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A Voting Scheme for BLOB Replication

A Voting Scheme for BLOB Replication. Preethi Vishwanath Dr. Chris Pollett Dr. Robert Chun Mr. Tuong Truong May 11 th 2007 (10 am – 12 pm). Outline. Brief overview System Design Implementation Phases Bandwidth Usage Byzantine vs. No Replication Strategy Output – Screen shots

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A Voting Scheme for BLOB Replication

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  1. A Voting Scheme for BLOB Replication Preethi Vishwanath Dr. Chris Pollett Dr. Robert Chun Mr. Tuong Truong May 11th 2007 (10 am – 12 pm)

  2. Outline • Brief overview • System Design • Implementation Phases • Bandwidth Usage • Byzantine vs. No Replication Strategy • Output – Screen shots • Comparison with other models

  3. Distance Calculation • Distance Calculation • Distance between 2 adjacent nodes is 1 • Weight = ( distance ) * ( number of accesses )

  4. Our Model U1 U9 M1 U2 U8 M2 M8 2 * 1 2* 1 1 * 2 4 * 0 B1 M3 M7 4 * 1 2 * 3 U3 U7 1 * 2 1* 2 M4 B1 M6 4 * 4 2 * 3 U10 U4 U6 M5 Distance U5 Accesses

  5. Setup

  6. Operational Flow

  7. Parse XML Store C-XML Parser (expat) <node-details> <mesh-id> 0 </mesh-id> <blob> 0 </blob> <mesh-id> 1 </mesh-id> <blob> 2 </blob> </node-details> 0 0 1 2 Database XML Document

  8. BLOB access frequency For each BLOB Randomly pick a voter For each BLOB accessed by the voter selected , weight = access * distance BLOB B1 Data Generation

  9. Phase 2 • Pick Candidate for replication ? for each BLOB { ∑voters weight > α fraction ; }

  10. Basic - Byzantine Agreement • For each BLOB being replicated • Initial vote cast ( self machine) • Each node • Interested • Tabulate votes • Toss a coin • heads • Change vote for next round if more than 5/8 of the nodes agree on a common machine • Else change the vote to the majority machine. • tails • Change vote for next round if more than 6/8 of the nodes agree on a common machine. • Else change the vote to the majority machine. • Not interested • Convert vote to majority vote • Faulty • Randomly cast a vote. • Consensus = 7/8 of the voters agree on the same machine for replication. weight = distance * access freq

  11. User 6 = 3 * 2 = 6 User 5 = 2 * 3 = 6 User 4 = 3 * 2 = 6 Since weight of all three users = 6 units Not possible to decide where to replicate Disadvantage Tree Model 191 M1 B1 M6 M2 3*2 168 M5 M3 2*3 M4 0 3*2

  12. Possible Outcomes • BLOB replicated to new location • No BLOB Replication • No Candidates • No Agreement reached.

  13. Phase 4 - Simulator • Extract output information weight = distance * number of accesses

  14. Output Screens • Output • Byzantine Agreement – Tree Model • No Candidates for replication • No Byzantine Agreement reached, hence no replication.

  15. No Candidates for replication

  16. No Byzantine Agreement BLOB#, OLD_LOC, NEW_LOC 0 , 1 , 1 1 , 5 , 5 2 , 7 , 7 3 , 4 , 4 BLOB#, CUM_OLD_BW, CUM_NEW_BW 0 , 63, 63 1 , 22, 22 2 , 20, 20 3 , 41, 41

  17. Bandwidth Usage – Ring Topology

  18. Bandwidth Usage – Tree Model

  19. Comparing Byzantine replication with No-replica strategy

  20. Comparison • Continuous Broadcast Replication Wait for time t, and then broadcast all the BLOBs requested to all the voters. • Static Access Frequency Replication Replicate a copy to the machine which accesses maximum number of times. • Dynamic Access Frequency Neighborhood Model • Perform SAF • If the replicated copy is adjacent to the original copy, then replicate on the next highest access.

  21. Bandwidth Comparison Cost-based comparison Continuous Broadcast Replication

  22. Static Access Frequency ReplicationBandwidth Comparison/User

  23. Static Access Frequency ReplicationCumulative bandwidth

  24. Dynamic Access Frequency Neighborhood ReplicationBandwidth Replication

  25. Dynamic Access Frequency Neighborhood ReplicationCumulative Bandwidth

  26. References • [1] Zune, from Wikipedia the free encyclopedia. http://en.wikipedia.org/wiki/Zune • [2] D.J. Baker, J. Wieselthier and A. Ephremides, “A distributed algorithm for scheduling the activation of links in a self-organizing, mobile, radio network”, Proceedings of IEEE ICC’82, 1982. • [3] T Hara, N Murakami, S. Nishio, “Replica Allocation for Correlated Data Items in Ad Hoc Sensor Networks”, SIGMOD Record, Vol 33, No. 1, March 2004 • [4] T Hara, “Effective Replica Allocation in Ad Hoc Networks for improving Data Accessibility”, Proceedings of IEEE Infocom 2001, pp 1568-1576. • [5] Fundamentals of Database Systems, Fourth Edition, R. Elmasri, S. Navathe, 2003 • [6] “Using Expat”, http://www.xml.com/pub/a/1999/09/expat/index.html • [7] S. Jiang, D. He, and J. Rao, “A Prediction-based Link Availability estimation for Mobile Ad Hoc Networks,” in Proceedings of IEEE Infocom, Anchorage, Alaska, April 2001 

  27. Questions ?

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