Xiaodong Li
Yuanyuan Zhou
Sarita Adve
Abstract
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.This article 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 four main contributions. (1) We propose a technique that provides a performance guarantee for control algorithms. We show that our method works well for all tested cases, even with previously proposed algorithms that are not performance-aware. (2) We propose a new control algorithm, Performance-Directed Dynamic (PD), that dynamically adjusts its thresholds periodically, based on available slack and recent workload characteristics. For memory, PD consumes the least energy when compared to previous hand-tuned algorithms combined with a performance guarantee. However, for disks, PD is too complex and its self-tuning is unable to beat previous hand-tuned algorithms. (3) To improve on PD, we propose a simpler, optimization-based, threshold-free control algorithm, Performance-Directed Static (PS). PS periodically assigns a static configuration by solving an optimization problem that incorporates information about the available slack and recent traffic variability to different chips/disks. We find that PS is the best or close to the best across all performance-guaranteed disk algorithms, including hand-tuned versions. (4) We also explore a hybrid scheme that combines PS and PD algorithms to further improve energy savings.
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Index Terms
- Performance directed energy management for main memory and disks
Recommendations
Performance directed energy management for main memory and disks
ASPLOS '04Much 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 ...
Performance directed energy management for main memory and disks
ASPLOS '04Much 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 ...
Performance directed energy management for main memory and disks
ASPLOS 2004Much 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 ...
Reviews
Michael Zastre
Anyone who has used devices with several energy-saving modes is aware of the latencies introduced, such as disk drives that take some time to spin up, or programs that become unexpectedly sluggish as memory chips take a break from their naps. High-performance servers are usually configured more cleverly than desktop machines, but finding the right tradeoff between saving energy and producing acceptable performance has required manual tuning for each server application. These threshold values can be inappropriate when used for another program or workload. What this well-written and well-organized paper introduces are techniques and algorithms that eliminate the need for this manual tuning, while taking into account specific performance guarantees specified for the energy-managed computer system. Section 1 describes the problem context and sketches out the proposed solutions. Section 2 outlines the power models used for memory and for disk, and then lists some of the existing control algorithms that they will be compared with. Sections 3 and 4 are the core of the paper, as they establish the precise meaning of "performance guarantee" for memory (relatively straightforward) and disk (more complex given the number parameters), and then provide efficient algorithms for setting device energy management parameters that take into account guarantees and current device usage. Sections 5 and 6 present and analyze experimental results obtained from simulators. Sections 7, 8, and 9 are devoted to some work on a hybrid "static + dynamic" algorithm, brief summaries of related work, and a discussion of future work. There are several key ideas presented here. One is that of "slack," or "the amount of allowed execution delay" introduced by energy management that still permits performance guarantees to be met. This idea is combined with that of an "epoch," a sequence of device accesses (about a million such accesses for memory, and around 100 seconds for disk drives). Unused slack in one epoch can be used in a later epoch, and this allows energy management settings to differ from epoch to epoch (for performance-directed static (PS) control algorithms) or within an epoch (for performance-directed dynamic (PD) control algorithms). At the start of each epoch, the PS algorithm determines settings via optimization so that the energy savings over all devices are maximized while ensuring the sum of energy management-induced delays across all devices is less than the available slack. The PD algorithm computes threshold values at the start of each epoch, again chosen for all devices such that available slack is not exceeded. Both PS and PD use earlier epochs as predictions of expected device usage. Both PS and PD produce better results than other schemes, although PS is better for memory devices, while PD is better for disk devices. A hybrid scheme combining both PS and PD is investigated, but the authors report mixed results. Online Computing Reviews Service
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