Quantitative comparison of power management algorithms

Lu, Yung-Hsiang; Chung, Eui-Young; Simunic, T.; Benini, Luca; Micheli, Giovanni De

doi:10.1145/343647.343688

Cited by 97 publications

(64 citation statements)

References 7 publications

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“…In this section, we briefly describe a power consumption model for disk drives based on the studies [5], [17] (see also the paper [2] for a survey of modeling techniques), and fix the model parameters that we used in our evaluations.…”

Section: Modeling Power Consumptionmentioning

confidence: 99%

Power-Saving in Storage Systems for Cloud Data Sharing Services with Data Access Prediction

Hasebe

Okoshi

Kato

2015

IEICE Trans. Inf. & Syst.

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SUMMARYWe present a power-saving method for large-scale storage systems of cloud data sharing services, particularly those providing media (video and photograph) sharing services. The idea behind our method is to periodically rearrange stored data in a disk array, so that the workload is skewed toward a small subset of disks, while other disks can be sent to standby mode. This idea is borrowed from the Popular Data Concentration (PDC) technique, but to avoid an increase in response time caused by the accesses to disks in standby mode, we introduce a function that predicts future access frequencies of the uploaded files. This function uses the correlation of potential future accesses with the combination of elapsed time after upload and the total number of accesses in the past. We obtain this function in statistical analysis of the real access patterns of 50,000 randomly selected publicly available photographs on Flickr over 7,000 hours (around 10 months). Moreover, to adapt to a constant massive influx of data, we propose a mechanism that effectively packs the continuously uploaded data into the disk array in a storage system based on the PDC. To evaluate the effectiveness of our method, we measured the performance in simulations and a prototype implementation. We observed that our method consumed 12.2% less energy than the static configuration (in which all disks are in active mode). At the same time, our method maintained a preferred response time, with 0.23% of the total accesses involving disks in standby mode.

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Section: Modeling Power Consumptionmentioning

confidence: 99%

Power-Saving in Storage Systems for Cloud Data Sharing Services with Data Access Prediction

Hasebe

Okoshi

Kato

2015

IEICE Trans. Inf. & Syst.

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“…2) ACPI-Based DPM Implementations: A set of experiments were carried out by Lu et al [35], [36] to measure the effectiveness of different DPM policies. Lu used two ACPI-compliant computers, running a beta version of Windows NT V5, which is also ACPI compliant.…”

Section: B System-level Power Management Implementationmentioning

confidence: 99%

“…Experimental results are reported in [36], where a comparative analysis of different algorithms is presented. For comparison purposes, both computers execute the same trace of input data (an 11-h-long execution trace).…”

Section: B System-level Power Management Implementationmentioning

confidence: 99%

A survey of design techniques for system-level dynamic power management

Benini

Bogliolo

Micheli

2000

IEEE Trans. VLSI Syst.

1,007

579

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Abstract-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.

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“…In [7], one can find a quantitative comparison between various existing management strategies. Previous work on prediction based dynamic power management can be categorized into two groups: adaptive and non-adaptive.…”

Section: Introductionmentioning

confidence: 99%

Competitive analysis of dynamic power management strategies for systems with multiple power saving states

Irani

Shukla

Gupta

Proceedings 2002 Design, Automation and Test in Europe Conference and Exhibition

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We present strategies for "online" dynamic power management(DPM) based on the notion of the competitive ratio that allows us to compare the effectiveness of algorithms against an optimal strategy. This paper makes two contributions: it provides a theoretical basis for the analysis of DPM strategies for systems with multiple power down states; and provides a competitive algorithm based on probabilistically generated inputs that improves the competitive ratio over deterministic strategies. Experimental results show that our probability-based DPM strategy improves the efficiency of power management over the deterministic DPM strategy by 25%, bringing the strategy to within 23% of the optimal offline DPM.

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Quantitative comparison of power management algorithms

Cited by 97 publications

References 7 publications

Power-Saving in Storage Systems for Cloud Data Sharing Services with Data Access Prediction

Power-Saving in Storage Systems for Cloud Data Sharing Services with Data Access Prediction

A survey of design techniques for system-level dynamic power management

Competitive analysis of dynamic power management strategies for systems with multiple power saving states

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