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A Voting Scheme for BLOB Replication

This presentation outlines a voting scheme designed for BLOB (Binary Large Object) replication, focusing on its system design, implementation phases, and bandwidth usage. Key aspects include comparisons between Byzantine replication strategies and models without replication. The distance calculation method between nodes and the approach for selecting candidates for replication are highlighted. The simulation phase covers consensus reaching and bandwidth efficiency, providing insights into various replication methods and their performance metrics. Key outputs and system screenshots will be included to illustrate findings.

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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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