Power and energy consumption are key concerns for Internet data centers. These centers house hundreds, sometimes thousands, of servers and supporting cooling infrastructures. Research on power and energy management for servers can ease data center installation, reduce costs, and protect the environment. Given these benefits, researchers have made important strides in conserving energy in servers. Inspired by this initial progress, researchers are delving deeper into this topic. In this paper, we detail the motivation for this research, survey the previous work, describe a few ongoing efforts, and discuss the challenges that lie ahead. 1

Traditional disk management strategies---prefetching and caching in particular---are designed to maximize performance. In mobile systems they conflict with strategies that attempt to save energy by powering down the disk when it is idle. We present new rules for prefetching and caching that maximize power-down opportunities (without performance loss) by creating an access pattern characterized by intense bursts of activity separated by long idle times. We also describe an automatic system that monitors past application behavior in order to generate appropriate prefetching hints, and a general system of kernel enhancements that coordinate I/O activity across all running applications. We have

OS resource management policies traditionally employ buffering to “smooth out ” fluctuations in resource demand. By minimizing the length of idle periods and the level of contention during non-idle periods, such smoothing tends to maximize overall throughput and minimize the latency of individual requests. For certain important devices, however (disks, network interfaces, or even computational elements), smoothing eliminates opportunities to save energy using low-power modes. As devices with such modes proliferate, and as energy efficiency becomes an increasingly important design consideration, we argue that OS policies should be redesigned to increase burstiness for energysensitive devices. We are currently experimenting with techniques to increase the disk access pattern burstiness of the Linux operating system. Our results indicate that the deliberate creation of bursty activity can save up to 78.5 % of the energy consumed by a Hitachi DK23DA disk (in comparison with current policies), while simultaneously decreasing the negative impact of disk congestion and spin-up latency on application performance. 1.

Energy consumption is an important concern at data centers, where storage systems consume a significant fraction of the total energy. A recent study proposed power-aware storage cache management to provide more opportunities for the underlying disk power management scheme to save energy. However, the on-line algorithm proposed in that study requires cumbersome parameter tuning for each workload and is therefore difficult to apply to real systems.

In a mobile computer the hard disk consumes a considerable amount of energy. Existing dynamic power management policies usually take conservative approaches to save disk energy, and disk energy consumption remains a serious issue. Meanwhile, the flash drive is becoming a must-have portable storage device for almost every laptop user on travel. In this paper, we propose to make another highly desired use of the flash drive — saving disk energy. This is achieved by using the flash drive as a standby buffer for caching and prefetching disk data. Our design significantly extends disk idle times with careful and deliberate consideration of the particular characteristics of the flash drive. Trace-driven simulations show that up to 41 % of disk energy can be saved with a relatively small amount of data written to the flash drive.

Current approaches to power management are based on operating systems with full knowledge of and full control over the underlying hardware; the distributed nature of multi-layered virtual machine environments renders such approaches insufficient. In this paper, we present a novel framework for energy management in modular, multi-layered operating system structures. The framework provides a unified model to partition and distribute energy, and mechanisms for energy-aware resource accounting and allocation. As a key property, the framework explicitly takes the recursive energy consumption into account, which is spent, e.g., in the virtualization layer or subsequent driver components. Our prototypical implementation targets hypervisor-based virtual machine systems and comprises two components: a host-level subsystem, which controls machine-wide energy constraints and enforces them among all guest OSes and service components, and, complementary, an energy-aware guest operating system, capable of fine-grained applicationspecific energy management. Guest level energy management thereby relies on effective virtualization of physical energy effects provided by the virtual machine monitor. Experiments with CPU and disk devices and an external data acquisition system demonstrate that our framework accurately controls and stipulates the power consumption of individual hardware devices, both for energy-aware and energyunaware guest operating systems. 1

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Much research has been conducted on energy management for memory and disks. Most studies use control algorithms that dynamically transition devices to low power modes after they are idle for a certain threshold period of time. The control algorithms used in the past have two major limitations. First, they require painstaking, application-dependent manual tuning of their thresholds to achieve energy savings without significantly degrading performance. Second, they do not provide performance guarantees. In one case, they slowed down an application by 835%! This paper addresses these two limitations for both memory and disks, making memory/disk energy-saving schemes practical enough to use in real systems. Specifically, we make three contributions: (1) We propose a technique that provides a performance guarantee for control algorithms. We show that our method works well for

Hard disks for portable devices, and the operating systems that manage them, incorporate spin-down policies that idle the disk after a certain period of inactivity. In essence, these policies use a recent period of inactivity to predict that the disk will remain inactive in the near future. We propose an alternative strategy, in which the operating system deliberately seeks to cluster disk operations in time, to maximize the utilization of the disk when it is spun up and the time that the disk can be spun down. In order to cluster disk operations we postpone the service of non-urgent operations, and use aggressive prefetching and file prediction to reduce the likelihood that synchronous reads will have to go to disk. In addition, we present a novel predictive spin-down/spin-up policy that exploits high level operating system knowledge to decrease disk idle time prior to spin-down, and application wait time due to spin-up. We evaluate our strategy through trace-driven simulation of several different workload scenarios. Our results indicate that the deliberate creation of bursty activity can save up to 55% of the energy consumed by an IBM TravelStar disk, while simultaneously decreasing significantly the negative impact of disk spin-up latency on application performance.

Reducing energy consumption has become one of the major challenges in designing future computing systems. This paper proposes a novel idea of using program counters to predict I/O activities in the operating system. The paper presents a complete design of Program-Counter Access Predictor (PCAP) that dynamically learns the access patterns of applications and predicts when an I/O device can be shut down to save energy. PCAP uses path-based correlation to observe a particular sequence of program counters leading to each idle period, and predicts future occurrences of that idle period. PCAP differs from previously proposed shutdown predictors in its ability to: (1) correlate I/O operations to particular behavior of the applications and users, (2) carry prediction information across multiple executions of the applications, and (3) attain better energy savings while incurring low mispredictions. 1.