How to assign votes in a distributed system
Journal of the ACM (JACM)
Consistency in a partitioned network: a survey
ACM Computing Surveys (CSUR)
SIGMOD '87 Proceedings of the 1987 ACM SIGMOD international conference on Management of data
A Majority consensus approach to concurrency control for multiple copy databases
ACM Transactions on Database Systems (TODS)
Weighted voting for replicated data
SOSP '79 Proceedings of the seventh ACM symposium on Operating systems principles
Voting class — an approach to achieving high availability for replicated data
DPDS '90 Proceedings of the second international symposium on Databases in parallel and distributed systems
Storage Efficient Replicated Databases
IEEE Transactions on Knowledge and Data Engineering
A New Dynamic Voting Algorithm for Distributed Database Systems
IEEE Transactions on Knowledge and Data Engineering
Voting as the Optimal Static Pessimistic Scheme for Managing Replicated Data
IEEE Transactions on Parallel and Distributed Systems
Obtaining Coteries That Optimize the Availability of Replicated Databases
IEEE Transactions on Knowledge and Data Engineering
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A replicated database system may partition into isolated groups in the presence of node and link failures. When the system has partitioned, a pessimistic scheme maintains availability and consistency of replicated data by ensuring that updates occur in at most one group. A pessimistic scheme is called a static scheme if these distinguished groups are determined only by the membership of different groups in the partitioned system. In this paper, we present a new static scheme that is more powerful than voting. In this scheme, the set of distinguished groups, called an acceptance set, is chosen at design time. To commit an update, a node checks if its enclosing group is a member of this acceptance set. Using an encoding scheme for groups, this check is implemented very efficiently. Another merit of the proposed scheme is that the problem of determining an optimal acceptance set is formulated as a sparse 0-1 linear programming problem. Hence, the optimization problem can be handled using the very rich class of existing techniques for solving such problems. Based on our experiments, we feel that this optimization approach is feasible for systems containing up to 10 nodes (copies).