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Feasibility of Consistent Data Replication Over WANs

This paper explores the challenges and solutions for maintaining consistent data replication in Wide Area Networks (WANs). While replication protocols traditionally perform well in Local Area Networks (LANs), their effectiveness in WAN environments is less understood. This study examines different replication approaches, such as symmetric and local copy methods, highlighting the importance of total order multicast for ensuring timely response times. By analyzing various experiments, this research provides insights into optimizing data replication strategies across WANs, contributing to improved fault tolerance and performance.

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Feasibility of Consistent Data Replication Over 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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