jpg
Bus Network Scenario contd.

  • Simulation Setup

    • Radio bandwidth 400kbps, radio range 100m

    • 20 messages of size 10kb, sent randomly every hour for 12 hours

    • bus storage 1Mb

    • Message expiration time 6 hours

    • Paths are multi-hop



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Sensor Network Scenario contd.

  • Simulation Setup

    • Nodes placed in 40x16 foot grid, grid size 8ft



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Related Work contd.

  • Portfolio Theory

    • Theory used to optimize the Sharpe-ratio

  • Waterfilling in Gaussian channels

    • Formulation uses convex optimization techniques

  • FEC, Erasure Coding, Internet Routing

    • Choice of erasure code

  • Combinatorial Optimization

    • Computes Prob(Y>c) for a given configuration


Summary l.jpg
Summary contd.

  • Problem of reliable transmission in DTN

  • Replication and erasure code for increasing reliability

  • Formulate the optimal allocation problem

  • Study of this problem for Bernoulli and partial path failures

  • Evaluation of the analysis in three different scenarios


Slide24 l.jpg

  • Strengths contd.:

    • Use of erasure coding and replication in DTN

    • Performed extensive analysis of the optimal allocation problem

    • The idea presented in generic and can be applied in other fields too

  • Weakness:

    • Computations involved are complex and may not feasible

    • The study is applicable for probabilities which remain constant over time

    • In partial path failure analysis, it is assumed that the path probabilities have comparable mean and variance. This might not be always true


Future development l.jpg
Future Development contd.

  • Apply this analysis to other fields such as replication of objects in distributed system

  • Develop an efficient method for allocation in Bernoulli path failures

  • Theoretical analysis for choosing replication factor


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