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 filtering system, which facilitates the design of two efficient scaling algorithms. The first is a time-invariant scaling policy and it is proved to be a generalization of several popular DVS algorithms for periodic, sporadic, and aperiodic tasks. A more energy efficient policy is a time-variant scaling algorithm for aperiodic tasks. It is optimal in the sense that it is online without assumed information about future task releases. The algorithm turns out to be a water-filling process with a linear time complexity. It can be applied to scheduling based on worst-case execution times as well as online slack distribution when jobs complete earlier. We further establish two relationships between computation capacity and deadline misses to provide a statistical real-time guarantee with reduced capacity.