Energy efficient real-time systems have been a prime concern in the past few years. Techniques at all
levels of system design are being developed to reduce energy consumption. At the physical level, new
fabrication technologies attempt to minimize overall chipset power. At the system design level,
technologies such as Dynamic Voltage and Frequency Scaling (DVFS) and Dynamic Power Management
(DPM) allow for changing the processor frequency on-the-fly or go into sleep modes to minimize
operational power. At the operating system level, energy-efficient scheduling utilizes DVFS and DPM at
the task level to achieve further energy savings. Most energy-efficient scheduling research efforts focused
on reducing processor power. Recently, system-wide solutions have been investigated. In this work, we
extend on the previous work by adapting two evolutionary algorithms for system-wide energy
minimization. We analyse the performance of our algorithms under variable initial conditions. We further
show that our meta-heuristics statistically provide energy minimizations that are closer to the optimum
85% of the time compared to about 30% of those achieved by simulated annealing over 500 unique test
sets. Our results further demonstrate that in over 95% of the cases, meta-heuristics provide more
minimizations than the CS-DVS static method.
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