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Algorithmic power management - Energy minimisation under real-time constraints

Energy consumption is a major concern for designers of embedded devices. 
Especially for battery operated systems (like many embedded systems), the energy consumption limits the time for which a device can be active, and the amount of processing that can take place. In this thesis we study how the energy consumption can be reduced for certain classes of real-time applications. 
 
To minimise the energy consumption, we introduce several algorithms that combine power management techniques with scheduling (algorithmic power management). 
The power management techniques that we focus on are speed scaling and sleep modes. When the processor (or some peripheral) is active, its speed, and with it the supply voltage, can be decreased to reduce the power consumption (speed scaling), while when the processor is idle it can be put in a low power mode (sleep modes). The resulting problem is to determine a schedule, speeds for the processors (which may vary over time) and/or times when a device is put to sleep. 
 
We discuss energy minimisation for three classes of real-time systems, namely (1) real-time systems with agreeable deadlines, (2) real-time systems with precedence constraints, and (3) frame-based real-time systems. Below we elaborate on these classes of real-time systems. 
 
(1) For real-time systems with agreeable deadlines it holds that an earlier arrival time implies an earlier deadline (and vice versa). 
Compared to existing methods our algorithms can reduce the energy consumption by up to 54% for the considered multimedia workloads, and our evaluation shows that these algorithms are near optimal even with inaccurate predictions.