Distributed Recovery with K-Optimistic Logging

Fault-tolerance techniques based on checkpointing and message logging have been increasingly used in real-world applications to reduce service down-time. Most industrial applications have chosen pessimistic logging because it allows fast and localized recovery. The price that they must pay, however, is the high 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 trade-off 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 results generalize several previously known protocols. 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 piggy-backed on a message then ind...

• Type: Conference
• Year: 1997
• Where: ICDCS
• Authors: Yi-Min Wang, Om P. Damani, Vijay K. Garg
• Keywords: Distributed And Parallel Computing, Failure-free Overhead, ICDCS 1997, K-optimistic Logging, Pessimistic Logging