Hierarchical Quorum Consensus: A New Algorithm for Managing Replicated Data Akhil Kumar IEEE TRANSACTION ON COMPUTERS, VOL.40, NO.9, SEPTEMBER 1991

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

Introduction(1/8) Motivations of Data Replication 1.Fault Tolerant 2.Increasing System Reliability

Introduction(2/8) 1.Providing Fault tolerant capability in distributed system :One copy of an object

Introduction(3/8) 2.Replication of data for concurrent read/write The copy is using :One copy of an object The copy is using

Introduction(4/8) Two problems occur in distribution system: –RW problem –WW problem Read Write Read Write

Introduction(5/8) Two operations of quorum structure in distribution system: –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. Read quorum Write quorum

Introduction(6/8) The solution : intersect property of read/write quorum –RW problem –WW problem Read and Write Read quorumWrite quorum Write and Write write quorumWrite quorum

Introduction(7/8) 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 n 0.63 copies. A smaller quorum size results in a lower cost of synchronization.

Introduction(8/8) Our method is based on organizing the copies of an object into –extending the quorum consensus algorithm –Logical node –multilevel hierarchy

QC Algorithm 8 copies let n=8+1 qr+qw > 9 2qw > copies let n=9+1 qr+qw > =10 2qw > = Read and Write Read quorumWrite quorum The quorum intersection conditions: Read and Write Read quorum Write quorum

The concept of HQC An example of 2-level l1=3 l2=3 r1+w1>3 r2+w2>3 2w1>3 2w2> r w best size

The concept of HQC

HQC algorithm For example: l1=3 r1+w1>3 2w1>

HQC algorithm

=

best size worst size

Availability Analysis HQC Majority Voting HQC  Majority Voting HQC Majority Voting HQC  Majority Voting

Availability Analysis HQC Majority Voting HQC  Majority Voting HQC Majority Voting HQC  Majority Voting

Tradeoffs between HQC and Related Algorithm HQC is better than others fully.

Conclusion 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)/2  copies (as in majority voting) to n 0.63 copies –The HQC method produces certain intersecting sets of quorums that cannot be produced in a single-level vote assignment