In this paper we address power conservation for clusters of workstations or PCs. Our approach is to develop systems that dynamically turn cluster nodes on -- to be able to handle the load imposed on the system efficiently -- and off -- to save power under lighter load. The key component of our systems is an algorithm that makes load balancing and unbalancing decisions by considering both the total load imposed on the cluster and the power and performance implications of turning nodes off. The algorithm is implemented in two different ways: (1) at the application level for a cluster-based, localityconscious network server; and (2) at the operating system level for an operating system for clustered cycle servers. Our experimental results are very favorable, showing that our systems conserve both power and energy in comparison to traditional systems.

Despite constant improvements in fabrication technology, hardware components are consuming more power than ever. With the everincreasing demand for higher performance in highly-integrated systems, and as battery technology falls further behind, managing energy is becoming critically important to various embedded and mobile systems. In this paper, we propose and implement power-aware virtual memory to reduce the energy consumed by the memory in response to workloads becoming increasingly data-centric. We can use the power management features in current memory technology to put individual memory devices into low power modes dynamically under software control to reduce the power dissipation. However, it is imperative that any techniques employed weigh memory energy savings against any potential energy increases in other system components due to performance degradation of the memory. Using a novel power-aware virtual memory implementation, we show a significant reduction in memory power dissipation, from 4.1 W to 0.5--2.7 W, when using Rambus memory and running various real-world applications in a working Linux system. Applying more advanced techniques, we can reduce this further to 0.2--1.7 W, depending on the actual workload, with negligible effects on performance. We also show this work is applicable to other memory architectures, and is orthogonal to previouslyproposed hardware-controlled power-management techniques, so it can be applied simultaneously to further enhance energy conservation in a variety of platforms.

Many portable systems deploy operating systems (OS) to support versatile functionality and to manage resources, including power. This paper presents a new approach for using OS to reduce the power consumption of IO devices in interactive systems. Low-power OS observes the relationship between hardware devices and processes. The OS kernel estimates the utilization of a device from each process. If a device is not used by any running process, the OS puts it into a low-power state. This paper also explains how scheduling can facilitate power management. When processes are properly scheduled, power reduction can be achieved without degrading performance. We implemented a prototype on Linux to control two devices; experimental results showed nearly 70% power saving on a network card and a hard disk drive.

This paper describes a comprehensive approach for using the memory controller to improve DRAM energy efficiency and manage DRAM power. We make three contributions: (1) we describe a simple power-down policy for exploiting low power modes of modern DRAMs; (2) we show how the idea of adaptive history-based memory schedulers can be naturally extended to manage power and energy; and (3) for situations in which additional DRAM power reduction is needed, we present a throttling approach that arbitrarily reduces DRAM activity by delaying the issuance of memory commands. Using detailed microarchitectural simulators of the IBM Power5+ and a DDR2-533 SDRAM, we show that our first two techniques combine to increase DRAM energy efficiency by an average of 18.2%, 21.7%, 46.1%, and 37.1 % for the Stream, NAS, SPEC2006fp, and commercial benchmarks, respectively. We also show that our throttling approach provides performance that is within 4.4 % of an idealized oracular approach. 1

Typically, power reduction is conducted by hardware techniques, such as varying clock frequencies and/or supply voltages. However, hardware devices consume power to serve the requests from software programs. Consequently, it is essential to consider software for power reduction. This paper proposes "requester-aware" power reduction through the collaboration with programs. Experimental results show that this approach can save nearly 70% power with negligible performance degradation.

Previous work on software optimization for low energy has focussed on instruction-level optimizations and compiler techniques. We argue, and demonstrate, that significant energy savings could be "left on the table" if energy is not considered during the design of the software architecture. As a first step towards addressing this gap, we propose a systematic framework for software architectural transformations to reduce energy consumption.

Data compression has been claimed to be an attractive solution to save energy consumption in high-end servers and data centers. However, there has not been a study to explore this. In this paper, we present a comprehensive evaluation of energy consumption for various file compression techniques implemented in software. We apply various compression tools available on Linux to a variety of data files, and we try them on server class and workstation class systems. We compare their energy and performance results against raw reads and writes. Our results reveal that software based data compression cannot be considered as a universal solution to reduce energy consumption. Various factors like the type of the data file, the compression tool being used, the read-to-write ratio of the workload, and the hardware configuration of the system impact the efficacy of this technique. In some cases, however, we found compression to save substantial energy and improve performance.

Abstract — With the total energy consumption of computing systems increasing in a steep rate, much attention has been paid to the design of energy-efficient computing systems and applications. So far, database system design has focused on improving performance of query processing. The objective of this study is to experimentally explore the potential of power conservation in relational database management systems. We hypothesize that, by modifying the query optimizer in a DBMS to take the power cost of query plans into consideration, we will be able to reduce the power usage of database servers and control the tradeoffs between power consumption and system performance. We also identify the sources of such savings by investigating the resource consumption features during query processing in DBMSs. To that end, we provide an in-depth anatomy and qualitatively analyze the power profile of typical queries in the TPC benchmarks. We perform extensive experiments on a physical testbed based on the PostgreSQL system using workloads generated from the TPC benchmarks. Our hypothesis is supported by such experimental results: power savings in the range of 11 %- 22 % can be achieved by equipping the DBMS with a query optimizer that selects query plans based on both estimated processing time and power requirements. I.

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Despite constant improvements in fabrication technology, hardware components are consuming more power than ever. With the everincreasing demand for higher performance in highly-integrated systems, and as battery technology falls further behind, managing energy is becoming critically important to various embedded and mobile systems. In this paper, we propose and implement power-aware virtual memory to reduce the energy consumed by the memory in response to workloads becoming increasingly data-centric. We can use the power management features in current memory technology to put individual memory devices into low power modes dynamically under software control to reduce the power dissipation. However, it is imperative that any techniques employed weigh memory energy savings against any potential energy increases in other system components due to performance degradation of the memory. Using a novel power-aware virtual memory implementation, we show a significant reduction in memory power dissipation, from 4.1 W to 0.5--2.7 W, when using Rambus memory and running various real-world applications in a working Linux system. Applying more advanced techniques, we can reduce this further to 0.2--1.7 W, depending on the actual workload, with negligible effects on performance. We also show this work is applicable to other memory architectures, and is orthogonal to previouslyproposed hardware-controlled power-management techniques, so it can be applied simultaneously to further enhance energy conservation in a variety of platforms.