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A Fully Polynomial-Time Approximation Scheme for Speed Scaling with Sleep State
We study classical deadline-based preemptive scheduling of tasks in a
computing environment equipped with both dynamic speed scaling and sleep state
capabilities: Each task is specified by a release time, a deadline and a
processing volume, and has to be scheduled on a single, speed-scalable
processor that is supplied with a sleep state. In the sleep state, the
processor consumes no energy, but a constant wake-up cost is required to
transition back to the active state. In contrast to speed scaling alone, the
addition of a sleep state makes it sometimes beneficial to accelerate the
processing of tasks in order to transition the processor to the sleep state for
longer amounts of time and incur further energy savings. The goal is to output
a feasible schedule that minimizes the energy consumption. Since the
introduction of the problem by Irani et al. [16], its exact computational
complexity has been repeatedly posed as an open question (see e.g. [2,8,15]).
The currently best known upper and lower bounds are a 4/3-approximation
algorithm and NP-hardness due to [2] and [2,17], respectively. We close the
aforementioned gap between the upper and lower bound on the computational
complexity of speed scaling with sleep state by presenting a fully
polynomial-time approximation scheme for the problem. The scheme is based on a
transformation to a non-preemptive variant of the problem, and a discretization
that exploits a carefully defined lexicographical ordering among schedules
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