Article

Task scheduling and voltage selection for energy minimization

Authors:

Xiaobo Sharon Hu,

Danny Z. ChenAuthors Info & Claims

DAC '02: Proceedings of the 39th annual Design Automation Conference

Pages 183 - 188

https://doi.org/10.1145/513918.513966

Published: 10 June 2002 Publication History

Abstract

In this paper, we present a two-phase framework that integrates task assignment, ordering and voltage selection (VS) together to minimize energy consumption of real-time dependent tasks executing on a given number of variable voltage processors. Task assignment and ordering in the first phase strive to maximize the opportunities that can be exploited for lowering voltage levels during the second phase, i.e., voltage selection. In the second phase, we formulate the VS problem as an Integer Programming (IP) problem and solve the IP efficiently. Experimental results demonstrate that our framework is very effective in executing tasks at lower voltage levels under different system configurations.

References

[1]

L. Benini, A. Bogliolo, and G. De Micheli, "A survey of design techniques for system-level dynamic power management," IEEE Transactions on VLSI systems, June 2000, pp. 299--316.

Digital Library

[2]

T. Burd, T. Pering, A. Stratakos, and R. Brodersen, "A dynamic voltage scaled microprocessor system," IEEE Journal of Solid-State Circuits, vol. 35, 2000, pp. 1571--1580.

[3]

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

Digital Library

[4]

W. Chuang, S. Sapatnekar, and I. Hajj, "Delay and area optimization for discrete gate sizes under double-sided timing constraints,"

[5]

http://dollar.biz.uiowa.edu/col.

[6]

ftp://ftp.es.ele.tue.nl/pub/lp_solve.

[7]

F. Gruian, K. Kuchcinski, "LEneS: task scheduling for low-energy systems using variable supply voltage processors,"

Digital Library

[8]

D. Hochbaum and J. Shanthikumar, "Convex separable optimization is not much harder than linear optimization," Journal of ACM, vol. 37, No. 4, 1990, pp. 843--862.

Digital Library

[9]

I. Hong, D. Kirovski, G. Qu, M. Potkonjak, and M. Srivastava, "Power optimization of variable voltage core-based systems,"

Digital Library

[10]

http://www.chips.ibm.com:80/products/powerpc/chips.

[11]

T. Ishihara and H. Yasuura, "Voltage scheduling problem for dynamically variable voltage processors,"

Digital Library

[12]

Y. Lin, C. Hwang and A. Wu, "Scheduling techniques for variable voltage low power designs," ACM Transaction on Design Automation of Electronic Systems, vol. 2, no. 2, 1997, pp. 81--97.

Digital Library

[13]

C. Liu and J. Layland, "Scheduling algorithms for multiprogramming in a hard-real-time environment," Journal of the ACM, vol. 20, no. 1, 1973, pp. 46--61.

Digital Library

[14]

J. Luo and N. Jha, "Power-conscious joint scheduling of periodic task graphs and a periodic tasks in distributed real-time embedded systems,"

[15]

N. Namgoong, M. Yu, and T. Meng, "A high-efficiency variable-voltage CMOS dynamic DC-DC switching regulator,"

[16]

G. Quan and X. Hu, "Energy efficient fixed-priority scheduling for real time systems on voltage variable processors," DAC'01, pp. 828--833.

Digital Library

[17]

http://helsinki.ee.Princeton.EDU/~dickrp/tgff

Cited By

Escobar JOrtega JDíaz AGonzález JDamas M(2019)Time-energy analysis of multilevel parallelism in heterogeneous clustersThe Journal of Supercomputing10.1007/s11227-019-02908-475:7(3397-3425)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1007/s11227-019-02908-4
Comito C(2019)Energy Efficiency in Big Data AnalysisEncyclopedia of Big Data Technologies10.1007/978-3-319-77525-8_141(715-722)Online publication date: 20-Feb-2019
https://doi.org/10.1007/978-3-319-77525-8_141
Garg RShukla N(2018)Energy Efficient Scheduling for Multiple Workflows in Cloud EnvironmentInternational Journal of Information Technology and Web Engineering10.4018/IJITWE.201807010213:3(14-34)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.4018/IJITWE.2018070102
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Index Terms

Task scheduling and voltage selection for energy minimization
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Search methodologies
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

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Published In

cover image ACM Conferences

DAC '02: Proceedings of the 39th annual Design Automation Conference

June 2002

956 pages

ISBN:1581134614

DOI:10.1145/513918

General Chair:
Bryan Ackland
Agere Systems, Inc., Holmdel, NJ

Copyright © 2002 ACM.

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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SIGDA: ACM Special Interest Group on Design Automation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 June 2002

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DAC02

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SIGDA

DAC02: 39th Design Automation Conference

June 10 - 14, 2002

Louisiana, New Orleans, USA

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DAC '02 Paper Acceptance Rate 147 of 491 submissions, 30%;

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

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Cited By

Escobar JOrtega JDíaz AGonzález JDamas M(2019)Time-energy analysis of multilevel parallelism in heterogeneous clustersThe Journal of Supercomputing10.1007/s11227-019-02908-475:7(3397-3425)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1007/s11227-019-02908-4
Comito C(2019)Energy Efficiency in Big Data AnalysisEncyclopedia of Big Data Technologies10.1007/978-3-319-77525-8_141(715-722)Online publication date: 20-Feb-2019
https://doi.org/10.1007/978-3-319-77525-8_141
Garg RShukla N(2018)Energy Efficient Scheduling for Multiple Workflows in Cloud EnvironmentInternational Journal of Information Technology and Web Engineering10.4018/IJITWE.201807010213:3(14-34)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.4018/IJITWE.2018070102
Sheikh SPasha M(2018)Energy-Efficient Multicore Scheduling for Hard Real-Time SystemsACM Transactions on Embedded Computing Systems10.1145/329138717:6(1-26)Online publication date: 24-Dec-2018
https://dl.acm.org/doi/10.1145/3291387
Ren JWang XFang JFeng YZhu DLuo ZZheng JWang ZDimitropoulos XDainotti AVanbever LBenson T(2018)ProteusProceedings of the 14th International Conference on emerging Networking EXperiments and Technologies10.1145/3281411.3281422(379-392)Online publication date: 4-Dec-2018
https://dl.acm.org/doi/10.1145/3281411.3281422
Leong SParodi AKranzlmller D(2018)Reprint of A robust reliable energy-aware urgent computing resource allocation for flash-flood ensemble forecasting on HPC infrastructures for decision supportFuture Generation Computer Systems10.1016/j.future.2016.11.01579:P1(114-127)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1016/j.future.2016.11.015
Comito C(2018)Energy Efficiency in Big Data AnalysisEncyclopedia of Big Data Technologies10.1007/978-3-319-63962-8_141-1(1-8)Online publication date: 29-Jan-2018
https://doi.org/10.1007/978-3-319-63962-8_141-1
Escobar JOrtega JDíaz AGonzález JDamas M(2018)Energy‐aware load balancing of parallel evolutionary algorithms with heavy fitness functions in heterogeneous CPU‐GPU architecturesConcurrency and Computation: Practice and Experience10.1002/cpe.468831:6Online publication date: 25-May-2018
https://doi.org/10.1002/cpe.4688
Mahmood AKhan SAlbalooshi FAwwad N(2017)Energy-Aware Real-Time Task Scheduling in Multiprocessor Systems Using a Hybrid Genetic AlgorithmElectronics10.3390/electronics60200406:2(40)Online publication date: 19-May-2017
https://doi.org/10.3390/electronics6020040
Li YNiu JAtiquzzaman MLong X(2017)Energy-aware scheduling on heterogeneous multi-core systems with guaranteed probabilityJournal of Parallel and Distributed Computing10.1016/j.jpdc.2016.11.014103:C(64-76)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1016/j.jpdc.2016.11.014
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