Optimistic distributed simulation based on transitive dependency tracking
Proceedings of the eleventh workshop on Parallel and distributed simulation
Fault-tolerant distributed simulation
PADS '98 Proceedings of the twelfth workshop on Parallel and distributed simulation
An Efficient Optimistic Message Logging Scheme for Recoverable Mobile Computing Systems
IEEE Transactions on Mobile Computing
Optimistic Recovery in Multi-Threaded Distributed Systems
SRDS '99 Proceedings of the 18th IEEE Symposium on Reliable Distributed Systems
Journal of Parallel and Distributed Computing
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Fault-tolerance techniques based on checkpointing and message logging have been increasingly used in real-world applications to reduce service downtime. Most industrial applications have chosen pessimistic logging because it allows fast and localized recovery. The price that they must pay, however, is the higher failure-free overhead. In this paper, we introduce the concept of K-optimistic logging where K is the degree of optimism that can be used to fine-tune the tradeoff between failure-free overhead and recovery efficiency. Traditional pessimistic logging and optimistic logging then become the two extremes in the entire spectrum spanned by K-optimistic logging. Our approach is to prove that only dependencies on those states that may be lost upon a failure need to be tracked on-line, and so transitive dependency tracking can be performed with a variable-size vector. The size of the vector piggybacked on a message then indicates the number of processes whose failures may revoke the message, and K corresponds to the system-imposed upper bound on the vector size.