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Consistent Data Replication: Is it feasible in WANs?

Consistent Data Replication: Is it feasible in WANs?. Yi Lin Bettina Kemme Marta Patiño-Martínez Ricardo Jiménez-Peris Sep 2, 2005. Data Replication: What,Why,How?. Without Replication. With Replication. Toronto. Montreal. Ottawa. Toronto. Montreal. Ottawa. …. …. WAN. Montreal.

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Consistent Data Replication: Is it feasible in WANs?

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  1. Consistent Data Replication: Is it feasible in WANs? Yi Lin Bettina Kemme Marta Patiño-Martínez Ricardo Jiménez-Peris Sep 2, 2005

  2. Data Replication: What,Why,How? Without Replication With Replication Toronto Montreal Ottawa Toronto Montreal Ottawa … … WAN Montreal Toronto Montreal Ottawa Benefits: Fault Tolerance, Performance Challenge: keep data consistent

  3. w(x) w(x) x x x x x x Data Replication: challenge • Keep data consistent Replica control

  4. Motivations • Most replication protocols have been proved to perform well in LANs. • Little work has been done in WANs • GlobData [DMBS02], Tech Report [JHU02] • Are these protocols also feasible in WANs? • Protocols working well in LANs may not work well in WANs. • Why? What are the bottlenecks? • Any solutions?

  5. Intro to Group Communication Systems • GCS provides • multicast primitives to all members in the group • Group maintenance (removal of failed members, etc.) • Ordering • Unordered • Total order (messages delivered in all members in the same order) • Reliability • Different degrees of delivery guarantees in case of site failures • Analyzed in paper;

  6. Total Order w(x) w(x) w(x) w(x) x x x x x x Data Replication: Using Group Communication Systems • Read-Only requests: • Executed in the local site • Update requests: • Multicast in total order firstly. • executed according to total order delivery. • Num of msgs for an update • 1 total order w(x) w(x) Symmetric

  7. Total Order w(x) w(x) w(x) w(x) unordered x x Data Replication: Using Group Communication Systems • Read-Only requests: • Executed in the local site • Update requests: • Request totally ordered firstly. • executed only in the primary site • Multicast the changes in unordered msg. • Apply change in other sites • Num of msgs for an update • 1 total order + 1 unordered • Local write (w(x)) • 1 total order within response time • Remote write (w(x)) • 1 total order + 1 unordered within response time w(x) w(x) primary x x x x Primary Copy

  8. Total Order w(x) w(x) w(x) w(x) unordered x x Data Replication: Using Group Communication Systems • Read-Only requests: • Executed in the local site • Update requests: • Request totally ordered firstly. • executed locally • Multicast the changes in unordered msg. • Apply change in other sites • Num of msgs for an update • 1 total order + 1 unordered • No concurrent conflicting req • 1 total order within response time • Has concurrent conflicting req • 1 total order + 1 unordered within response time w(x) w(x) x x x x Local Copy

  9. Num of messages summary

  10. Experiment (I) LAN WAN (5 sites, 100% update)

  11. Experiment (I): Response time analysis

  12. Experiment (II): Scalability in WAN Read-only requests Update requests 50% update, Symmetric

  13. Different Total Order Algorithms Seq # token A (seq) A SEQUENCER m m B B C C TOKEN m2 m <1,0,0> A A m1 m2m1 <1,0,0> B B <1,0,0> C C LAMPORT Round Robin (ATOP)

  14. Experiment (III): Different Total Order Alg 5 sites in WAN, without replication 5 sites in WAN, with replication 100% update, Symmetric,

  15. Conclusions • Consistent database replication is feasible in WANs; • In WANs, • For deterministic applications, Symmetric approach is preferable. • For non-deterministic applications, Local Copy is preferable; • In WAN, total order multicast is crucial to response time. Round Robin total order has better performance over others; • We have some other interesting optimizations. Please refer to our paper.

  16. References • [C-JDBC] E. Ceccet, J.Marguerite, and W. Zwaenepoel. C-JDBC: Flexible database clustering middleware. In USENIX conference 2004 • [Ganymed] C. Plattner and G. Alonso. Ganymed: Scalable replication for transactional web applications. In Middleware, 2004. • [GlobData] L. Rodrigues, H. Miranda, R. Almeida, J. Martins, and P. Vicente. Strong Replication in the GlobData Middleware. In Workshop on Dependable Middleware-Based Systems, 2002. • [Middle-R] R. Jimenez-Peris, M. Patiòno-Martnez, B. Kemme, and G. Alonso. Improving Scalability of Fault Tolerant Database Clusters. In ICDCS'02. • [Conflict-Aware] C. Amza, A. L. Cox, and W. Zwaenepoel. Conict-Aware Scheduling for Dynamic Content Applications. In USENIX Symp. on Internet Tech. and Sys., 2003. • [State Machine] F. Pedone, R. Guerraoui, and A. Schiper. The Database State Machine Approach. Distributed and Parallel Databases, 14:71-98, 2003. • [Spread]http://www.spread.org • [JGroups] http://www.jgroups.org

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