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.

Abstract. We generalize the recent relative loss bounds for on-line algorithms where the additional loss of the algorithm on the whole sequence of examples over the loss of the best expert is bounded. The generalization allows the sequence to be partitioned into segments, and the goal is to bound the additional loss of the algorithm over the sum of the losses of the best experts for each segment. This is to model situations in which the examples change and different experts are best for certain segments of the sequence of examples. In the single segment case, the additional loss is proportional to log n, where n is the number of experts and the constant of proportionality depends on the loss function. Our algorithms do not produce the best partition; however the loss bound shows that our predictions are close to those of the best partition. When the number of segments is k +1and the sequence is of length ℓ, we can bound the additional loss of our algorithm over the best partition by O(k log n + k log(ℓ/k)). For the case when the loss per trial is bounded by one, we obtain an algorithm whose additional loss over the loss of the best partition is independent of the length of the sequence. The additional loss becomes O(k log n + k log(L/k)), where L is the loss of the best partition with k +1segments. Our algorithms for tracking the predictions of the best expert are simple adaptations of Vovk’s original algorithm for the single best expert case. As in the original algorithms, we keep one weight per expert, and spend O(1) time per weight in each trial.

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.

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.

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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.

Portable systems require long battery lifetime while still delivering high performance. Dynamic power management (DPM) policies trade off the performance for the power consumption at the system level in portable devices. In this work we present the time-indexed SMDP model (TISMDP) that we use to derive optimal policy for DPM in portable systems. TISMDP model is needed to handle the nonexponential user request interarrival times we observed in practice. We use our policy to control power consumption on three different devices: the SmartBadge portable device [18], the SonyVaio laptop hard disk and WLAN card. Simulation results show large savings for all three devices when using our algorithm. In addition, we measured the power consumption and performance of our algorithm and compared it with other DPM algorithms for laptop hard disk and WLAN card. The algorithm based on our TISMDP model has 1.7 times less power consumption as compared to the default Windows timeout policy for the hard disk and three times less power consumption as compared to the default algorithm for the WLAN card.

this paper, we present the design and implementation of an innovative transport level protocol capable of significantly reducing 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. We present results from experiments using our implementation of the protocol. These experiments measure the energy consumption for three simulated communication patterns as well as three trace-based communication patterns and compare the effects of different suspension strategies. Our results show up to 83% savings in the energy consumed by the communication. For a high-end laptop, this can translate to 6--9% sav

The declining costs of commodity disk drives is rapidly changing the economics of deploying large amounts of online or near-line storage. Conventional mass storage systems use either high performance RAID clusters, automated tape libraries or a combination of tape and disk. In this paper, we analyze an alternative design using massive arrays of idle disks, or MAID. We argue that this storage organization provides storage densities matching or exceeding those of tape libraries with performance similar to disk arrays. Moreover, we show that with effective power management of individual drives, this performance can be achieved using a very small power budget. In particular, we show that our power management strategy can result in the performance comparable to an always-on RAID system while using the power of such a RAID system. 1

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.