A Homogeneous Architecture for Power Policy Integration in Operating Systems

Abstract

Abstract-A significant volume of research has concentrated on operating system (OS)-directed power management. The primary focus of previous research has been the development of better policies. In this paper, we provide evidence that one policy may outperform another under different conditions. Hence, it is difficult, or even impossible, to design the "best" policy for all computers. We explain how to select the best policies at runtime without user or administrator intervention by using a software framework called the Homogeneous Architecture for Power Policy Integration (HAPPI). This architecture is portable across different platforms running Linux. HAPPI specifies common requirements for policies and provides an interface to simplify the implementation of policies in a commodity OS. Our approach allows these policies to be compared simultaneously to select the best policy among a set of distinct policies at runtime. Experimental results indicate that HAPPI achieves energy savings within 4 percent of the best individual policy for each device in several computing systems without a priori knowledge of workloads.

Hybrid Power Management for Office Equipment

“…Pettis and Lu [12] introduce power policy selection in an operating system and show one policy outperforming another under some conditions. It may be difficult, or even impossible, to design the "best" policy for all conditions.…”

“…Several other policies employ machine learning techniques to learn the power management policy [8], [9], [10]. Some maintain a set of policies and perform online policy selection [11], [12] based on user specified energy-performance criteria. In case of printers, an adaptive timeout policy is proposed [13], [14] using the estimated distribution of past print requests.…”

“…Office equipment presents unique challenges wherein the wakeup energy and delay are significantly higher than the peripheral devices. The idle power of a printer can be 100 Watts, while a HDD's idle power is around 5 Watts [12]. This thesis has the following contributions:…”

An effective feedback-driven approach for energy saving in battery powered systems

“…To reassign QoS parameters for these old tasks, Manager needs to evaluate the utilization ratio δ j−1 of the prior task and current values of miss ratio M j−1 and data size DS. Then, Manager determines a proper value of RQ S for the reassignment after these evaluations (lines [8][9][10][11][12][13][14][15]. After that, the QoS assignment will be established for all old and new tasks, before it is delivered to Executor …”

“…Our FQM design dynamically adapts and guarantees application QoS based on user/system interaction behaviors. This is different from the power-aware systems of Lu et al [7], [8] which do not aim to guarantee the application QoS, the FCS of Lu et al [9], [10], [11], which is applicable only for adaptive real time systems, the EC of Minerick et al [12], which exploits the voltage and frequency of processor to meet a given energy level, the HAPPI of Pettis et al [13], which automatically simplifies power policies but does not considers all primitive I/Os, the Grace-OS of Yuan et al [2], which requires a predictable CPU scheduling and only exploits particular multimedia tasks, and QoSPM, which only statically maintains application QoS.…”