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.

Previous work demonstrates that migrating files between disks may induce idleness in servers and facilitate power management. However, these studies focus on steady-state power savings and do not consider the energy consumption and performance overhead of migrating files between disks. In this study, we construct a constrained optimization problem of file location for high-bandwidth applications by considering file migration energy, as well as steady-state energy consumption. We present an algorithm that improves quality-of-service by 63% for a streaming video server compared with existing techniques while maintaining comparable energy savings.

In this paper, a method for reducing the power consumption in a pipeline of streaming data is presented. The pipeline is divided into multiple stages and buffer memories are inserted between adjacent stages. By dynamically changing the power state of each stage, the overall power consumption of the pipeline, including the power consumed by each stage and by each buffer, can be reduced. For example, when a buffer is full, the stage immediately before it can be turned off for a certain time period to save power. In this paper, the problem of finding the optimal strategy for managing the power state to minimize average power consumption is studied for a pipeline of data consisting of three stages with two buffers in between. The problem is formulated as an optimal control problem of a hybrid system, namely, a dynamical system with both continuous dynamics and discrete transitions. Several important properties of the optimal solutions are derived. Using these properties, the optimal periodic solutions are obtained analytically for the special case when each stage is allowed to turn on and off at most once during each period. Simulation results using practical data are presented. The results show substantial power savings of the proposed method over heuristic buffering strategies

A significant volume of research has concentrated on operating-system directed power management (OSPM). The primary focus of previous research has been the development of OSPM policies. Under different conditions, one policy may outperform another and vice versa. In this paper, we explain how to select the best policies at run-time without user or administrator intervention. We present a hardware-neutral architecture portable across different platforms running Linux. Our experiments reveal that changing policies at run-time can adapt to workloads more quickly than using any of the policies individually.

This paper presents a method that uses data buffers to create long periods of idleness to exploit power management. This method considers the power consumed by the buffers and assigns an energy penalty for buffer underflow. Our approach provides analytic formulas for calculating the optimal buffer sizes without subjective or heuristic decisions. We simulate four different hardware configurations with MPEG-1, MPEG-2, and MPEG-4 formats as a case study. Our results indicate that the optimal buffer size varies significantly for different data formats on different hardware. Simulation results indicate that 16 MB buffers are sufficient for MPEG-1, MPEG-2, and MPEG-4 video streams from a microdrive or a network card, but transfers from an IDE disk require buffer sizes ranging from 16 MB to 176 MB, depending on each video's statistical properties.

This paper presents a method to combine memory resizing and disk shutdown to achieve better energy savings than can be achieved individually. The method periodically adjusts the size of physical memory and the timeout value to shut down a hard disk to reduce the average energy consumption. Pareto distributions are used to model the disk idle time. The parameters of the distributions are estimated at runtime and used to calculate the appropriate timeout value. The memory size is changed based on the predicted number of disk accesses at different memory sizes. The method also considers the delay caused by power management and limits the performance degradation. The method is simulated and compared with other power management methods. Simulation results show that the method consistently achieves better energy savings and less performance degradation across different workloads

This paper presents a method that uses data buffers to smoothen request variations and to create long idleness for power management. This method considers the power consumed by the buffers and assigns an energy penalty for buffer underflow. Our approach provides analytic formulas for calculating the optimal buffer sizes and the amount of data to store in the buffers. We use video prefetching as a case study and obtain power savings of more than 74% for MPEG-1 and 34% for MPEG-2 videos.