  In this paper, we revisit traditional checkpointing and rollback recovery
strategies, with a focus on silent data corruption errors. Contrarily to
fail-stop failures, such latent errors cannot be detected immediately, and a
mechanism to detect them must be provided. We consider two models: (i) errors
are detected after some delays following a probability distribution (typically,
an Exponential distribution); (ii) errors are detected through some
verification mechanism. In both cases, we compute the optimal period in order
to minimize the waste, i.e., the fraction of time where nodes do not perform
useful computations. In practice, only a fixed number of checkpoints can be
kept in memory, and the first model may lead to an irrecoverable failure. In
this case, we compute the minimum period required for an acceptable risk. For
the second model, there is no risk of irrecoverable failure, owing to the
verification mechanism, but the corresponding overhead is included in the
waste. Finally, both models are instantiated using realistic scenarios and
application/architecture parameters.
