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Dynamic power management based on continuous-time Markov decision processes

Authors:

Qinru Qiu,

Massoud PedramAuthors Info & Claims

DAC '99: Proceedings of the 36th annual ACM/IEEE Design Automation Conference

Pages 555 - 561

https://doi.org/10.1145/309847.309997

Published: 01 June 1999 Publication History

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References

[1]

A. Chandrakasan, R. Brodersen, Low Power Digital CMOS Design, Kluwer Academic Publishers, July 1995.

Digital Library

Google Scholar

[2]

M. Horowitz, T. Indermaur, and R. Gonzalez, "Low-Power Digital Design", IEEE Symposium on Low Power Electronics, pp.8-11, 1994.

Crossref

Google Scholar

[3]

A. Chandrakasan, V. Gutnik, and T. Xanthopoulos, "Data Driven Signal Processing: An Approach for Energy Efficient Computing", 1996 International Symposium on Low Power Electronics and Design, pp. 347-352, Aug. 1996.

Digital Library

Google Scholar

[4]

J. Rabaey and M. Pedram, Low Power Design Methodologies, Kluwer Academic Publishers, 1996

Digital Library

Google Scholar

[5]

L. Benini and G. De Micheli, Dynamic Power Management: Design Techniques and CAD Tools, Kluwer Academic Publishers, 1997.

Digital Library

Google Scholar

[6]

Intel, Microsoft and Toshiba, "Advanced Configuration and Power Interface specification", URL: http://www.intel.com/ial{powemlgm{specs.html, 1996

Google Scholar

[7]

U. Narayan Bhat, "Elements Of Applied Stochastic Processes", John Wiley & Sons, Inc. 1984

Google Scholar

[8]

B. Miller, "Finite State Continuous Time Markov Decision Processes With an Finite Planning Horizon." SIAM J. Control, Vol. 5, No. 2, pp. 266-281, 1968.

Crossref

Google Scholar

[9]

B. Miller, "Finite State Continuous Time Markov Decision Processes With an Infinite Planning Horizon". J. Of Mathematical Analysis and Applications, No. 22, pp. 552-569, 1968.

Google Scholar

[10]

R.A.Howard, Dynamic Programming and Markov Processes, Wiley, New York, 1960

Google Scholar

[11]

G. A. Paleologo, L. Benini, et.al, "Policy Optimization for Dynamic Power Management", Proceedings of Design Automation Conference, pp. 182-187, Jun. 1998.

Digital Library

Google Scholar

[12]

D. P. Heyman, M. J. Sobel, Stochastic Models in Operations Research, McGraw-Hill Book Company, 1982

Google Scholar

[13]

L. Benini, A. Bogliolo, S. Cavallucci, B. Ricco, "Monitoring System Activity For OS-Directed Dynamic Power Management", Proceedings of International Symposium of Low Power Electronics and Design Conference, pp. 185-190, Aug. 1998.

Digital Library

Google Scholar

[14]

L. Benini, R. Hodgson, P. Siegel, "System-level Estimation And Optimization", Proceedings of International Symposium of Low Power Electronics and Design Conference, pp. 173-178, Aug. 1998.

Digital Library

Google Scholar

[15]

G. Bolch~ S. Greiner, I4. D. Meer and K. S. Trivedi, Queueing Networks and Markov Chains, John Wiley & Sons, Inc., 1998

Digital Library

Google Scholar

[16]

M. Srivastava, A. Chandrakasan. R. Brodersen, "Predictive system shutdown and other architectural techniques for energy efficient programmable computation," IEEE Transactions on VLSI Systems, Vol. 4, No. 1 (1996), pages 42-55.

Digital Library

Google Scholar

[17]

C.-H. Hwang and A. Wu, "A Predictive System Shutdown Method for Energy Saving of Event-Driven Computation," Proc. of the Intl. Conference on Computer Aided Design, pages 28-32, November 1997.

Digital Library

Google Scholar

[18]

Q. Qiu, Q. Wu and M. Pedram, "Dynamic Power management: A Continuous-Time Stochastic Approach", USC EE-Systems Dept., CENG 99-02.

Google Scholar

Cited By

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Alasmari NCalinescu R(2022)Synthesis of Pareto-optimal Policies for Continuous-Time Markov Decision Processes2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)10.1109/SEAA56994.2022.00071(414-417)Online publication date: Aug-2022
https://doi.org/10.1109/SEAA56994.2022.00071
Kallimani RPai KRasane K(2020)Stochastic Model of a Sensor NodeNext Generation Information Processing System10.1007/978-981-15-4851-2_26(242-250)Online publication date: 14-Jun-2020
https://doi.org/10.1007/978-981-15-4851-2_26
Ma JXia LLi Q(2019)Optimal energy-efficient policies for data centers through sensitivity-based optimizationDiscrete Event Dynamic Systems10.1007/s10626-019-00293-xOnline publication date: 29-Oct-2019
https://doi.org/10.1007/s10626-019-00293-x
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Index Terms

Dynamic power management based on continuous-time Markov decision processes
1. Hardware
  1. Electronic design automation
    1. Physical design (EDA)

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Published In

DAC '99: Proceedings of the 36th annual ACM/IEEE Design Automation Conference

June 1999

1000 pages

ISBN:1581131097

DOI:10.1145/309847

Editor:
Mary Jane Irwin

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 01 June 1999

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DAC99

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IEEE-CAS

DAC99: The 36th ACM/IEEE-CAS/EDAC Design Automation Conference

June 21 - 25, 1999

Louisiana, New Orleans, USA

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DAC '99 Paper Acceptance Rate 154 of 451 submissions, 34%;

Overall Acceptance Rate 1,770 of 5,499 submissions, 32%

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June 22 - 26, 2025

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Cited By

View all

Alasmari NCalinescu R(2022)Synthesis of Pareto-optimal Policies for Continuous-Time Markov Decision Processes2022 48th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)10.1109/SEAA56994.2022.00071(414-417)Online publication date: Aug-2022
https://doi.org/10.1109/SEAA56994.2022.00071
Kallimani RPai KRasane K(2020)Stochastic Model of a Sensor NodeNext Generation Information Processing System10.1007/978-981-15-4851-2_26(242-250)Online publication date: 14-Jun-2020
https://doi.org/10.1007/978-981-15-4851-2_26
Ma JXia LLi Q(2019)Optimal energy-efficient policies for data centers through sensitivity-based optimizationDiscrete Event Dynamic Systems10.1007/s10626-019-00293-xOnline publication date: 29-Oct-2019
https://doi.org/10.1007/s10626-019-00293-x
Li QMa JFan RXia L(2019)An Overview for Markov Decision Processes in Queues and NetworksStochastic Models in Reliability, Network Security and System Safety10.1007/978-981-15-0864-6_3(44-71)Online publication date: 16-Oct-2019
https://doi.org/10.1007/978-981-15-0864-6_3
Yu LZhou ZFan YPapka MLan Z(2018)System-wide trade-off modeling of performance, power, and resilience on petascale systemsThe Journal of Supercomputing10.1007/s11227-018-2368-874:7(3168-3192)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.1007/s11227-018-2368-8
Turkin IVdovitchenko O(2018)Model and Methods of Human-Centered Personal Computers Adaptive Power ControlGreen IT Engineering: Social, Business and Industrial Applications10.1007/978-3-030-00253-4_14(323-348)Online publication date: 30-Sep-2018
https://doi.org/10.1007/978-3-030-00253-4_14
Logeshwaran JRex MKiruthiga TRajan V(2017)FPSMM: Fuzzy probabilistic based semi morkov model among the sensor nodes for realtime applications2017 International Conference on Intelligent Sustainable Systems (ICISS)10.1109/ISS1.2017.8389449(442-446)Online publication date: Dec-2017
https://doi.org/10.1109/ISS1.2017.8389449
Logeshwaran JRex MKiruthiga TRajan V(2017)FPSMM : Fuzzy probabilistic based semi morkov model among the sensor nodes for realtime applications2017 International Conference on Intelligent Sustainable Systems (ICISS)10.1109/ISS1.2017.8389391(163-167)Online publication date: Dec-2017
https://doi.org/10.1109/ISS1.2017.8389391
Pughat ASharma V(2017)Performance analysis of an improved dynamic power management model in wireless sensor nodeDigital Communications and Networks10.1016/j.dcan.2016.10.0083:1(19-29)Online publication date: Feb-2017
https://doi.org/10.1016/j.dcan.2016.10.008
Zéphyr LAnderson C(2017)Stochastic dynamic programming approach to managing power system uncertainty with distributed storageComputational Management Science10.1007/s10287-017-0297-215:1(87-110)Online publication date: 30-Dec-2017
https://doi.org/10.1007/s10287-017-0297-2
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