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

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. Data Replication: Using Group Communication Systems
Total Order
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)
w(x)
w(x) x x x
Symmetric

7. 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
Total Order
w(x)
w(x)
primary
w(x)
w(x)
unordered x x x x x
Primary Copy

8. 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
Total Order
w(x)
w(x)
w(x)
w(x)
unordered x x x x x
Local Copy

9. Num of messages summary
Symmetric
Primary Copy
Local Copy
Total num of msgs
1 total order
1 unordered
Num of msgs within respone time
Local write
No concurrent conflicting request
Remote write
Has concurrent conflicting request

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 m m B B C C
SEQUENCER
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;
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.
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