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Online strategies for dynamic power management in systems with multiple power-saving states

Authors:

Sandy Irani,

Sandeep Shukla,

Rajesh GuptaAuthors Info & Claims

ACM Transactions on Embedded Computing Systems (TECS), Volume 2, Issue 3

Pages 325 - 346

https://doi.org/10.1145/860176.860180

Published: 01 August 2003 Publication History

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Abstract

Online dynamic power management (DPM) strategies refer to strategies that attempt to make power-mode-related decisions based on information available at runtime. In making such decisions, these strategies do not depend upon information of future behavior of the system, or any a priori knowledge of the input characteristics. In this paper, we present online strategies, and evaluate them based on a measure called the competitive ratio that enables a quantitative analysis of the performance of online strategies. All earlier approaches (online or predictive) have been limited to systems with two power-saving states (e.g., idle and shutdown). The only earlier approaches that handled multiple power-saving states were based on stochastic optimization. This paper provides a theoretical basis for the analysis of DPM strategies for systems with multiple power-down states, without resorting to such complex approaches. We show how a relatively simple "online learning" scheme can be used to improve the competitive ratio over deterministic strategies using the notion of "probability-based" online DPM strategies. Experimental results show that the algorithm presented here attains the best competitive ratio in comparison with other known predictive DPM algorithms. The other algorithms that come close to matching its performance in power suffer at least an additional 40% wake-up latency on average. Meanwhile, the algorithms that have comparable latency to our methods use at least 25% more power on average.

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Reviews

Reviewer: John P. Dougherty

The popularity and ubiquity of portable computing devices brings the desire for efficiency to the forefront. Dynamic power management (DPM) refers to strategies that determine when to switch the power state of a device on the fly (in this context, switching it to an online status). This paper presents a DPM algorithm that provides a balance of two qualities in conflict: reduced average energy dissipated and reduced average latency. The online probability based algorithm (OPBA) is a variant of a deterministic approach known as the lower envelope algorithm (LEA). OPBA utilizes a so-called sliding window of recent idle period information to continually update the probability of expected idle time. A comparison of LEA, OBPA, and three other predictive approaches (each in preemptive and nonpreemptive implementations) to an optimal offline algorithm (for example, those based on a priori knowledge of idle periods) is conducted. The test data is based on a mobile hard drive with four power states (sleep, standby, idle, active), each with different power consumption, start-up energy, and transition time to active. The results are convincing that OPBA performs very well (refer to Figure 4 in the paper). Furthermore, the paper presents a proof that LEA is 2-competitive (meaning the cost is no worse than twice the optimal), and a thorough analysis of the expected cost for OPBA. The paper is a complete treatment of the proposed algorithms, with appropriate theoretical foundation, reasonable experimental work, and plausible conclusions. The presentation is well organized. A Web site is provided for others to evaluate DPM approaches using the authors' framework, encouraging future work. Online Computing Reviews Service

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cover image ACM Transactions on Embedded Computing Systems

ACM Transactions on Embedded Computing Systems Volume 2, Issue 3

August 2003

197 pages

ISSN:1539-9087

EISSN:1558-3465

DOI:10.1145/860176

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Association for Computing Machinery

New York, NY, United States

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ACM Journals for the Design of Smart and Connected Systems

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Published: 01 August 2003

Published in TECS Volume 2, Issue 3

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Managing power constraints in a single-core scenario through power tokens

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