Portable systems require long battery lifetime while still delivering high performance. Dynamic power management (DPM) policies trade off the performance for the power consumption at the system level in portable devices. In this work we present the time-indexed SMDP model (TISMDP) that we use to derive optimal policy for DPM in portable systems. TISMDP model is needed to handle the nonexponential user request interarrival times we observed in practice. We use our policy to control power consumption on three different devices: the SmartBadge portable device [18], the Sony Vaio laptop hard disk and WLAN card. Simulation results show large savings for all three devices when using our algorithm. In addition, we measured the power consumption and performance of our algorithm and compared it with other DPM algorithms for laptop hard disk and WLAN card. The algorithm based on our TISMDP model has 1.7 times less power consumption as compared to the default Windows timeout policy for the hard disk and three times less power consumption as compared to the default algorithm for the WLAN card.

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Energy consumption of electronic devices has become a serious concern in recent years. Power management (PM) algorithms aim at reducing energy consumption at the system-level by selectively placing components into low-power states. Formerly, two classes of heuristic algorithms have been proposed for power management: timeout and predictive. Later, a category of algorithms based on stochastic control was proposed for power management. These algorithms guarantee optimal results as long as the system that is power managed can be modeled well with exponential distributions. We show that there is a large mismatch between measurements and simulation results if the exponential distribution is used to model all user request arrivals. We develop two new approaches that better model system behavior for general user request distributions. Our approaches are event driven and give optimal results verified by measurements. The first approach we present is based on renewal theory. This model assumes that the decision to transition to low power state can be made in only one state. Another method we developed is based on the Time-Indexed Semi-Markov Decision Process model (TISMDP). This model has wider applicability because it assumes that a decision to transition into a lower-power state can be made upon each event occurrence from any number of states. This model allows for transitions into low power states from any state, but it is also more complex than our other approach. It is important to note that the results obtained by renewal model are guaranteed to match results obtained by TISMDP model, as both approaches give globally optimal solutions. We implemented our power management algorithms on two different classes of devices: two different hard disks and client-server WLAN systems such as the SmartBadge [19] or a laptop. The measurement results show power savings ranging from a factor of 1¡ 7 up to 5¡ 0 with insignificant variation in performance.

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