#Number
TR-PDS-1998-010

#Title
Distributed Recovery with $K$-Optimistic Logging

#Author
Om P. Damani
Yi-Min Wang 
Vijay K. Garg

#Abstract
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 indicates the number of processes whose
failures may revoke the message, and $K$ corresponds to the upper bound on the
vector size.  Furthermore, the parameter $K$ is dynamically tunable in
response to changing system characteristics.	

#Bib
@InProceedings{,
  author =       "",
  title =        "",
  booktitle =    "the publication name",
  address =      "location of publication",
  month =        "",
  note =         1998,
  note =         "available via ftp or WWW at maple.ece.utexas.edu
                  as technical report TR-PDS-1998-010"
}