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A novel energy reduction strategy to maximally exploit the dynamic workload variation is proposed for the offline voltage scheduling of preemptive systems. The idea is to construct a fully preemptive schedule that leads to minimum energy consumption when the tasks take on approximately the average execution cycles yet still guarantees no deadline violation during the worst-case scenario. End-time for each sub-instance of the tasks obtained from the schedule is used for the on-line dynamic voltage scaling (DVS) of the tasks. For the tasks that normally require a small number of cycles but occasionally a large number of cycles to complete, such a schedule provides more opportunities for slack utilization and hence results in larger energy saving. The concept is realized by formulating the problem as a nonlinear programming (NLP) optimization problem. Experimental results show that, by using the proposed scheme, the total energy consumption at runtime is reduced by as much as 60% for randomly generated task sets when compared with the static scheduling approach only using worst case workload.