Concurrency control and recovery in database systems
Concurrency control and recovery in database systems
Epidemic algorithms for replicated database maintenance
PODC '87 Proceedings of the sixth annual ACM Symposium on Principles of distributed computing
Managing update conflicts in Bayou, a weakly connected replicated storage system
SOSP '95 Proceedings of the fifteenth ACM symposium on Operating systems principles
ACM Transactions on Computer-Human Interaction (TOCHI)
ACM Transactions on Computer Systems (TOCS)
Decentralized replicated-object protocols
Proceedings of the eighteenth annual ACM symposium on Principles of distributed computing
Eventually-serializable data services
Theoretical Computer Science
Time, clocks, and the ordering of events in a distributed system
Communications of the ACM
The IceCube approach to the reconciliation of divergent replicas
Proceedings of the twentieth annual ACM symposium on Principles of distributed computing
The Database State Machine Approach
Distributed and Parallel Databases
Roam: A Scalable Replication System for Mobile Computing
DEXA '99 Proceedings of the 10th International Workshop on Database & Expert Systems Applications
Epidemic Algorithms for Replicated Databases
IEEE Transactions on Knowledge and Data Engineering
Handling message semantics with Generic Broadcast protocols
Distributed Computing
ACM Computing Surveys (CSUR)
Draw-together: graphical editor for collaborative drawing
CSCW '06 Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work
An efficient and fault-tolerant update commitment protocol for weakly connected replicas
Euro-Par'05 Proceedings of the 11th international Euro-Par conference on Parallel Processing
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We study large-scale distributed cooperative systems that use optimistic replication. We represent a system as a graph of actions (operations) connected by edges that reify semantic constraints between actions. Constraint types include conflict, execution order, dependence, and atomicity. The local state is some schedule that conforms to the constraints; because of conflicts, client state is only tentative. For consistency, site schedules should converge; we designed a decentralised, asynchronous commitment protocol. Each client makes a proposal, reflecting its tentative and/or preferred schedules. Our protocol distributes the proposals, which it decomposes into semantically-meaningful units called candidates, and runs an election between comparable candidates. A candidate wins when it receives a majority or a plurality. The protocol is fully asynchronous: each site executes its tentative schedule independently, and determines locally when a candidate has won an election. The committed schedule is as close as possible to the preferences expressed by clients.