Optimistic recovery in distributed systems
ACM Transactions on Computer Systems (TOCS)
Recovery in distributed systems using optimistic message logging and check-pointing
Journal of Algorithms
Manetho: Transparent Roll Back-Recovery with Low Overhead, Limited Rollback, and Fast Output Commit
IEEE Transactions on Computers - Special issue on fault-tolerant computing
Optimistic Crash Recovery without Changing Application Messages
IEEE Transactions on Parallel and Distributed Systems
Fail-stop processors: an approach to designing fault-tolerant computing systems
ACM Transactions on Computer Systems (TOCS)
Time, clocks, and the ordering of events in a distributed system
Communications of the ACM
A survey of rollback-recovery protocols in message-passing systems
ACM Computing Surveys (CSUR)
Message Logging: Pessimistic, Optimistic, Causal, and Optimal
IEEE Transactions on Software Engineering
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
CANPC '98 Proceedings of the Second International Workshop on Network-Based Parallel Computing: Communication, Architecture, and Applications
Publishing: a reliable broadcast communication mechanism
SOSP '83 Proceedings of the ninth ACM symposium on Operating systems principles
Towards a Communication Characterization Methodology for Parallel Applications
HPCA '97 Proceedings of the 3rd IEEE Symposium on High-Performance Computer Architecture
An Asynchronous Recovery Scheme based on Optimistic Message Logging for Mobile Computing Systems
ICDCS '00 Proceedings of the The 20th International Conference on Distributed Computing Systems ( ICDCS 2000)
Completely Asynchronous Optimistic Recovery with Minimal Rollbacks
FTCS '95 Proceedings of the Twenty-Fifth International Symposium on Fault-Tolerant Computing
Distributed recovery with K-optimistic logging
Journal of Parallel and Distributed Computing
Causality tracking in causal message-logging protocols
Distributed Computing
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In a distributed computing system, message logging is widely used for providing nodes with recoverability. To reduce the piggyback overhead of traditional causal message logging, we present a zoning causal message logging approach in this paper. The crux of the approach is to control the propagation of dependency information: the nodes in the system are divided into zones, and by a message fragment mechanism, the dependency information of a node is only visible in the zone scope. Simulation results show that the piggyback overhead of the proposed approach is lower than that of traditional causal message logging.