Operating system services for task specific power management

Mobile computing systems have to provide sufficient operating time in spite of limited battery capacity. Therefore, they rely on energy-efficient management of system resources. This issue is addressed by system components with low-power operating modes which reduce the power consumption considerably. However, power management mechanisms can cause increased latencies and may affect application quality negatively. While this may be tolerated for specific applications as far as energy is saved, the user will expect maximum performance for other tasks. Consequently, one important insight is that algorithms controlling low-power operating modes have to make application-specific trade-offs between performance and energy savings. Furthermore, contemporary power management policies are often based on heuristics and implicit assumptions that do not consider this trade-off and cannot be modified or adapted to the performance requirements of the specific application. In this context, the terms performance and quality have to be understood as synonyms, related to speed, usability or other runtime properties of a task. The goal of this thesis is to provide system services that allow to make application-specific trade-offs between energy savings and application performance. Different approaches to power management are presented that consider task-specific performance requirements and take the effects of low-power modes on application quality into account. First, system services are introduced that determine the energy consumption and monitor runtime parameters related to application performance. With this information, power management policies obtain a feedback on the consequences of their decisions. Thus, they can react to insufficient energy savings and avoid violations of application-specific performance requirements. It will be demonstrated that an adaptive management of low-power modes is feasible for interactive applications. As a second approach, an extended system interface to be used by energy-aware programs is presented. The application developer can specify which device operations are time-critical and for which operations a performance degradation is tolerated. The granted flexibility can be exploited by the operating system to maximize energy savings without violating performance requirements of specific operations. Finally, an approach is presented that enables the user to train the system to make optimum, application-specific trade-offs between performance and energy savings at runtime. Therefore, methods from machine learning are applied to system power management. With this approach, the individual user’s preferred power/performance trade-off can be taken into account. It is shown how to realize a hierarchical energy management that distinguishes certain applications and switches dynamically between different, specialized power management policies. Prototype implementations for Linux are presented and evaluated with energy measurements, proving the feasibility of task-specific power management.