Dynamic voltage scaling (DVS) is a promising technique for battery-powered systems to conserve energy consumption. Most existing DVS algorithms assume information about task periodicity or a priori knowledge about the task set to be scheduled. This paper presents an analytical model of general tasks for DVS assuming job timing information is known only after a task release. It models the voltage scaling process as a transfer function-based filter system, which facilitates the design of two efficient scaling algorithms. The first is a time-invariant scaling policy based on a voltage scaling function independent of input jobs over time. It is proved to be a generalization of several existing DVS algorithms. A more energy efficient policy is a time-variant scaling algorithm. The algorithm turns out to be a water-filling process of information theory with a low time complexity. It can not only be applied to scheduling based on worst case execution times, but also to online slack distribution when jobs complete earlier. We further establish two relationships between computation capacity and deadline misses. The relationships make it possible to the provisioning of statistical real-time guarantees.