Optimizing the Level of Confidence for Multiple Jobs

Correct operation of real-time systems (RTS) is defined as producing correct results within given time constraints (deadlines). As RTS are becoming more susceptible to soft errors, employing fault-tolerant techniques is crucial. Rollback Recovery with Checkpointing (RRC) is an efficient fault-tolerant technique. However, RRC introduces a time overhead which depends on the number of checkpoints. The imposed time overhead may cause deadline violations. Therefore, it is important at design time to have a metric to evaluate to what extent a time constraint is met such that RRC can be optimized. In our previous work we introduced the usage of Level of Confidence (LoC), i.e. the probability to meet a given deadline, and showed for a single job that there exists an optimal number of checkpoints which results in the maximal LoC. In this paper we assume given is a deadline and a set of jobs that employ RRC, and the objective is to find the optimal checkpoint assignment that maximizes the LoC. We show that our previous work is not sufficient for multiple jobs. Therefore, we derive an expression to compute the LoC and propose an efficient method to maximize the LoC for multiple jobs