An algorithm for concurrency control and recovery in replicated distributed databases
ACM Transactions on Database Systems (TODS)
Concurrency control and recovery in database systems
Concurrency control and recovery in database systems
Scale and performance in a distributed file system
ACM Transactions on Computer Systems (TOCS)
Leases: an efficient fault-tolerant mechanism for distributed file cache consistency
SOSP '89 Proceedings of the twelfth ACM symposium on Operating systems principles
Disconnected operation in the Coda File System
ACM Transactions on Computer Systems (TOCS)
A Majority consensus approach to concurrency control for multiple copy databases
ACM Transactions on Database Systems (TODS)
A low-bandwidth network file system
SOSP '01 Proceedings of the eighteenth ACM symposium on Operating systems principles
Replication Techniques in Distributed Systems
Replication Techniques in Distributed Systems
SnapMirror: File-System-Based Asynchronous Mirroring for Disaster Recovery
FAST '02 Proceedings of the Conference on File and Storage Technologies
Weighted voting for replicated data
SOSP '79 Proceedings of the seventh ACM symposium on Operating systems principles
The failure and recovery problem for replicated databases
PODC '83 Proceedings of the second annual ACM symposium on Principles of distributed computing
A principle for resilient sharing of distributed resources
ICSE '76 Proceedings of the 2nd international conference on Software engineering
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This paper describes an approach to real-time decision-making for quality of service based scheduling of distributed asynchronous data replication. The proposed approach addresses uncertainty and variability in the quantity of data to replicate over low bandwidth fixed communication links. A dynamic stochastic knapsack is used to model the acceptance policy with dynamic programming optimization employed to perform offline optimization. The obtained optimal values of the input variables are used to build and train a multi-layer neural network. The obtained neural network weights and configuration can be used to perform near optimal accept/reject decisions in real-time. Off-line processing is used to establish the initial acceptance policy and to verify that the system continues to perform near-optimally. The proposed approach is implemented via simulation enabling the evaluation of a variety of scenarios and refinement of the scheduling portion of the model. The preliminary results are very promising.