Adaptive Power Management (APM) attempts to increase the perceived battery life of a laptop by turning off certain components when their services are not going to be needed. Of course, it is not known in advance which services will be needed and when. Moreover, the perceived impact of having certain services not available is much greater than the perceived impact of others. The goal of APM is to extend the battery life as much as possible with an acceptable perceived loss of performance. APM, as opposed to non-adaptive power management, attempts to make better decisions with time and to adapt to individual users.

APM is an example of an autonomic system. The autonomics concept is derived from the human body's autonomic nervous system that regulates individual organ function, and for the most part is not subject to voluntary control. An autonomic computing system controls the functioning of computer applications or systems without explicit user input. The goal of autonomic computing is to create systems that are self-managing, self-healing, and self-protecting. Autonomic computing promises to reduce expenditures associated with operations, maintenance, and support, and to significantly improve the end user's experience.

Autonomic systems ensure smooth and uninterrupted operation by monitoring the system state, diagnosing problems, identifying user context, and executing actions to fix the problems. For example, an APM system monitors the user and laptop state, diagnoses the user mood or context, and changes power states such that the user is not noticeably impacted. Autonomic systems need to naturally reason about uncertainty in both perception and action. In an APM system, perception uncertainty arises from the fact that the user context cannot be directly observed from sensors, such as keyboard and mouse activity, or the currently active application. Actions that turn off components also generate uncertainty. They create uncertainty in terms of the time it takes to turn a component on and off and in terms of the effect it would have on the user's context. For example, placing the machine on standby does not always take the same amount of time and sometimes the Operating System (OS) does not even allow it. If the user context is for example a measure of idleness, then going to standby, could either increase idleness if it succeeds or decrease it if it fails, hence the uncertainty. If the user context is a measure of the users' mode of operation (e.g., working, browsing for fun) then going to standby could again change the context in an unpredictable manner.

Modeling real-world phenomena as stochastic processes and sequential decision-making under uncertainty (see Figure 1) are widely used paradigms in artificial intelligence. The process of estimating these stochastic process models and the computation of optimal policies fall into the category of machine learning. Machine learning refers to the problem of constructing computer programs that automatically improve with experience. The simplest class of machine learning algorithms are classifiers, which are functions that learn (from examples) to map input patterns to output classes. For the APM domain, our algorithms learn to map laptop usage patterns (at the OS level) into power management actions as shown in Figure 2.

In this paper, we first summarize past approaches to the APM problem and then describe a machine-learning approach that maps user patterns to power-saving sections. Our approach outperforms current commercial solutions such as timeout policies. We then propose a more general model-based approach that relies on building stochastic process models. We finally discuss the potential application of such models to other domains.

Figure 1: A classic sequential decision-making task under uncertainty. The doctor performs treatments and diagnostic tests on the patient, which change in a stochastic manner the internal state of the patient. The doctor then observes symptoms. The optimal policy is one where in the long run the patient is made well.
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Figure 2: APM takes as input user and system activity and issues power management actions to the OS and to the hardware
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