Using Redundancy to Cope with Failures in a Delay Tolerant Network

1
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

2
Outline of Discussion

• Introduction
• Erasure Coding
• Formal Problem Statement
• Path Failure Models
• Evaluation
• Related Work
• Conclusion
• Future Development

3
Introduction

• 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

4
Erasure Coding

• N block message is encoded into large (gtN) 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.

5
(No Transcript)
6
(No Transcript)
7
Bernoulli Path Failure, are identical and
independent

• Family of allocation strategies is used
• for kth strategy
• Probability of success of kth strategy

8
Bernoulli Path Failure, are identical and
independent contd.
9
Bernoulli Path Failure, are different
Formulation of Mixed Integer Program (MIP)
Objective Function
10
Partial Path Failures
Objective Maximize Sharpe Ratio
11
Markowitz algorithm
12
Evaluation

• 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

13
Data MULE Scenario

• 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),
14
MULE Density
15
Forced Splitting
16
Different Success Probabilities
17
Tolerance to Probability Estimation Errors
18
Bus Network Scenario

• 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

19
Bus Network Scenario contd.
20
Sensor Network Scenario

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

21
Benefits of Erasure coding
22
Related Work

• 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(Ygtc) for a given configuration

23
Summary

• 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

24

• Strengths
• 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

25
Future Development

• 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