#Number
TR-PDS-1997-007
#Title
Distributed Recovery with K-Optimistic Logging
#Author
Yi-Min Wang
Om P. Damani
Vijay K. Garg
#Abstract
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.
#Bib
@TechReport{,
author = "Yi-Min Wang, Om P. Damani, and Vijay K. Garg",
title = "Distributed Recovery with K-Optimistic Logging",
institution = "Parallel and Distributed Systems Laboratory, ECE
Dept. University of Texas at Austin",
month = "July",
note = 1997,
note = "available via ftp or WWW at maple.ece.utexas.edu
as technical report TR-PDS-1997-007"
}