Interact hosting centers serve multiple service sites from a common hardware base. This paper presents the design and implementation of an architecture for resource management in a hosting center op-erating system, with an emphasis on energy as a driving resource management issue for large server clusters. The goals are to provi-sion server resources for co-hosted services in a way that automati-cally adapts to offered load, improve the energy efficiency of server dusters by dynamically resizing the active server set, and respond to power supply disruptions or thermal events by degrading service in accordance with negotiated Service Level Agreements (SLAs). Our system is based on an economic approach to managing shared server resources, in which services "bid " for resources as a func-tion of delivered performance. The system continuously moni-tors load and plans resource allotments by estimating the value of their effects on service performance. A greedy resource allocation algorithm adjusts resource prices to balance supply and demand, allocating resources to their most efficient use. A reconfigurable server switching infrastructure directs request traffic to the servers assigned to each service. Experimental results from a prototype confirm that the system adapts to offered load and resource avail-ability, and can reduce server energy usage by 29 % or more for a typical Web workload. 1.

In this paper, we demonstrate that a collaborative relationship between the operating system and applications can be used to meet user-specified goals for battery duration. We first show how applications can dynamically modify their behavior to conserve energy. We then show how the Linux operating system can guide such adaptation to yield a batterylife of desired duration. By monitoring energy supply and demand, it is able to select the correct tradeoff between energy conservation and application quality. Our evaluation shows that this approach can meet goals that extend battery life by as much as 30%.

Dynamic power management (DPM) is a design methodology for dynamically reconfiguring systems to provide the requested services and performance levels with a minimum number of active components or a minimum load on such components. DPM encompasses a set of techniques that achieves energy-efficient computation by selectively turning off (or reducing the performance of) system components when they are idle (or partially unexploited). In this paper, we survey several approaches to system-level dynamic power management. We first describe how systems employ power-manageable components and how the use of dynamic reconfiguration can impact the overall power consumption. We then analyze DPM implementation issues in electronic systems, and we survey recent initiatives in standardizing the hardware/software interface to enable software-controlled power management of hardware components.

We address the problem of deciding when to spin down the disk of a mobile computer in order to extend battery life. Since one of the most critical resources in mobile computing environments is battery life, good energy conservation methods can dramatically increase the utility of mobile systems. We use a simple and efficient algorithm based on machine learning techniques that has excellent performance in practice. Our experimental results are based on traces collected from HP C2474s disks. Using this data, the algorithm outperforms several algorithms that are theoretically optimal in under various worst-case assumptions, as well as the best fixed time-out strategy. In particular, the algorithm reduces the power consumption of the disk to about half (depending on the disk's properties) of the energy consumed by a one minute fixed time-out. Since the algorithm adapts to usage patterns, it uses as little as 88% of the energy consumed by the best fixed time-out computed in retrospect. 1 In...

Energy consumption has recently been widely recognized as a major challenge of computer systems design. This paper explores how to support energy as a first-class operating system resource. Energy, because of its global system nature, presents challenges beyond those of conventional resource management. To meet these challenges we propose the Currentcy Model that unifies energy accounting over diverse hardware components and enables fair allocation of available energy among applications. Our particular goal is to extend battery lifetime by limiting the average discharge rate and to share this limited resource among competing tasks according to user preferences. To demonstrate how our framework supports explicit control over the battery resource we implemented ECOSystem, a modified Linux, that incorporates our currentcy model. Experimental results show that ECOSystem accurately accounts for the energy consumed by asynchronous device operation, can achieve a target battery lifetime, and proportionally shares the limited energy resource among competing tasks.

A large portion of the power budget in server environments goes into the I/O subsystem- the disk array in particular. Traditional approaches to disk power management involve completely stopping the disk rotation, which can take a considerable amount of time, making them less useful in cases where idle times between disk requests may not be long enough to outweigh the overheads. This paper presents a new approach called DRPM to modulate disk speed (RPM) dynamically, and gives a practical implementation to exploit this mechanism. Extensive simulations with different workload and hardware parameters show that DRPM can provide significant energy savings without compromising much on performance. This paper also discusses practical issues when implementing DRPM on server disks. Keywords: Server Disks, Power Management. 1

One of the major challenges of post-PC computing is the need to reduce energy consumption, thereby extending the lifetime of the batteries that p ower these mobile devices. Memory is a particularly important tar get for e orts to improve energy e ciency. Memory technolo gy is becoming available that o ers power management featur es such as the ability to put individual chips in any one of several di erent power modes. In this paper we explor e the interaction of page plac ement with static and dynamic hardware policies to exploit these emer ginghardwar efeatur es. In p articular, we c onsider p age allo cation p olicies that ancbe employed by an informed operating system to complement the hardware power management strategies. We perform experiments using two complementary simulation envir onments: a tracedriven simulator with workload traces that are representative of mobile computing and an execution-driven simulator with a detaile d processor/memory model and a more memoryintensive set of benchmarks (SPEC2000). Our r esults make a compelling case for a cooperative hardwar e/software approach for exploiting power-aware memory, with down to as little as 45 % of the Energy Delay for the best static policy and 1 % to 20 % of the Ener gyDelay for a traditional fullpower memory. 1.

In mobile computing, power is a limited resource. Like other devices, communication devices need to be properly managed to conserve energy. In this paper, we present the design and implementation of an innovative transport level protocol capable of significantly reduc- ing the power usage of the communication device. The protocol achieves power savings by selectively choosing short periods of time to suspend communications and shut down the communication device. It manages the important task of queuing data for future delivery during periods of communication suspension, and decides when to restart communication. We also address the tradeoff between reducing power consumption and reducing delay for incoming data.

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Limiting the energy consumption of computers, especially portables, is becoming increasingly important. Thus, new energy-saving computer components and architectures have been and continue to be developed. Many architectural features have both high-performance and low-power modes, with the mode selection under software control. The problem is to minimize energy consumption while not significantly impacting the effective performance. We group the software control issues as follows: transition, load-change, and adaptation. The transition problem is deciding when to switch to low-power, reduced-functionality modes. The load-change problem is determining how to modify the load on a component so that it can make further use of its low-power modes. The adaptation problem is determining how to create software that allows components to be used in novel, power-saving ways. We survey implemented and proposed solutions to software energy management issues created by existing and suggested hardware innovations.