Clustered checkpointing: Maximizing the level of confidence for non-equidistant checkpointing

Employing fault tolerance often introduces a time overhead, which may cause a deadline violation in real-time systems (RTS). Therefore, for RTS it is important to optimize the fault tolerance techniques such that the probability to meet the deadlines, i.e. the Level of Confidence (LoC), is maximized. Previous studies have focused on evaluating the LoC for equidistant checkpointing. However, no studies have addressed the problem of evaluating the LoC for non-equidistant checkpointing. In this work, we provide an expression to evaluate the LoC for non-equidistant checkpointing. Further, we detail an exhaustive search approach to find the distribution of a given number of checkpoints that results in the maximal LoC. Since the exhaustive search approach is very time-consuming, we propose the Clustered Checkpointing method, a heuristic that distributes checkpoints in a number of clusters with the goal to maximize the LoC. The results show that the LoC can be improved when non-equidistant checkpointing is used. Further, the results indicate that the proposed Clustered Checkpointing method is capable to find the distribution that results in the maximal LoC in much shorter time than the exhaustive search approach, while considering only few clusters.