Analytical models for the design and evaluation of checkpointing of real-time tasks are developed. First, the execution of a real-time tasks is modeled under a commong assumption of perfect coverage on online detection mechanisms (which is termed a basic model). Then, the model is generalized (to an extended model) to include more realistic cases, i. e. , imperfect coverages of online detection mechanisms and acceptance tests. Finally, an optimal placement of checkpoints is determined that will minimize the mean task execution time while the probability of an unreliable result (or lack of confidence) is kept below a specified level. In the basic model, it is shown that equidistant intercheckpoint intervals are optimal, whereas this is not necessarily true for the extended model. An algorithm for calculating the optimal number of checkpoints and intercheckpoint intervals is presented with some numerical examples for the extended model.