PERFORMANCE EVALUATION OF META-HEURSITCS IN ENERGY AWARE REAL-TIME SCHEDULING PROBLEMS -

Energy efficient real-time systems has been a prime concern in the last few years. Techniques on all levels of system design are being developed to reduce energy consumption. On the physical level, new fabrication technologies attempt to minimise 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 minimise operational power. At the operating system level, energy-efficient scheduling utilises 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 minimisation. We analyse the performance of our algorithms under variable initial conditions. We further show that our meta-heuristics improve on previous work and are three times more likely to reach near-optimal energy savings.