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The periodic update transaction model has been used to maintain the freshness (or temporal validity) of real-time data. Period and deadline assignment has been the main focus of past studies, such as the More-Less scheme , in which update transactions are guaranteed by the Deadline Monotonic scheduling algorithm  to complete by their deadlines. In this paper, we propose a deferrable scheduling algorithm for fixed-priority transactions, a novel approach for minimizing update workload while maintaining the temporal validity of real-time data. In contrast to prior work on maintaining data freshness periodically, update transactions follow an aperiodic task model in the deferrable scheduling algorithm. The deferrable scheduling algorithm exploits the semantics of temporal validity constraint of real-time data by judiciously deferring the sampling times of update transaction jobs as late as possible. We present a theoretical estimation of its processor utilization and a sufficient condition for its schedulability. Our experimental results verify the theoretical estimation of the processor utilization. We demonstrate through the experiments that the deferrable scheduling algorithm is an effective approach and it significantly outperforms the More-Less scheme in terms of reducing processor workload.