System-wide energy minimization for real-time tasks: lower bound and approximation
Proceedings of the 2006 IEEE/ACM international conference on Computer-aided design
IEEE Transactions on Computers
Stochastic DVS-based dynamic power management for soft real-time systems
Microprocessors & Microsystems
Proceedings of the conference on Design, automation and test in Europe
DVSMT: Dynamic Voltage Scaling for Scheduling Mixed Real-Time Tasks
ICESS '07 Proceedings of the 3rd international conference on Embedded Software and Systems
CWS: a model-driven scheduling policy for correlated workloads
Proceedings of the ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Parametric timing analysis and its application to dynamic voltage scaling
ACM Transactions on Embedded Computing Systems (TECS)
Dynamic Cache Reconfiguration for Soft Real-Time Systems
ACM Transactions on Embedded Computing Systems (TECS)
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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.