Consistent Data Replication: Is it feasible in WANs

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
Toronto
Montreal
Montreal
Ottawa
Ottawa


WAN
Montreal
Montreal
Toronto
Ottawa
Benefits Fault Tolerance, Performance
Challenge keep data consistent
3
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
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
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
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
token
Seq
A (seq)
A
m
m
B
B
C
C

• SEQUENCER

TOKEN
m2
m
lt1,0,0gt
A
A
m1
m2?m1
lt1,0,0gt
B
B
lt1,0,0gt
C
C
LAMPORT
Round Robin (ATOP)
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Experiment (III) Different Total Order Alg
5 sites in WAN, with replication 100 update,
Symmetric,
5 sites in WAN, without replication
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, 1471-98,
  2003.
• Spread http//www.spread.org
• JGroups http//www.jgroups.org