Grid \u00a0Computing \u00a0has \u00a0become \u00a0major \u00a0player \u00a0in \u00a0super-computing \u00a0community. \u00a0But \u00a0due \u00a0to \u00a0the
diversity \u00a0and \u00a0disruptive \u00a0nature \u00a0of \u00a0its \u00a0resources, \u00a0failure \u00a0of \u00a0jobs \u00a0is \u00a0not \u00a0an \u00a0exception. \u00a0 \u00a0However, \u00a0many
researchers \u00a0have \u00a0come \u00a0up \u00a0with \u00a0models \u00a0that \u00a0enhance \u00a0jobs \u00a0survivability. \u00a0Popular \u00a0among \u00a0this \u00a0model \u00a0is
checkpoint model which have the ability of saving already computed jobs on a stable secured storage. This
model avoids re-computing of already computed jobs from the scratch in case of resources failure. But the
time a job takes in checkpoinitng also becomes another task which adds overheads to computing resources
thereby reducing the resources performance. In order not to add too many overheads to computing resources,
the \u00a0number \u00a0of \u00a0checkpoints \u00a0must \u00a0be \u00a0minimized. \u00a0This \u00a0study \u00a0proposed \u00a0checkpoint \u00a0interval \u00a0models \u00a0which \u00a0is
implemented based on fault index history of computing resources. Failed jobs are re-allocated from their last
saved checkpoint using an exception handler. The study observed that arithmetic checkpoint model is better
used when fault index of computing resources is high while geometric checkpoint model is better when fault
index of resources is low.
