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We propose a new framework for the (length and reliability) bicriteria static multiprocessor scheduling problem. Our first criterion remains the schedule's length, which is crucial to assess the system's real-time property. For our second criterion, we consider the global system failure rate, seen as if the whole system were a single task scheduled onto a single processor, instead of the usual reliability, because it does not depend on the schedule length like the reliability does (due to its computation in the classical exponential distribution model). Therefore, we control better the replication factor of each individual task of the dependency task graph given as a specification, with respect to the desired failure rate. To solve this bicriteria optimization problem, we take the failure rate as a constraint, and we minimize the schedule length. We are thus able to produce, for a given dependency task graph and multiprocessor architecture, a Pareto curve of nondominated solutions, among which the user can choose the compromise that fits his or her requirements best. Compared to the other bicriteria (length and reliability) scheduling algorithms found in the literature, the algorithm we present here is the first able to improve significantly the reliability, by several orders of magnitude, making it suitable to safety-critical systems.