  Failure is inevitable in scientific computing. As scientific applications and
facilities increase their scales over the last decades, finding the root cause
of a failure can be very complex or at times nearly impossible. Different
scientific computing customers have varying availability demands as well as a
diverse willingness to pay for availability. In contrast to existing solutions
that try to provide higher and higher availability in scientific grids, we
propose a model called Task Replication for User-defined Availability (Trua).
Trua provides flexible, user-defined, availability in scientific grids,
allowing customers to express their desire for availability to computational
providers. Trua differs from existing task replication approaches in two folds.
First, it relies on the historic failure information collected from the virtual
layer of the scientific grids. The reliability model for the failures can be
represented with a bimodal Johnson distribution which is different from any
existing distributions. Second, it adopts an anomaly detector to filter out
anomalous failures; it additionally adopts novel selection algorithms to
mitigate the effects of temporary and spatial correlations of the failures
without knowing the root cause of the failures. We apply the Trua on real-world
traces collected from the Open Science Grid (OSG). Our results show that the
Trua can successfully meet user-defined availability demands.
