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Hierarchical Quorum Consensus: A New Algorithm for Managing Replicated Data PowerPoint Presentation

Hierarchical Quorum Consensus: A New Algorithm for Managing Replicated Data

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### Hierarchical Quorum Consensus: A New Algorithm for Managing Replicated Data

Akhil Kumar

IEEE TRANSACTION ON COMPUTERS, VOL.40, NO.9, SEPTEMBER 1991

Outline Replicated Data

- Introduction
- Quorum Consensus Algorithm
- Hierarchical Quorum Consensus
- HQC algorithm
- Availability Analysis
- Tradeoffs between HQC and Related Algorithm
- Conclusion

Introduction(1/8) Replicated Data

- Motivations of Data Replication
- Fault Tolerant
- Increasing System Reliability

:One copy of an object Replicated Data

Introduction(2/8)1.Providing Fault tolerant capability in distributed system

The copy is using Replicated Data

:One copy of an object

Introduction(3/8)2.Replication of data for concurrent read/write

The copy is using

Introduction(4/8) Replicated Data

- Two problems occur in distribution system:
- RW problem
- WW problem

Write

Read

Read

Write

Write

Write

Two operations of quorum structure in distribution system: Replicated Data

Read operation

To access all of the copies in a read quorum

a copy with the highest version number is returned

Write operation

To write to all of the copies in a write quorum

assigns each copy the version number that is one more than the maximum version number encountered in the write quorum.

Introduction(5/8)Read quorum

Write quorum

Read and Write Replicated Data

Write and Write

Read quorum

write quorum

Write quorum

Write quorum

Introduction(6/8)- The solution : intersect property of read/write quorum
- RW problem
- WW problem

Introduction(7/8) Replicated Data

- This paper generalizes the quorum consensus scheme (QC)
- into a multilevel algorithm called hierarchical quorum consensus (HQC)
- shows that given a collection of n copies of an object, the minimum size of a quorum is n0.63 copies.

- A smaller quorum size results in a lower cost of synchronization.

Introduction(8/8) Replicated Data

- Our method is based on organizing the copies of an object into
- extending the quorum consensus algorithm
- Logical node
- multilevel hierarchy

8 copies let n=8+1 Replicated Data

qr+qw > 9

2qw > 9

5 5

4 6

. .

. .

9 copies let n=9+1

qr+qw > =10

2qw > =10

5 5

4 6

. .

. .

Read and Write

Read quorum

Write quorum

The quorum intersection conditions:

Read and Write

Read quorum

Write quorum

QC Algorithmbest size Replicated Data

The concept of HQC- An example of 2-level
l1=3 l2=3

r1+w1>3 r2+w2>3

2w1>3 2w2>3

2 2 2 2 4 4

1 3 1 3 1 9

1 3 2 2 2 6

r w

The concept of HQC Replicated Data

HQC algorithm Replicated Data

HQC algorithm Replicated Data

=

HQC Replicated Data Majority Voting

HQC Majority Voting

Majority Voting

Majority Voting

HQC

HQC

Availability AnalysisHQC Replicated Data Majority Voting

HQC Majority Voting

Majority Voting

Majority Voting

HQC

HQC

Availability AnalysisTradeoffs between HQC and Related Algorithm Replicated Data

HQC is better than others fully.

Conclusion Replicated Data

- In this paper, they introduced a new algorithm, also based on voting, and showed that:
- It is possible to reduce the size of a quorum from (n+1)/2copies (as in majority voting) to n0.63 copies
- The HQC method produces certain intersecting sets of quorums that cannot be produced in a single-level vote assignment

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